Measure how much of an API description your governance actually inspects — coverage by section, each rule's reach, and the rules that match nothing. Like test coverage, one altitude up.
| Name | Required | Description | Default |
|---|---|---|---|
| document | Yes | The API description to operate on — OpenAPI, AsyncAPI, Arazzo or JSON Schema. A YAML/JSON string or an already-parsed object; both are accepted. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the nature of the output (coverage by section, per-rule reach, unmatched rules), which signals a read-only analysis. However, it does not explicitly confirm the operation is non-mutating, state prerequisites (e.g., whether governance rules must already be configured), or describe error behavior for malformed documents.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact at roughly two sentences and front-loads the core purpose before the analogy. It is efficient with no filler, though the 'one altitude up' metaphor is slightly expansive and adds limited concrete value for an agent deciding whether to call the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter analysis tool whose parameter is fully documented in the schema and which has no output schema, the description adequately conveys the output semantics (coverage by section, rule reach, and unmatched rules). Nothing critical for correct invocation appears to be missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already thoroughly documents the lone 'document' parameter (format accepted, both YAML/JSON strings and parsed objects). The description adds no parameter-specific meaning beyond the schema, so the baseline 3 is appropriate — the structured schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Measure how much of an API description your governance actually inspects — coverage by section, each rule's reach, and the rules that match nothing.' This is a clear, specific purpose. However, it does not explicitly distinguish itself from closely related siblings like validate_api, governance_report, or govern_estate — the differentiation is only implicit in the 'governance inspection' framing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The analogy 'Like test coverage, one altitude up' implies a use case (analyzing governance thoroughness) but provides no explicit when-to-use or when-not-to-use guidance, and no alternative tools are named. The usage context is implied rather than stated, leaving an agent to infer when this tool is appropriate versus siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes with clear descriptions, reducing ambiguity. However, some overlap exists between search tools like 'find_posts' and 'search_api_evangelist', though they target different scopes (stories vs. unified search). Overall, an agent can reasonably differentiate them.
The majority of tools follow a verb_noun pattern (e.g., find_areas, get_post), but several use noun_noun or inconsistent prefixes (e.g., api_coverage, company_gaps, insights_adoption). This inconsistency can confuse pattern recognition, though the pattern is still readable.
With 56 tools, the server is overloaded for a typical MCP context. While the domain is broad, the sheer number risks agent confusion and selection errors. Calibration suggests 25+ tools are excessive, and this server far exceeds that threshold.
The tool set covers a wide range of API governance, search, analysis, and generation tasks. There are no obvious dead ends for navigating the API Evangelist network, though some areas (e.g., direct API creation) are intentionally out of scope. Minor consolidation could improve efficiency.
