Lint an OpenAPI, AsyncAPI, Arazzo, or JSON Schema document against the curated best-of-breed ruleset (Spectral) and return the findings. Pass your own ruleset to run rules you own instead of the catalog — which is what you should do before gating on anything.
| 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. It states 'lint' and 'return the findings' but doesn't disclose the output format, whether the operation is read-only, or any side effects. This is minimal disclosure for a tool with no structured safety hints.
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 two sentences, front-loads the main action, and includes a targeted usage tip. No filler; every sentence adds value.
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?
No output schema exists, and the description only says 'return the findings' without describing the structure or error handling. It covers the input and offers a custom rule option, but lacks details on what the agent will receive, which is incomplete for an agent deciding how to use the result.
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?
The schema fully describes the 'document' parameter, but the description introduces the 'ruleset' parameter which is not in the schema (it appears allowed via additionalProperties). This adds meaning beyond the schema, though the semantics of the ruleset are not detailed.
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 clearly states a specific verb ('Lint') and resource (OpenAPI, AsyncAPI, Arazzo, or JSON Schema documents) and mentions the ruleset (Spectral). This makes the tool's purpose unambiguous and distinguishes it from generic validation tools, though it doesn't name specific siblings.
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 description provides a clear usage tip: pass your own 'ruleset' for gating, implying that the default catalog may not be suitable for gating. This gives contextual guidance, but it doesn't explicitly contrast with alternative tools or state when not to use this tool.
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.
