compare_cohorts
MCP vs AI Agents: building for agents beats talking about them
Providers that ship MCP outscore providers that market AI agents by 11 points, and by 14 points on agent readiness.
MCP
AI Agents
Each sun is drawn from the cohort's averages: six facet rings, the mean Kin Score and mean agent readiness.
This is the largest gap on the page. The 1,460 providers carrying the MCP tag average a Kin Score of 42.6 and an agent readiness of 33.9. The 1,034 tagged AI agents average 31.5 and 19.6. The MCP cohort has 131 exemplars against 46.
The facets show the difference between building for agents and talking about them. MCP providers lead on access clarity (48.7 against 38.1), operational transparency (30.1 against 20.4) and contract quality (30.6 against 21.4). 79% of them publish a first-party MCP server. That is close to the definition of the tag, but it also means 21% are tagged MCP for something other than shipping one. Nearly half (47%) publish OAuth scopes, more than any other cohort here.
depth_confounded is true and the margin is real: we hold seven more artifact directories on the median MCP provider. Some of that is enrichment, and some is that MCP providers simply publish more for us to find.
The distributions
| MCP | AI Agents | Delta | |
|---|---|---|---|
| Kin Score, mean | 42.6 | 31.5 | +11.1 |
| Kin Score, median | 42.8 | 29.3 | +13.5 |
| Kin Score, highest | 94 | 87.8 | |
| Kin Score, std dev | 17.3 | 19.5 | |
| Agent readiness, mean | 33.9 | 19.6 | +14.3 |
| Agent readiness, highest | 91.8 | 73.8 |
Delta is MCP minus AI Agents, as the tool returns it.
Where the providers sit
MCP
AI Agents
Facet averages
| Facet | MCP | AI Agents | Delta |
|---|---|---|---|
| Discoverability | 71.7 | 63.6 | +8.1 |
| Contract Quality | 30.6 | 21.4 | +9.2 |
| Contract Governance | 15 | 10 | +5 |
| Operational Transparency | 30.1 | 20.4 | +9.7 |
| Developer Ergonomics | 49.7 | 36 | +13.7 |
| Access Clarity | 48.7 | 38.1 | +10.6 |
What they publish
Share of each cohort's providers publishing each artifact. mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.
| Artifact | MCP | AI Agents | Delta |
|---|---|---|---|
| First-party MCP server | 79% | 45% | +34 |
| Any MCP artifact (incl. derived) | 92% | 45% | +47 |
| OAuth scopes | 47% | 17% | +30 |
| Spectral rules | 13% | 12% | +1 |
| Arazzo workflows | 4% | 4% | 0 |
| Agent skills | 0% | 0% | 0 |
| Security artifact | 99% | 99% | 0 |
How well we know each cohort
The coverage behind the averages. A cohort with a higher catalog gap and fewer artifact directories has been enriched less, so part of any difference is our coverage rather than the market.
| MCP | AI Agents | |
|---|---|---|
| Declared members | 1463 | 1049 |
| Scored (in the averages) | 1460 (100%) | 1034 (99%) |
| Thinly covered | 1136 | 890 |
| Median artifact directories | 21 | 14 |
| Mean catalog gap (points we could add) | 67.7 | 74.7 |
The full compare_cohorts response
{"a":{"kind":"tag","slug":"mcp","name":"MCP","members":1463,"stats":{"kind":"tag","slug":"mcp","name":"MCP","tier":"report","coverage":{"declared_members":1463,"resolved_members":1463,"scored_members":1460,"agent_scored_members":1460,"scored_share_pct":100,"unexamined_members":3,"enrichment_depth":{"mean_catalog_gap":67.7,"median_artifact_dirs":21,"n":1460,"thinly_covered":1136,"thinly_covered_of":1460,"thin_coverage_note":"1136 of 1460 scored members had under half their catalog-attributable points satisfied at all (catalog_earned / catalog_max < 0.5). They are INCLUDED in the mean above. A large share here means the cohort mean is partly a measurement of how far our enrichment reached, not only of how these providers publish.","note":"Rubric points API Evangelist could add to the average member with no action by the providers, and the median count of artifact directories we hold on them. A cohort with a higher gap and thinner artifacts has been enriched less, so part of any difference between its mean and another cohort's is our coverage rather than the market. Compare two cohorts only when this figure is comparable. It is an upper bound on our backlog: it says who CAN close a gap, not who did."},"unexamined_note":"Excluded from the distribution: these carry a catalog-owned needs_work flag (never enriched, or no resolvable host). They are listed as members and keep their own page and score; they do not vote in the averages, because their score measures our coverage rather than their posture."},"sufficient":true,"kin_score":{"mean":42.6,"median":42.8,"min":3.7,"max":94,"stdev":17.3},"agent_readiness":{"mean":33.9,"median":33.7,"min":0,"max":91.8,"stdev":17.9},"band_distribution":{"exemplar":131,"strong":269,"developing":425,"thin":324,"emerging":285,"minimal":26},"facet_averages":{"discoverability":{"mean":71.7,"n":1460},"contract_quality":{"mean":30.6,"n":1460},"contract_governance":{"mean":15,"n":1460},"operational_transparency":{"mean":30.1,"n":1460},"developer_ergonomics":{"mean":49.7,"n":1460},"access_clarity":{"mean":48.7,"n":1460}},"adoption":{"mcp_pct":92,"mcp_first_party_pct":79,"mcp_note":"mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.","skills_pct":0,"scopes_pct":47,"security_pct":99,"arazzo_pct":4,"rules_pct":13}}},"b":{"kind":"tag","slug":"ai-agents","name":"AI Agents","members":1049,"stats":{"kind":"tag","slug":"ai-agents","name":"AI Agents","tier":"report","coverage":{"declared_members":1049,"resolved_members":1049,"scored_members":1034,"agent_scored_members":1034,"scored_share_pct":99,"unexamined_members":15,"enrichment_depth":{"mean_catalog_gap":74.7,"median_artifact_dirs":14,"n":1034,"thinly_covered":890,"thinly_covered_of":1034,"thin_coverage_note":"890 of 1034 scored members had under half their catalog-attributable points satisfied at all (catalog_earned / catalog_max < 0.5). They are INCLUDED in the mean above. A large share here means the cohort mean is partly a measurement of how far our enrichment reached, not only of how these providers publish.","note":"Rubric points API Evangelist could add to the average member with no action by the providers, and the median count of artifact directories we hold on them. A cohort with a higher gap and thinner artifacts has been enriched less, so part of any difference between its mean and another cohort's is our coverage rather than the market. Compare two cohorts only when this figure is comparable. It is an upper bound on our backlog: it says who CAN close a gap, not who did."},"unexamined_note":"Excluded from the distribution: these carry a catalog-owned needs_work flag (never enriched, or no resolvable host). They are listed as members and keep their own page and score; they do not vote in the averages, because their score measures our coverage rather than their posture."},"sufficient":true,"kin_score":{"mean":31.5,"median":29.3,"min":1.7,"max":87.8,"stdev":19.5},"agent_readiness":{"mean":19.6,"median":15,"min":0,"max":73.8,"stdev":19.6},"band_distribution":{"exemplar":46,"strong":113,"developing":211,"thin":196,"emerging":282,"minimal":186},"facet_averages":{"discoverability":{"mean":63.6,"n":1034},"contract_quality":{"mean":21.4,"n":1034},"contract_governance":{"mean":10,"n":1034},"operational_transparency":{"mean":20.4,"n":1034},"developer_ergonomics":{"mean":36,"n":1034},"access_clarity":{"mean":38.1,"n":1034}},"adoption":{"mcp_pct":45,"mcp_first_party_pct":45,"mcp_note":"mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.","skills_pct":0,"scopes_pct":17,"security_pct":99,"arazzo_pct":4,"rules_pct":12}}},"comparable":true,"delta":{"kin_score_mean":11.1,"kin_score_median":13.5,"agent_readiness_mean":14.3,"facets":{"discoverability":8.1,"contract_quality":9.2,"contract_governance":5,"operational_transparency":9.7,"developer_ergonomics":13.7,"access_clarity":10.6},"adoption":{"mcp_pct":47,"mcp_first_party_pct":34,"mcp_note":null,"skills_pct":0,"scopes_pct":30,"security_pct":0,"arazzo_pct":0,"rules_pct":1},"enrichment_depth":{"mean_catalog_gap":-7,"median_artifact_dirs":7}},"depth_confounded":true,"depth_note":"MCP scores 11.1 points higher than AI Agents AND is the better-enriched cohort: 7 points less catalog_gap and 7 more artifact directories on the median member. The market difference and our coverage difference point the same way, so this delta cannot be attributed to the markets on its own. Close the enrichment gap, or report both figures together."}
Result read 2026-10-27. MCP and AI Agents each have their own page with the full roster and leaderboard.
Run this comparison yourself
Everything above came from one call to compare_cohorts. Swap in any two tags, industries, regions, areas or VC portfolios and you get the same answer for your own shortlist. It is an Understanding tool, included in the Understanding and Influence plans, or pay per call.
https://apis.io/mcp:
{"name": "compare_cohorts", "arguments": {"a": "tag:mcp", "b": "tag:ai-agents"}}
Over the REST API:
curl "https://apis.io/api/v1/cohorts/compare?a=tag:mcp&b=tag:ai-agents" \
-H "X-API-Key: $APIS_IO_KEY"
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Every comparison here is available over the APIs.io API and to AI agents over MCP.