Every published count of the AI connector ecosystem measures a different thing and none of them say which. We counted both official directories end to end, classified all 68,000+ declared tools, and then checked how much of the software a real business runs on is covered at all.
There are 7,000+ connectors across the two official AI connector directories as of September 2026. That is 4,000+ apps in the ChatGPT directory and 2,800+ connectors in the Claude directory. Between them they declare 68,000+ tools. Of those tools, 0.8% can transact, and 93.6% of the business software people actually pay for has no connector at all.
If you are deciding what to build next, those four numbers matter more than any roundup of the best connectors to install. A directory with 7,000+ entries sounds like a saturated market. It is not one. It is a very wide, very shallow layer sitting on top of a software economy it barely touches, and the shape of what is missing is legible if you count the whole thing instead of sampling it.
We publish this the way we publish every other corpus study on this site, whether that is who is already taking payments in a niche, where venture capital has landed, or what SaaS businesses sell for. Count the whole population, state the boundary, publish the holes.
This is a first-party census. We enumerated both official directories, parsed every declared tool name, and then cross-matched the vendors against 22,000+ business software listings from two independent review corpora. Every figure below was re-queried live on 19 September 2026. The method and the limits are stated in full, because the reason published counts of this ecosystem disagree by a factor of three is that almost none of them say what they counted.
Search for how many MCP servers exist and you will get 9,800, 14,000, 17,000 and 22,775, all in the same afternoon, none with a stated boundary. One registry operator is candid enough to write that its own listing count is not a good proxy for the ecosystem. A widely shared community thread reports that when packages were indexed across nine sources, roughly 30% of things with MCP in the name turned out not to be servers at all.
None of that is dishonest. It is just uncounted. A registry indexes what people submit, an npm scan indexes what people publish, and neither answers the question a builder is actually asking, which is how much of the world an agent can reach through the front door. That question has exactly two answers, because there are exactly two official directories, and nobody had counted both.
The gap is not an accident. Counting a directory end to end is dull, unglamorous work with no affiliate revenue attached, which is precisely the kind of research nobody does and therefore the kind worth doing. It is the same reason we built a complaint corpus instead of another idea generator, and the reason most SaaS research tools resell the same public summaries.
We counted every listing in the ChatGPT app and plugin directory and every listing in the Claude connectors directory. That is the whole boundary. We did not count npm packages, GitHub repositories, community registries, self-hosted servers, or anything a user has to paste a URL to reach.
This is why our number is smaller than the inflated ones, and it is the entire point. The official directories are where a non-technical user browses, which makes them the distribution surface that matters if you are weighing whether to build here. A server nobody can find is not a distribution channel. Distribution, not capability, decides whether a small software business gets found at all, which is the through line of who micro SaaS actually sells to and finding your first customers. The same logic drives how we size any market, which we walk through in how to calculate market size for a startup.
Every original-data study we publish carries this table, with a limitation on every row. If a methodology section does not tell you where its numbers break, it is marketing.
| What we measured | How | Limitation |
|---|---|---|
| Connector count | Full enumeration of both official directories | Point in time. Directories change daily and listings are removed without notice. |
| Tool count and verb class | Every declared tool name parsed into verb plus object noun, verb classified | Only 46.9% of connectors declare tools at all. The other 53.1% are unmeasured, not empty. |
| Transactional share | Verb class assignment over 68,000+ tool names | Classification is name-based. A tool called update_order may transact without saying so. |
| Platform overlap | Normalized vendor domain compared across both platforms | Domain matching misses vendors who use different domains per platform, so overlap is a floor. |
| Category distribution | 18 canonical categories mapped from two source taxonomies | The taxonomies differ. Several categories are single-platform artifacts. See the caveat below. |
| Business software coverage | 22,000+ Capterra and G2 vendor listings name-matched to connectors | Name matching is conservative and undercounts. 6.4% is a floor on coverage, not a precise rate. |
| Tool name vocabulary | 51,000+ normalized names, singleton flag per name | Normalization strips vendor prefixes, which slightly reduces the measured singleton rate. |
| Growth over time | Not measured | Our growth table is empty. This article makes no trend claim and none should be attributed to it. |
Two things we deliberately do not publish. We hold per-connector AI enrichment out because that layer is unpopulated, and we hold any growth or trajectory claim out because we have no time series. The discipline is the same one we apply to complaint data in the state of SaaS pain points, to acquisition data in the state of SaaS acquisitions, and to revenue data in our SaaS revenue benchmarks: publish the measurement, publish the hole, never fill the hole with a guess.
| Measure | ChatGPT directory | Claude directory | Combined |
|---|---|---|---|
| Connectors listed | 4,000+ | 2,800+ | 7,000+ |
| Declaring a tool list | 541 (12.7%) | 2,700+ (98.6%) | 3,300+ (46.9%) |
| Tools declared | 5,300+ | 62,000+ | 68,000+ |
| Mean tools per declaring connector | 30.8 | 70.1 | 20.6 |
| Transactional tools | 17 (0.3%) | 501 (0.8%) | 518 (0.8%) |
| Vendor tier published | None | All 2,800+ | 2,800+ |
| Category assigned | 100% | 2,600+ of 2,800+ | 6,900+ of 7,000+ |
| Distinct vendor domains | 3,400+ | 2,500+ | 5,100+ |
Read that table once more before moving on, because almost every interesting finding in this article is already visible in it. The two directories are not the same size, are not documented to the same standard, and do not describe their contents in the same vocabulary.
The phrase the MCP ecosystem does a lot of quiet work in most analysis. It implies one pool of connectors that any agent can draw on. The data says otherwise. These are two directories with different admission rules, different disclosure standards, and largely different populations.
That distinction has a direct cost. If you build for one and assume reach across both, your addressable audience is roughly a fifth of what you modelled. It is the same class of error we watch founders make when sizing a market from a category page rather than a population, covered in how founders research markets and how to research market size for SaaS.
Across 5,100+ distinct vendor domains, only 918, or 18.0%, appear on both platforms. 2,500+ are ChatGPT only. 1,600+ are Claude only. Four out of five vendors in this ecosystem have made a platform bet, whether they framed it as one or not.
This is the finding that invalidates most cross-platform commentary. When somebody tells you the connector ecosystem has consolidated around a set of usual suspects, they are looking at the 18% overlap and mistaking it for the whole. The other 82% is where the actual variety lives, and it is also where the coverage holes are, because a vendor present on one directory is invisible to agents on the other.
For a builder the read is straightforward. Single-platform presence is the norm, so covering both is a genuine differentiator rather than table stakes, and a product that reads across both directories is doing something 82% of the ecosystem does not. That is the kind of small structural edge we look for in single-feature micro SaaS ideas.
Two structural reasons, both visible in the data. First, the directories have different submission economics. Getting into the Claude directory runs through a submission portal that a builder in r/ClaudeAI describes plainly: “getting listed in Claude’s official Connectors Directory now requires access to a submission portal that’s only available on Team or Enterprise plans, individual Pro accounts can’t submit at all.” Second, the disclosure standards differ enough that a single build does not satisfy both.
Neither reason is about the protocol. Both are about distribution policy, which is the thing most builders discover after they have already built. It is the same lesson we drew comparing app marketplaces in Shopify app vs WordPress plugin: the billing and listing rails shape outcomes far more than the technology does.
It rhymes with what we found looking at Chrome extension economics: the platform decisions that look administrative at signup turn out to be the ones that set the ceiling on the business.
A connector listing is a name, a description, a vendor, sometimes a category, and sometimes a list of tools. The tool list is the only part that tells you what an agent can genuinely do, which is why the disclosure rate matters so much. 3,300+ of 7,000+ connectors, 46.9%, declare tools. For the remaining 53.1% we know a name and a promise.
2,600+ connectors publish an MCP endpoint. That is the subset an agent can reach directly rather than through a platform-mediated UI, and it is a useful second definition of the reachable ecosystem if the first one feels too generous.
To see this field by field rather than in aggregate, the connector browser exposes the raw listing for every row in this census, and the get_connector tool returns the same record to an AI assistant.
We parsed every declared tool name into a verb and an object noun, then classified the verb. This is the part of the census that nobody else has, and it is where the ecosystem’s actual shape shows up. Nothing else published on this ecosystem does it, which is why the transaction finding below has not appeared before. It is the same technique we use to turn unstructured review text into structured opportunity in turning G2 reviews into SaaS ideas.
| Verb class | Tools | Share | Connectors exposing it | What it means |
|---|---|---|---|---|
| Read | 36,000+ | 53.2% | 2,800+ | Fetch, list, search, get. Nothing changes. |
| Write | 18,000+ | 26.7% | 1,700+ | Create, update, delete a record inside the vendor. |
| Unknown | 11,000+ | 16.7% | 2,000+ | Name does not disclose the verb. Genuinely unclassifiable. |
| Admin | 1,300+ | 1.9% | 659 | Permissions, users, configuration. |
| Transactional | 518 | 0.8% | 264 | Money moves, an order is placed, something is irreversible. |
| Meta | 479 | 0.7% | 375 | Describes the connector itself. |
53.2% of all declared tools are reads, exposed by 2,800+ connectors. An agent standing in front of the entire official ecosystem can see an enormous amount. Fetching, listing, searching and summarizing are solved. This is real and it is useful, and it is also the ceiling for most connectors.
Read capability is genuinely valuable and worth building on. An agent that can pull from six systems and assemble an answer is doing work people currently do by hand, which is the premise behind automated business ideas and AI automation agency services.
26.7% of tools write, but they are concentrated: 1,700+ connectors expose a write, against 2,800+ exposing a read. A connector is meaningfully more likely to let an agent look than to let it act, and the write tools that do exist mostly create records inside the vendor’s own system rather than doing anything with external consequence.
The 1,700+ against 2,800+ ratio is worth holding onto. Roughly a third of connectors that let an agent look will not let it touch anything, and that third is disproportionately the vendors whose systems matter most. The same reluctance shows up from the customer side throughout why SaaS customers churn.
518 tools out of 68,000+. Exposed by 264 connectors out of 7,000+, which is 3.7% of the directory. Split by platform, the ChatGPT directory declares 17 transactional tools in total and the Claude directory declares 501.
That is the single most important number in this census. The entire official AI connector ecosystem, both platforms, every category, contains roughly five hundred tools that can move money or commit an irreversible external action.
For scale, the companies we track taking payments live on Stripe number in the tens of thousands. The payment rail is enormous. The agent-reachable slice of it is five hundred tools.
Agents can read your business. They can, in a narrower set of cases, write into it. They almost never transact on its behalf. Every confident claim that agents are about to run businesses end to end has to get past that 0.8%, and none of them address it because none of them have counted it.
It is also why we do not think the opportunity here is another AI wrapper. A wrapper inherits the ceiling of the tools underneath it, and that ceiling is now measured. The alternative framing starts from a documented problem rather than a model capability, which runs through SaaS ideas backed by pain points and business ideas that solve real problems.
A vertical SaaS operator on Hacker News, arguing against the idea that agents are about to eat his category, landed on the sentence that explains why a census beats a capability demo: “The bottleneck is still knowing what to build, not building.” A founder of a niche inventory product in the same thread said it from the other direction: “most of the difficulty and complexity is not in the code. Coding is the last part, and the easiest one.” and, on what he actually competes with, “The spreadsheet is my biggest competitor.”
This is the substrate under our second study, which scores 54 business verticals for autopilot readiness and finds that most of them cannot run on autopilot for exactly this reason.
The census figure has an unusually good independent check, because builders keep rediscovering it one integration at a time. A developer who built a network that only AI agents could post to catalogued the failures and led with this one: “PLAIN CHATGPT CANNOT POST AT ALL, and the documented fix doesn’t work either. Its browsing tool is GET-only. It can read every page and physically cannot write, however the instruction is worded.”
Another, auditing social scheduling connectors by hand against each vendor’s own docs, found the same asymmetry inside a single category: “Agorapulse and Planable create drafts only. Media upload is missing on several. Only eleven expose comments or an inbox through the agent.” That is a hand count of one category landing on the same shape our machine count finds across all 18 of them. A hand audit and a machine census converging is the strongest evidence either can offer, and it is the same convergence test we apply in validating an idea before writing code.
And a third, running an agent against real systems, described why the write path stays closed even when it is technically available: “the external system completes the action, the response times out, and the agent retries. Now you have two tickets, two emails, or two purchases.” Their conclusion was to stop: “I’m not adding mutate tools until the read loop is tighter.”
The same developer described the deeper problem with autonomous writes: “Work doesn’t always fail loudly. Sometimes it just disappears between states.” and “the difficult question wasn’t can the agent do the task, but what exactly was completed, what was verified, and where can we safely resume without doing the work twice.” Another, trying to get an agent to complete a real booking end to end, reported the outcome bluntly: “The immediate suggestions from Claude was to use the integrated connectors. I tried those but they were really poor. I still had to book myself, and they didn’t give full results.”
A fourth ran an open network that only agents could use and measured retention rather than capability: “about 35 external keys ever showed up. of those, 3 came back on any later day.” Reading is solved, acting is not, and returning is barely attempted. Guidance on working inside that reality is in AI agents beyond coding.
Vendors are not failing to ship transactional tools. They are declining to. Exposing a read costs a vendor almost nothing. Exposing a transaction means accepting that an autonomous process can create liability inside their system, and the failure mode above is exactly why that decision keeps going the other way.
Which means the 0.8% is durable. It is not a gap that closes because the protocol matures. If you are hunting for a structural opening rather than a temporary one, this is the distinction we use throughout finding SaaS ideas from real user pain points.
A small business owner asking the question from the other side of the table makes the incentive obvious: “Would you trust AI with access to your business finances? Not just using AI to analyze financial data, but actually giving it some level of access.” Vendors read that hesitation correctly, and it is documented across 1M+ complaints in our corpus and summarised in the complaint analysis platform.
The two directories do not hold themselves to the same standard about saying what their listings can do, and the gap is not subtle.
541 of 4,000+ ChatGPT apps (12.7%) publish a machine-readable tool list. 2,700+ of 2,800+ Claude connectors (98.6%) do. Roughly 87% of the larger directory tells you nothing about what its apps can actually do beyond prose. If you judge a marketplace by listing quality, this is a catalogue with no spec sheets, a problem we have also measured in software review coverage.
The ChatGPT directory is, to its credit, complete on the fields it does publish: description, vendor and category are present on 100% of rows. There are no holes in the catalogue. There is simply no capability layer in it.
The two directories were built for different readers. OpenAI’s help documentation notes that the app directory was migrated into the plugin directory in July 2026, and the surface is organised around a consumer browsing for something to switch on. The Claude connectors directory is organised around a connector as an MCP server, and an MCP server publishes its tool list as a matter of protocol. The practical setup differences follow from that, which using MCP with Claude and the troubleshooting guide both cover.
One is a storefront. The other is an interface registry. Counting them together without saying so is how you end up with an ecosystem number that means nothing.
If you build for the ChatGPT directory you are entering a larger catalogue where nobody can see what your app does until they install it, which pushes the competition toward name, description and placement. If you build for the Claude directory you are entering a smaller catalogue where your tool surface is public and directly comparable to everyone else’s.
Those are genuinely different games. The first rewards positioning, the second rewards depth. Neither is better in the abstract and the honest answer depends on what you are building. If that is not settled yet, start with what to build as a solo developer rather than with a platform. One small business owner summarised the whole category problem in a sentence: “Every tool I tried felt like it was built for tech companies, not for people actually doing the work.” That sentence describes a 28.7% productivity share better than our chart does. It is the framing we use in how to find a profitable niche.
Among connectors that declare anything, the Claude side averages 70.1 tools and the ChatGPT side 30.8. Part of that is genuine depth and part is that an MCP server enumerates everything it can do while a plugin listing summarizes. Treat it as a disclosure difference first and a capability difference second. Surface area is not depth of product, a distinction we keep returning to in what micro SaaS actually charges.
| Tier | Connectors | Share | Declaring tools | Authless |
|---|---|---|---|---|
| Community | 1,900+ | 70.8% | 1,900+ | 236 |
| Partner | 813 | 28.9% | 775 | 86 |
| Anthropic | 9 | 0.3% | 9 | 8 |
Seven in ten listings are community tier. If you have been treating directory presence as a proxy for vetting, it is not one. The tier is published, which is more than the other directory does, and it is worth reading before you assume a listing implies review. Marketplace review signals are easy to over-trust, which is the point of reading G2 reviews properly and the G2 insight tools.
Anthropic itself ships 9 connectors, and 8 of those 9 are authless. The platform owner has built almost nothing and has built it to work without setup. That is a deliberate posture and a useful one to read: the platform is not trying to own the connector layer, it is trying to make the connector layer exist.
Platform owners that stay out of the application layer leave more room for independents than ones that colonise it, which is worth weighing before you commit. We compared that dynamic across seven ecosystems in the plugin ecosystem study.
The Claude directory’s submission route sits behind a Team or Enterprise plan. From the same r/ClaudeAI thread: “indie devs or small projects with a genuinely useful MCP server have to pay a monthly subscription fee just to be discoverable inside Claude.” The server still works as a custom connector on any paid plan. It just does not appear where people browse. That distinction, between working and being discoverable, decides outcomes across micro SaaS competition generally.
It means our Claude count is a count of who could afford to be listed, not a count of who built something. That cuts against the naive read of the community tier share: 70.8% community does not mean 70.8% hobbyist, because the hobbyists who could not clear the submission bar are not in the denominator at all.
We flag this rather than smooth it because it is the kind of selection effect that quietly breaks conclusions. The same discipline applies to any directory-derived dataset, including the Stripe Index, where saturation and presence are also not the same measurement. The Stripe Index landing page and its MCP tools carry that caveat on every category count they return.
330 of 7,000+ connectors, 4.7%, are authless. These are the ones an agent can use on a cold start with no OAuth dance and no credentials, which makes them the practical floor of agent capability. Almost everything genuinely useful to a business sits behind auth, and auth is where setup friction lives. Setup friction is where most agent workflows quietly die, and it is worth budgeting for deliberately. Teams that skip it end up where this operator did: “We get a lot of patient inquiries through WhatsApp and handling them manually is overwhelming. Need shared inbox plus workflow automation without integrating multiple tools.” The ask is explicitly for fewer integrations, not more. See agent cost control and verifying agent work both set out.
| Category | Connectors | Share | ChatGPT | Claude |
|---|---|---|---|---|
| Productivity | 2,000+ | 28.7% | 989 | 1,000+ |
| Business operations | 791 | 11.2% | 791 | 0 |
| Data and analytics | 627 | 8.9% | 197 | 430 |
| Developer tools | 625 | 8.8% | 406 | 219 |
| Financial services | 588 | 8.3% | 361 | 227 |
| Other | 464 | 6.6% | 421 | 43 |
| Travel | 384 | 5.4% | 325 | 59 |
| Education | 289 | 4.1% | 251 | 38 |
| Sales and marketing | 243 | 3.4% | 0 | 243 |
| Creative | 231 | 3.3% | 195 | 36 |
| Media and entertainment | 184 | 2.6% | 147 | 37 |
| Healthcare | 140 | 2.0% | 77 | 63 |
| Commerce | 106 | 1.5% | 0 | 106 |
| Communication | 94 | 1.3% | 38 | 56 |
| Legal | 60 | 0.8% | 0 | 60 |
| Security | 35 | 0.5% | 35 | 0 |
| Science and research | 20 | 0.3% | 19 | 1 |
| Nonprofit | 9 | 0.1% | 0 | 9 |
28.7% of the entire corpus is productivity. The top three categories account for 48.8%. Documents, notes, tasks and calendars have been connected many times over, which is exactly what you would expect from an ecosystem whose first users were knowledge workers connecting their own tools.
Concentration like this is the most reliable signal in any marketplace dataset. Where a third of supply piles into one category, the far end of the distribution is where unserved demand sits, which is how we read saturation in boring business ideas and B2B SaaS ideas.
140 healthcare connectors and 60 legal connectors. 200 rows out of 7,000+. Nonprofit is 9. Those are not categories that lack software, they are categories whose software has not shown up here, and the reasons are structural: procurement cycles, regulatory review, and vendors with no commercial incentive to expose their data to a third-party agent.
This is the thinnest coverage in the census and it lines up precisely with where our vertical readiness scoring says the blocked markets are. It also matches the complaint record: documented systemic pain in legal and dental software is about accounting reports, cross-platform data entry and billing accuracy, all reachable through the Capterra analysis guide. We work through that overlap in detail in vertical AI SaaS ideas and in the boring industries analysis.
The complaint record for those same categories is not thin at all, which is the whole point. In legal software: “Not being able to integrate properly with other software our firm uses was a deal breaker.” and “Syncing with QuickBooks Online would be very helpful since we often deal with multiple client invoices and payments.” In dental: “Accounting reports are lengthy and confusing. I often need to run multiple reports to reconcile balances, which delays processing.” and “The integration issues left us unable to take calls for three weeks.”
Our Capterra scoring flags Cumbersome Data Entry Processes Across Platforms in legal case management at a 8.50 market gap and 4.30 severity, and Inability to Generate Accurate Accounting Reports in dental at 8.00 gap and 4.50 severity across 23 companies. Two of the thinnest connector categories in the census are two of the best-documented complaint categories in our corpus. That is not a coincidence, it is the shape of an opening.
Look again at the category table and you will notice something odd. Business operations is 791 and all of them are ChatGPT. Sales and marketing is 243 and all of them are Claude. So are commerce, legal and nonprofit. Security is entirely ChatGPT.
That is not a finding about those markets. It is an artifact of mapping two different source taxonomies into one canonical set. Cross-platform category comparison is not reliable in this dataset and we do not make one. The within-corpus shares are sound, the platform split inside a category often is not. We would rather publish the caveat than a clean-looking chart that misleads.
The one honest addition: Claude category coverage is 2,600+ of 2,800+, leaving 148 connectors uncategorised. We could have hidden that by excluding them. We did not. Coverage honesty is a standing rule across everything we publish, including trending SaaS ideas and SaaS product ideas.
Normalizing every declared tool name produces 51,000+ distinct names across 33,000+ distinct object nouns. For an ecosystem of 68,000+ tools, that is nearly one unique name per tool. That ratio alone shows how young this layer is next to the software market it is trying to reach, which we sized against real vendors in market sizing.
46,000+ of 51,000+ normalized tool names are singletons, appearing on one connector and nowhere else. Fewer than one name in ten is shared by two or more connectors.
It means there is no shared vocabulary. Every vendor invents its own word for send, for list, for create, for the object being acted on. An agent cannot transfer what it learned from one connector to the next, because the next one calls the same action something else.
The practical consequence is that any product promising to work across many connectors is quietly taking on a mapping problem, and mapping problems grow with the corpus. We flag this the way we flag hidden operational cost in how to build a SaaS.
The lived version of that appears constantly in automation communities: “We built a few monday dev automations, every minor change breaks the chain and I’m back to manual fixes.” Integration surfaces drift, and a product whose value is a mapping inherits every drift. Our G2 corpus records the same thing as a category-level finding: “Users mainly experience synchronization lags, complex UI navigation, and inadequate customer service, impacting productivity and operational efficiency.”
The optimistic read is that a standard vocabulary layer is an obvious missing piece. The realistic read is the one our own kill pass produced: naming conventions are the kind of thing a platform absorbs, so a product whose only asset is a mapping table has a short life. That is the distinction between a gap and a defensible gap, and we apply it in the 8-stage validation framework.
Everything above describes the ecosystem on its own terms. The question a builder actually has is different: of the software that businesses genuinely run on and pay for, how much of it can an agent reach?
To answer that we needed an outside reference set, so we used two: every business software vendor listing in our Capterra corpus and every one in our G2 corpus. 22,000+ vendor listings in total, assembled independently of anything to do with AI. Both are queryable inside BigIdeasDB through the Capterra tools and the G2 insight tools, so the reference set is not a black box.
1,400+ of 22,000+ business software vendor listings match a connector on either directory. That is 6.4%. Put the other way round, about 93.6% of the software on the two biggest business software review sites cannot be reached by an agent through an official directory at all.
The reason we trust the number is that it reproduces. Capterra: 737 of 13,000+ matched, 5.5%. G2: 720 of 9,400+ matched, 7.6%. Two corpora assembled by different companies with different inclusion rules, landing two points apart.
A single number is an assertion. Two independent sources agreeing is evidence. This is the triangulation habit that runs through all of our research, and it is why we cross-reference complaints, revenue and funding rather than leaning on any one. It is why cross-source research is the workflow we recommend above any single tool, and why the pattern runs through the state of SaaS pain points.
| Business function | Vendor listings | With a connector | Covered |
|---|---|---|---|
| Support | 332 | 11 | 3.3% |
| Compliance | 431 | 20 | 4.6% |
| Inventory | 132 | 8 | 6.1% |
| Payments | 97 | 6 | 6.2% |
| Ecommerce | 139 | 9 | 6.5% |
| Payroll and HR | 547 | 38 | 6.9% |
| Scheduling | 580 | 41 | 7.1% |
| Communications | 927 | 75 | 8.1% |
| Accounting | 809 | 69 | 8.5% |
| Documents | 814 | 75 | 9.2% |
| Marketing | 1,100+ | 141 | 12.2% |
| Analytics | 243 | 31 | 12.8% |
| CRM | 294 | 53 | 18.0% |
CRM at 18.0% is more than five times better covered than support at 3.3%. Analytics and marketing sit near the top. Inventory, payments, compliance and support sit at the bottom. For the demand-side mirror of this table, the pain points and opportunities tools return documented complaint volume for the same functions.
Rank those functions by who buys AI tooling and you get almost exactly the same order. CRM, marketing and analytics are where the AI budget lives and where the buyer is already enthusiastic. Inventory, compliance, payments and support are where the work happens.
That is the census’s closing argument. The connector layer has grown fastest toward the audience most willing to adopt it, which leaves the operational core of most businesses untouched. A small business owner writing in r/smallbusiness put the consequence better than a chart can: “A lot of his work involves moving information between different websites that have no integration with each other.”
Another described what that costs in a month: “What starts as a quick check turns into a 15-hour-a-month slog of copy-pasting numbers, hunting down missing PDFs, and playing detective to spot duplicate payments.” The complaint corpus says the same from inside the software: “Bank integration eats CONTROLLERS for breakfast. Integration promised automation but delivered manual uploads, broken file formats, cryptic bank error messages.” and “to update a PO, I’ve to manually copy the tracking, go to the carrier portal, find the status, then type it back into netsuite. all day, every day.” Both are the shape described in finding problems worth solving. A third writer, cataloguing the shapes of back-office work across industries, named the one our data keeps pointing at: “Extract, scheduled or on-demand pulls from a system that doesn’t have an API. Every ‘log into the portal and download the report’ workflow.”
Because the coverage table is abstract, here is what its weakest rows sound like in the words of the people paying for the software. Every quote below is from our own corpus, anonymized to platform and software category.
| Function | Coverage | What the customer says |
|---|---|---|
| Support | 3.3% | “Customer service tools have disconnected systems, so AI can’t bridge the gaps to provide true understanding and context.” – r/CustomerService |
| Compliance | 4.6% | “Users are concerned about cybersecurity, the steep learning curve for customization, limited data export capabilities.” – G2 insight, governance and compliance |
| Inventory | 6.1% | “When doing returns, it often fails to register multiple returned items, causing lots of headaches during inventory reconciliation.” – Capterra review, auto dealer |
| Payments | 6.2% | “Biggest pain point in B2B fintech is integration with legacy systems, nothing ever just fits.” – r/fintech |
| Scheduling | 7.1% | “If a guest books online, we can’t rely on it showing up unless we do it manually.” – Capterra review, property management |
| Accounting | 8.5% | “The accounting tab does not allow for a credit balance, forcing us to maintain an external spreadsheet for client transactions.” – Capterra review, law practice management |
| Accounting | 8.5% | “Our Accounting software is still not integrated due no respond from the Account Manager.” – Capterra review, hospitality property management |
| Documents | 9.2% | “The reports we were looking for aren’t there, and I’m forced to compile everything manually.” – Capterra review, law practice management |
| CRM | 18.0% | “They lied about the integration with my existing customer database. I couldn’t even access my 7,000 loyal customers after switching.” – Capterra review, auto body |
| Cross-system | n/a | “Modules need more seamless integration to eliminate repeated data entry.” – Capterra review, insurance |
| Cross-system | n/a | “We are building a tool where pipeline lives in more than one place. How do sales ops teams handle this problem today? Manual reconciliation?” – r/SalesOps |
| Cross-system | n/a | “you open 12 tabs, export CSVs, and wrestle VLOOKUPs.” – r/remotejobs |
Not one of those complaints is about AI. They are all about the same thing: data that lives in two systems and a human in the middle doing the joining. The census says that human is not being replaced any time soon in the functions where the complaints are loudest, and that is where the buildable work is. We keep a running read on this in how to find problems worth solving.
Query this census yourself. Agent Index is a Pro feature inside BigIdeasDB, with seven MCP tools that let Claude or ChatGPT search the connector corpus, pull per-category coverage, and return the autopilot verdict for any of 54 verticals. It sits alongside 1M+ documented complaints, the Stripe Index and revenue intelligence in one workspace.
Not because anyone is wrong, but because four different questions are being answered with one number. How many servers have ever been published. How many are listed somewhere. How many are listed officially. How many a user can reach without pasting a URL. Those produce wildly different totals and only the last two are useful to a builder. Picking the right denominator is most of research, and getting it wrong is how an ordinary market comes to look either saturated or empty. We apply the same discipline to revenue in the state of indie SaaS revenue.
| Source type | Typical figure | What it counts | Why it differs from ours |
|---|---|---|---|
| Community registry | 5,500 to 9,800 | Submitted listings | Self-submission, includes unlisted and self-hosted servers. |
| Package scan | 14,000 to 22,775 | Packages named like servers | A large share are not servers. Same server counted across sources. |
| Vendor roundup | 9 to 80 | Hand-picked favourites | Editorial selection, not a census. Not comparable to any count. |
| Community catalogue | ~2,500+ | Claude directory only | Closest to our Claude figure. Does not cover the ChatGPT directory. |
| This census | 7,000+ | Both official directories, first-party listings only | Excludes npm, GitHub, registries and self-hosted entirely. |
The community catalogue row is the interesting one. An independently maintained Claude catalogue reports roughly 2,500+ integrations against our 2,800+, which is close enough to be a genuine cross-check on our Claude half. Nobody has done the same for the ChatGPT half, which is why the combined number has not existed until now. Independent replication of half a result is not nothing, and we would rather point at it than pretend we are unchecked. The same instinct governs how we handle complaint analysis.
The snapshot will not reproduce exactly, because directories change. The method will. Enumerate both official directories. Keep only first-party listings. Normalize the vendor domain. Parse each declared tool name into a verb and an object noun and classify the verb into read, write, transactional, admin, meta or unknown. Count disclosure separately from capability, because an undeclared tool list is not an empty one. Keeping disclosure and capability in separate columns is the most important choice in the whole method, and it is the same separation that makes the pain points database usable rather than merely large.
Then get an outside reference set. That last step is the one most analyses skip, and it is the only one that turns a directory count into a coverage measurement. If you want to run this kind of cross-corpus comparison yourself, the cross-source research guide covers the pattern, and the MCP setup guide gets the tools into your assistant.
Three specific things, stated in advance so they are falsifiable. If the ChatGPT directory began publishing tool lists at anything close to Claude’s rate, the capability picture would change within one sync. If transactional tools moved from under 1% into double digits, the read-only ceiling would break and the autopilot argument would need rewriting. If vendor overlap climbed well above 18%, the two directories would become one ecosystem in fact rather than in rhetoric.
None of those look imminent, but all three are measurable, and we will say so if they move. Stating falsification conditions up front is what separates research from positioning, a standard we hold idea validation tooling to as well.
Four things follow directly from the numbers.
Before those four, one thing worth naming. The most common message in the founder subreddits is not about connectors at all, it is the prior question: “Every time I try to brainstorm an idea I hit the wall. Either someone has already built a tool for it or it does not exist because no one really cares enough to pay for a solution.” and “as a developer living in a tech bubble how do you actually find what normal people or businesses are struggling with?” A census is one answer. It replaces a guess about crowding with a measurement of it.
One, do not read directory size as saturation. 7,000+ listings against a 6.4% coverage floor and a 90.5% singleton rate is a thin layer, not a crowded one. We make the same argument about existing competitors in the state of micro SaaS competition.
Two, do not build the connector. A connector is an interface any vendor can add in a sprint, and the protocol layer is commoditizing fast. The r/SaaS objection to prompt-as-moat applies exactly: “What happens when OpenAI or Anthropic releases their next minor model update, context window expansion, or native workflow feature?” That question kills more AI product plans than any competitor does, and we treat it as a first-round filter in AI product validation for solo founders and AI SaaS ideas.
A builder in the same subreddit wrote the brief better than we could: “I don’t want to spend another 3-6 months building something that people think is cool but nobody actually pays for” and “I’m looking for real problems, not build another AI wrapper suggestions.” The coverage table above is a list of real problems with the crowding already measured.
Three, build where the reads already work and the writes never will. The 53.2% read share is a capability you can rely on. The 0.8% transactional share is a constraint that is not lifting. Products that assemble evidence across systems work today; products that assume an agent will complete a transaction do not. See simple SaaS ideas for solo developers for what that looks like at small scale.
Four, go where the coverage table is worst. Support at 3.3%, compliance at 4.6%, inventory at 6.1%. Those are not empty markets. They are markets full of paying customers whose software an agent cannot reach, which is a different and much better situation than an empty market. The companion study, autopilot readiness across 54 verticals, takes that list down to the specific businesses and the specific missing piece, and the ChatGPT directory market map covers whether that directory is a distribution channel worth building for at all.
If you want to work this corpus directly rather than read about it, Agent Index is where it lives, the connector browser is the fastest way in, and the Agent Index MCP guide documents the seven tools that let your assistant query it. For everything else we hold on demand rather than supply, start with the pain points database or Discover.
Across the two official directories we count 7,000+ as of September 2026: 4,000+ ChatGPT apps and 2,800+ Claude connectors. That is deliberately smaller than the 9,800 to 22,775 figures quoted elsewhere, because those include community registries and npm packages. We count only first-party directory listings.
Because almost nobody states what they counted. Registry aggregators index submissions, package scans index names, and the same server gets counted repeatedly across sources. One registry operator notes publicly that its own count is not a good proxy for the ecosystem. Two censuses are only comparable when both publish a boundary.
Very few. Of 68,000+ declared tools, 53.2% are reads and only 0.8% are transactional. Writes are 26.7%. Only 264 connectors of 7,000+ expose even one transactional tool, which is 3.7% of the directory.
Mostly not. Across 5,100+ vendor domains, 18.0% appear on both platforms. 2,500+ are ChatGPT only and 1,600+ are Claude only. Four in five vendors have effectively made a platform bet.
About 6.4%, and that is a floor. We matched 22,000+ vendor listings from two independent review corpora and found 1,400+ matches: Capterra 5.5%, G2 7.6%. Because the matching is name-based and conservative, real coverage is somewhat higher, but nowhere near complete.
Claude, by a wide margin. 98.6% of Claude connectors publish a machine-readable tool list against 12.7% of ChatGPT apps. Roughly 87% of the ChatGPT directory tells you nothing about capability beyond its written description.
No. 70.8% is community tier, 28.9% partner, and 9 connectors carry the Anthropic tier. Directory presence does not imply vetting, and the tier is published so you can check.
Productivity, at 28.7% of all connectors, with the top three categories accounting for 48.8%. Healthcare and legal together are 2.8%, which is the concentration finding that matters if you are looking for uncontested ground.
That there is no shared vocabulary. 46,000+ of 51,000+ normalized tool names appear on exactly one connector. Agents cannot generalize across tools when nearly every tool invents its own name for the same action.
CRM at 18.0%, then analytics at 12.8% and marketing at 12.2%. The worst covered are support at 3.3%, compliance at 4.6%, inventory at 6.1% and payments at 6.2%.
The connector itself is rarely the business. Our kill pass on 432 generated ideas left 25 survivors and almost all of them are cross-system reconciliation or close layers rather than connectors. A connector is an interface any vendor can add in a sprint. The durable asset is the data model and the exception history above it.
We cannot say from this dataset and we will not guess. Our growth table is empty, so this is a point-in-time snapshot for September 2026 with no trend claim attached.
330 of 7,000+, which is 4.7%. Notably 8 of the 9 Anthropic-tier connectors are authless. These are the connectors an agent can reach on a cold start with no credentials.
The boundary is reproducible even if the exact snapshot is not. Enumerate both official directories, keep first-party listings only, normalize vendor domains, parse each tool name into verb and object noun, and classify the verb. Counts will move. The ratios have been stable across our syncs.
ChatGPT publishing tool lists at Claude’s rate, transactional tools moving into double digits, or vendor overlap climbing well above 18%. Any one of those would require a rewrite, and all three are measurable.
Agent Index lives inside BigIdeasDB and ships seven MCP tools that let an AI assistant query the census directly. The Agent Index MCP guide documents search_connectors, get_connector, get_agent_coverage, search_agent_ideas and get_vertical_readiness with example prompts, and the full tool reference covers everything else in the same assistant.
No, and this is the most expensive misread in the space. 7,000+ connectors sounds crowded until you notice 90.5% of tool names are unique, only 0.8% of tools transact, and 93.6% of the software businesses actually pay for has no connector at all. Breadth of listings is not depth of coverage.
BigIdeasDB Research. (2026). AI Connector Census 2026: What 7,000+ Connectors Actually Do. BigIdeasDB. Retrieved from https://bigideasdb.com/ai-connector-census-2026