Raven MCP

audit_api_contract

Read-onlyIdempotent

Run adversarial queries against a live API endpoint and compare responses to an expected schema. Get per-query verdicts to identify shape-valid but wrong results.

Instructions

Run adversarial queries against a live endpoint and return per-query verdict (shape-valid / shape-invalid / confident-wrong / uncertain) vs an expected shape schema + per-query expectations. Catches responses that are shape-valid but wrong.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "endpoint_url": "<endpoint_url>",
      "queries": "<queries>",
      "expected_shape_schema": "<expected_shape_schema>"
    },
    "name": "audit_api_contract"
  }
}

Input Schema

Name	Required	Description	Default
`queries`	Yes
`endpoint_url`	Yes
`expected_shape_schema`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.2.9Jul 28, 2026

TDQS

A3.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld, and non-destructive hints. The description adds behavioral transparency about live adversarial queries and the specific verdict categories returned, which are not inferable from annotations alone. There is no contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two tight sentences with front-loaded action and no filler. Every sentence earns its place: the first explains the operation and output, the second clarifies the key value proposition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description captures the high-level purpose and output verdicts, but the tool has complex nested parameters and no output schema. It does not fully describe how query expectations should be expressed or how the expected_shape_schema should be structured, though much can be inferred from the schema itself.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does little to explain the three required parameters beyond mentioning an expected shape schema and per-query expectations. Property names like endpoint_url, queries, and expected_shape_schema are somewhat self-explanatory, but the description fails to enrich their meaning or explain nested expectation fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it runs adversarial queries against a live endpoint and returns per-query verdicts. It clearly conveys the tool's unique purpose of catching shape-valid but wrong responses, but it does not explicitly distinguish itself from sibling tools like audit_contract.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when a live API's responses need adversarial validation against both a shape schema and per-query expectations. It does not provide explicit exclusion criteria or alternatives, so the guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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