uacp-interop

What does your agent definition lose when it crosses a format boundary?

Every agent framework invented its own idea of what a tool is. MCP has tools/list. OpenAI function-calling has a parameters block with a narrow schema subset. UACP has a capability schema that uses JSON Schema properly. The differences between them are invisible until an agent fails in production, and nobody has tooling for measuring them.

This measures them. It takes a format-native agent description, runs the resulting artifact through a real JSON Schema validator, and reports every piece of information that does not survive — with evidence, and with a confidence tier saying how much the finding is worth.

uvx uacp-interop

No install, no clone, no API key. A run takes under 200 ms (median 139 ms over 20 runs).

It found five real defects in its own protocol

We built this to grade wippa-uacp, the protocol we wrote. The first run reported five blockers. All five were real, and all five are fixed:

That is the part worth caring about. A conformance checklist proves your implementation matches your own reading of a spec. It says nothing about whether your reading and your code agree with each other, or with anyone else's. That is what this measures instead, and every finding it produced here was one a checklist would have passed.

What it actually checks

1. Schema versus prose. Build a message the spec requires an implementation to accept, validate it against the shipped schema, report the rejection.
2. Schema versus the implementations. Check each reference implementation can represent what the schema declares, and that they model the same envelope.
3. Cross-format divergence, both directions. Map a framework-native agent description into a target descriptor and validate the artifact actually produced; separately, test whether a capability survives being expressed in a format's native tool schema.
4. Target-format rules JSON Schema cannot express. An OpenAI-compatible parameters block must carry properties, so a capability publishing {"type": "object"} gets the whole tool list rejected with a 400. The capability schema accepts that shape perfectly well, so the constraint only exists in the target format — which is the same class as a schema keyword with no representation there.

Losses are computed, not asserted. SCHEMA_VIOLATION findings come from running the real validator over the artifact an adapter produced — a hand-copied regex predicting the same thing was removed in 0.1.0 because it double-counted every finding. Divergences in the other direction are found by walking the source JSON Schema and collecting keywords outside the target format's supported subset.

Every finding carries evidence and a confidence tier

This is the part most tooling in this space skips, and it is the reason to trust the output:

The report prints the tier for every entry and says which findings rest on weaker evidence. The bundled AutoGen entry models a documented constructor surface rather than an observed serialization, so it is labelled as the weakest in the corpus. An entry that cannot be sourced honestly is worse than a missing one, and the loader refuses to guess: a missing or misspelt confidence is an error, not a silent downgrade to the weakest tier.

Add your format

A worked example, captured from a live server rather than synthesised:

uvx uacp-interop --corpus examples/deepwiki-mcp.entry.json

examples/deepwiki-mcp.capture.json is the raw tools/list from mcp.deepwiki.com (DeepWiki 2.14.3), taken over a live MCP handshake, and the entry is a projection of it. scripts/verify_corpus_fidelity.py checks the two agree, because that check exists after an outputSchema was dropped in silence during exactly this conversion and the harness reported the omission as a major finding on all three tools — as if the server had left it out. It had not. A field carried from a real artifact must either appear in the entry or be declared in dropped_source_fields, and a carried field must equal the source value.

A corpus entry is pure data and the checks read nothing else, so measuring a format needs no Python and no pull request:

uacp-interop --corpus my-format.json

{
  "framework": "my-framework",
  "agent_id": "my_agent",
  "description": "What this is.",
  "confidence": "documented",
  "provenance": "Where you read the shape, and when",
  "capabilities": [
    { "name": "web.search", "description": "Search the web.",
      "parameters": { "type": "object",
                      "properties": { "query": { "type": "string" } } } }
  ]
}

It gets its own section in the report, computed by the same checks as everything else. --corpus also takes a directory and is repeatable. To make it permanent, append a ForeignAgent to uacp_interop/corpus.py — about twenty minutes, and CONTRIBUTING.md has the recipe and the provenance rules.

Install

uvx uacp-interop                       # or: pipx run uacp-interop

pip install -e .                       # from a checkout
uacp-interop

Every renderer derives its counts from the same findings, and a test asserts they cannot disagree — a renderer that tallied severities independently is the 0.1.0 double-counting bug in a new disguise. --badge reports only the spec and implementation audit, because a blocker in a foreign format is a finding about that format and reddening the badge would misattribute the fault.

It cannot go stale quietly

The report is only worth anything if it is current, so:

• the vendored schemas are pinned to an upstream commit in uacp_interop/schemas/PROVENANCE.json, and CI fails on drift
• a scheduled workflow re-runs the probe weekly and opens an issue if the result stops matching the committed artifact
• docs/scorecard.svg in this README is generated from a live run, and CI fails if it goes stale

A conformance report that silently tests an old protocol is worse than no report.

Known limits

• One external $ref needs a local registry to resolve. The schemas claim canonical URLs on a host that does not resolve, so a plain offline validator fetching agent-descriptor will attempt a network call. This harness builds a referencing.Registry for exactly that reason, and the finding is reported rather than hidden.
• No behavioural half. Everything here is structural: these two definitions cannot both be true, not these two implementations produced different messages. That is the harder half and it is not built.
• The corpus is small, because the honest entry is one you can source. Three ship; a LangGraph entry is open.
• Not yet format-to-format. It measures loss crossing into a target descriptor and loss expressing one in a native tool schema, not directly between two foreign formats. That is the obvious next thing and it is not built.

Verifying it

pip install -e ".[dev]"
pytest -q

The suite is mostly meta-tests: each mutates a copy of the input so the defect it looks for is absent, and asserts the check goes quiet. A check that quietly stopped working fails the suite rather than passing silently — which has twice caught a guard in this repo that had quietly stopped guarding anything.

MIT.