Compare / Markets / Biotechnology & Drug Discovery vs Pharmaceutical

compare_cohorts

Biotech vs Pharma: two of the least API-ready markets in the catalog

Biotech and pharma score 10.9 and 10.7, a near-tie at the bottom of the catalog. Agent readiness averages about 3.

Biotechnology & Drug Discovery

10.9 average
minimal on average
Agent readiness 3.2 · human-only
1322 scored providers · industry
VS

Pharmaceutical

10.7 average
minimal on average
Agent readiness 2.9 · human-only
606 scored providers · industry

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 coverageBiotechnology & Drug Discovery scores 0.2 points higher than Pharmaceutical AND is the better-enriched cohort: 0.1 points less catalog_gap and 1 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 a pairing of two of the lowest-scoring markets in the catalog. The 1,322 scored biotechnology and drug-discovery providers average 10.9 and the 606 pharmaceutical companies average 10.7. Agent readiness averages 3.2 and 2.9, the lowest on this page. Each cohort holds exactly one exemplar, and they share a 76.1 ceiling.

The facets explain why. Contract quality averages 3.1 and 2.6, contract governance 2.3 and 2.7, and operational transparency 2.6 and 2.5. Discoverability is the only facet above 15, at about 52 for both, because these companies have websites but rarely public APIs. Only 2% of either cohort ships a first-party MCP server, and none publishes Arazzo workflows.

The delta is technically depth-confounded, but at 0.2 points and one artifact directory it says almost nothing. What matters is the coverage: 1,307 of 1,322 biotech providers are thinly covered. Life-sciences data mostly sits behind partnerships and portals, not public developer programs, and that is where the opportunity lies.

The distributions

Biotechnology & Drug DiscoveryPharmaceuticalDelta
Kin Score, mean10.910.7+0.2
Kin Score, median8.88.9-0.1
Kin Score, highest76.176.1
Kin Score, std dev9.49.3
Agent readiness, mean3.22.9+0.3
Agent readiness, highest59.552.6

Delta is Biotechnology & Drug Discovery minus Pharmaceutical, as the tool returns it.

Where the providers sit

Biotechnology & Drug Discovery

exemplar 1strong 7developing 24thin 56emerging 297minimal 937

Pharmaceutical

exemplar 1strong 3developing 13thin 14emerging 148minimal 427

Facet averages

FacetBiotechnology & Drug DiscoveryPharmaceuticalDelta
Discoverability 52.6 52.3 +0.3
Contract Quality 3.1 2.6 +0.5
Contract Governance 2.3 2.7 -0.4
Operational Transparency 2.6 2.5 +0.1
Developer Ergonomics 9 8.1 +0.9
Access Clarity 13.9 13.8 +0.1

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.

ArtifactBiotechnology & Drug DiscoveryPharmaceuticalDelta
First-party MCP server2%2%0
Any MCP artifact (incl. derived)5%4%+1
OAuth scopes3%3%0
Spectral rules2%2%0
Arazzo workflows0%0%0
Agent skills0%0%0
Security artifact98%99%-1

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.

Biotechnology & Drug DiscoveryPharmaceutical
Declared members1565721
Scored (in the averages)1322 (84%)606 (84%)
Thinly covered1307597
Median artifact directories65
Mean catalog gap (points we could add)85.885.9
The full compare_cohorts response
{"a":{"kind":"industry","slug":"biotechnology","name":"Biotechnology & Drug Discovery","members":1565,"stats":{"kind":"industry","slug":"biotechnology","name":"Biotechnology & Drug Discovery","tier":"report","coverage":{"declared_members":1565,"resolved_members":1565,"scored_members":1322,"agent_scored_members":1322,"scored_share_pct":84,"unexamined_members":222,"enrichment_depth":{"mean_catalog_gap":85.8,"median_artifact_dirs":6,"n":1322,"thinly_covered":1307,"thinly_covered_of":1322,"thin_coverage_note":"1307 of 1322 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":10.9,"median":8.8,"min":0,"max":76.1,"stdev":9.4},"agent_readiness":{"mean":3.2,"median":0,"min":0,"max":59.5,"stdev":7.7},"band_distribution":{"exemplar":1,"strong":7,"developing":24,"thin":56,"emerging":297,"minimal":937},"facet_averages":{"discoverability":{"mean":52.6,"n":1322},"contract_quality":{"mean":3.1,"n":1322},"contract_governance":{"mean":2.3,"n":1322},"operational_transparency":{"mean":2.6,"n":1322},"developer_ergonomics":{"mean":9,"n":1322},"access_clarity":{"mean":13.9,"n":1322}},"adoption":{"mcp_pct":5,"mcp_first_party_pct":2,"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":3,"security_pct":98,"arazzo_pct":0,"rules_pct":2}}},"b":{"kind":"industry","slug":"pharmaceutical","name":"Pharmaceutical","members":721,"stats":{"kind":"industry","slug":"pharmaceutical","name":"Pharmaceutical","tier":"report","coverage":{"declared_members":721,"resolved_members":721,"scored_members":606,"agent_scored_members":606,"scored_share_pct":84,"unexamined_members":104,"enrichment_depth":{"mean_catalog_gap":85.9,"median_artifact_dirs":5,"n":606,"thinly_covered":597,"thinly_covered_of":606,"thin_coverage_note":"597 of 606 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":10.7,"median":8.9,"min":0,"max":76.1,"stdev":9.3},"agent_readiness":{"mean":2.9,"median":0,"min":0,"max":52.6,"stdev":7.6},"band_distribution":{"exemplar":1,"strong":3,"developing":13,"thin":14,"emerging":148,"minimal":427},"facet_averages":{"discoverability":{"mean":52.3,"n":606},"contract_quality":{"mean":2.6,"n":606},"contract_governance":{"mean":2.7,"n":606},"operational_transparency":{"mean":2.5,"n":606},"developer_ergonomics":{"mean":8.1,"n":606},"access_clarity":{"mean":13.8,"n":606}},"adoption":{"mcp_pct":4,"mcp_first_party_pct":2,"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":3,"security_pct":99,"arazzo_pct":0,"rules_pct":2}}},"comparable":true,"delta":{"kin_score_mean":0.2,"kin_score_median":-0.1,"agent_readiness_mean":0.3,"facets":{"discoverability":0.3,"contract_quality":0.5,"contract_governance":-0.4,"operational_transparency":0.1,"developer_ergonomics":0.9,"access_clarity":0.1},"adoption":{"mcp_pct":1,"mcp_first_party_pct":0,"mcp_note":null,"skills_pct":0,"scopes_pct":0,"security_pct":-1,"arazzo_pct":0,"rules_pct":0},"enrichment_depth":{"mean_catalog_gap":-0.1,"median_artifact_dirs":1}},"depth_confounded":true,"depth_note":"Biotechnology & Drug Discovery scores 0.2 points higher than Pharmaceutical AND is the better-enriched cohort: 0.1 points less catalog_gap and 1 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. Biotechnology & Drug Discovery and Pharmaceutical 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": "industry:biotechnology", "b": "industry:pharmaceutical"}}
Over the REST API:
curl "https://apis.io/api/v1/cohorts/compare?a=industry:biotechnology&b=industry:pharmaceutical" \
  -H "X-API-Key: $APIS_IO_KEY"
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