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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

42.6 average
developing on average
Agent readiness 33.9 · agent-ready
1460 scored providers · tag
VS

AI Agents

31.5 average
thin on average
Agent readiness 19.6 · agent-aware
1034 scored providers · tag

Each sun is drawn from the cohort's averages: six facet rings, the mean Kin Score and mean agent readiness.

Check this first: the gap is partly our coverageMCP 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.

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

MCPAI AgentsDelta
Kin Score, mean42.631.5+11.1
Kin Score, median42.829.3+13.5
Kin Score, highest9487.8
Kin Score, std dev17.319.5
Agent readiness, mean33.919.6+14.3
Agent readiness, highest91.873.8

Delta is MCP minus AI Agents, as the tool returns it.

Where the providers sit

MCP

exemplar 131strong 269developing 425thin 324emerging 285minimal 26

AI Agents

exemplar 46strong 113developing 211thin 196emerging 282minimal 186

Facet averages

FacetMCPAI AgentsDelta
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.

ArtifactMCPAI AgentsDelta
First-party MCP server79%45%+34
Any MCP artifact (incl. derived)92%45%+47
OAuth scopes47%17%+30
Spectral rules13%12%+1
Arazzo workflows4%4%0
Agent skills0%0%0
Security artifact99%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.

MCPAI Agents
Declared members14631049
Scored (in the averages)1460 (100%)1034 (99%)
Thinly covered1136890
Median artifact directories2114
Mean catalog gap (points we could add)67.774.7
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.

From any MCP client connected to 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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  • compare_providersUNDERSTANDING — two or more providers side by side: facets, artifacts, economics.
  • compare_cohortsUNDERSTANDING — two markets on one rubric, with the coverage check.
  • find_cohortsEvery scored population of providers in the catalog.
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