Compare / Markets / Accounting & Finance Operations vs Human Resources & Payroll

compare_cohorts

Accounting vs HR & Payroll: the back office, compared

Accounting and finance-ops platforms outscore HR and payroll by 4.2 points and ship first-party MCP at nearly twice the rate.

Accounting & Finance Operations

27.4 average
thin on average
Agent readiness 16.2 · agent-aware
920 scored providers · industry
VS

Human Resources & Payroll

23.2 average
emerging on average
Agent readiness 12.2 · agent-aware
884 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 coverageAccounting & Finance Operations scores 4.2 points higher than Human Resources & Payroll AND is the better-enriched cohort: 2.4 points less catalog_gap and 3 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.

Both are back-office software, and both are increasingly the system an agent needs to read and write. The 920 scored accounting and finance-operations providers average 27.4, against 23.2 for 884 HR and payroll providers. Accounting holds 16 exemplars to HR’s 9, and the medians are 24.2 against 17.9.

Accounting leads on every facet, most clearly on developer ergonomics (26.9 against 20.8), contract quality (22.1 against 17.4) and operational transparency (16.8 against 12.2). On agents, 20% of accounting providers publish a first-party MCP server, against 12% in HR and payroll. Agent readiness is 16.2 against 12.2.

HR and payroll has the single highest score in this pair, 95.3, so the market’s best is excellent and its middle is thin. The delta is depth-confounded: three more artifact directories on the median accounting member. See Gusto vs Rippling for two HR platforms head to head.

The distributions

Accounting & Finance OperationsHuman Resources & PayrollDelta
Kin Score, mean27.423.2+4.2
Kin Score, median24.217.9+6.3
Kin Score, highest84.595.3
Kin Score, std dev17.816.4
Agent readiness, mean16.212.2+4
Agent readiness, highest66.269.1

Delta is Accounting & Finance Operations minus Human Resources & Payroll, as the tool returns it.

Where the providers sit

Accounting & Finance Operations

exemplar 16strong 53developing 186thin 179emerging 292minimal 194

Human Resources & Payroll

exemplar 9strong 36developing 124thin 140emerging 348minimal 227

Facet averages

FacetAccounting & Finance OperationsHuman Resources & PayrollDelta
Discoverability 60.7 59.1 +1.6
Contract Quality 22.1 17.4 +4.7
Contract Governance 7.8 6.7 +1.1
Operational Transparency 16.8 12.2 +4.6
Developer Ergonomics 26.9 20.8 +6.1
Access Clarity 33.2 30.2 +3

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.

ArtifactAccounting & Finance OperationsHuman Resources & PayrollDelta
First-party MCP server20%12%+8
Any MCP artifact (incl. derived)21%13%+8
OAuth scopes17%14%+3
Spectral rules16%13%+3
Arazzo workflows2%2%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.

Accounting & Finance OperationsHuman Resources & Payroll
Declared members948928
Scored (in the averages)920 (97%)884 (95%)
Thinly covered827802
Median artifact directories118
Mean catalog gap (points we could add)76.178.5
The full compare_cohorts response
{"a":{"kind":"industry","slug":"accounting-finance-ops","name":"Accounting & Finance Operations","members":948,"stats":{"kind":"industry","slug":"accounting-finance-ops","name":"Accounting & Finance Operations","tier":"report","coverage":{"declared_members":948,"resolved_members":948,"scored_members":920,"agent_scored_members":920,"scored_share_pct":97,"unexamined_members":25,"enrichment_depth":{"mean_catalog_gap":76.1,"median_artifact_dirs":11,"n":920,"thinly_covered":827,"thinly_covered_of":920,"thin_coverage_note":"827 of 920 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":27.4,"median":24.2,"min":0,"max":84.5,"stdev":17.8},"agent_readiness":{"mean":16.2,"median":12.2,"min":0,"max":66.2,"stdev":17.6},"band_distribution":{"exemplar":16,"strong":53,"developing":186,"thin":179,"emerging":292,"minimal":194},"facet_averages":{"discoverability":{"mean":60.7,"n":920},"contract_quality":{"mean":22.1,"n":920},"contract_governance":{"mean":7.8,"n":920},"operational_transparency":{"mean":16.8,"n":920},"developer_ergonomics":{"mean":26.9,"n":920},"access_clarity":{"mean":33.2,"n":920}},"adoption":{"mcp_pct":21,"mcp_first_party_pct":20,"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":2,"rules_pct":16}}},"b":{"kind":"industry","slug":"human-capital-management","name":"Human Resources & Payroll","members":928,"stats":{"kind":"industry","slug":"human-capital-management","name":"Human Resources & Payroll","tier":"report","coverage":{"declared_members":928,"resolved_members":928,"scored_members":884,"agent_scored_members":884,"scored_share_pct":95,"unexamined_members":39,"enrichment_depth":{"mean_catalog_gap":78.5,"median_artifact_dirs":8,"n":884,"thinly_covered":802,"thinly_covered_of":884,"thin_coverage_note":"802 of 884 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":23.2,"median":17.9,"min":0,"max":95.3,"stdev":16.4},"agent_readiness":{"mean":12.2,"median":2.5,"min":0,"max":69.1,"stdev":15.8},"band_distribution":{"exemplar":9,"strong":36,"developing":124,"thin":140,"emerging":348,"minimal":227},"facet_averages":{"discoverability":{"mean":59.1,"n":884},"contract_quality":{"mean":17.4,"n":884},"contract_governance":{"mean":6.7,"n":884},"operational_transparency":{"mean":12.2,"n":884},"developer_ergonomics":{"mean":20.8,"n":884},"access_clarity":{"mean":30.2,"n":884}},"adoption":{"mcp_pct":13,"mcp_first_party_pct":12,"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":14,"security_pct":99,"arazzo_pct":2,"rules_pct":13}}},"comparable":true,"delta":{"kin_score_mean":4.2,"kin_score_median":6.3,"agent_readiness_mean":4,"facets":{"discoverability":1.6,"contract_quality":4.7,"contract_governance":1.1,"operational_transparency":4.6,"developer_ergonomics":6.1,"access_clarity":3},"adoption":{"mcp_pct":8,"mcp_first_party_pct":8,"mcp_note":null,"skills_pct":0,"scopes_pct":3,"security_pct":0,"arazzo_pct":0,"rules_pct":3},"enrichment_depth":{"mean_catalog_gap":-2.4,"median_artifact_dirs":3}},"depth_confounded":true,"depth_note":"Accounting & Finance Operations scores 4.2 points higher than Human Resources & Payroll AND is the better-enriched cohort: 2.4 points less catalog_gap and 3 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. Accounting & Finance Operations and Human Resources & Payroll 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:accounting-finance-ops", "b": "industry:human-capital-management"}}
Over the REST API:
curl "https://apis.io/api/v1/cohorts/compare?a=industry:accounting-finance-ops&b=industry:human-capital-management" \
  -H "X-API-Key: $APIS_IO_KEY"
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