Score an API's maturity 0-100 with a letter grade across design, governance coverage, documentation, and (from an apis.json) discoverability, agent-readiness, and operations. Pass your own ruleset — a score against rules you never adopted means little.
| 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?
With no annotations, the description must carry the full burden of behavioral disclosure. It mentions the output (score and grade) and that it derives certain dimensions from an apis.json, but it does not disclose whether the operation is read-only, any authentication requirements, rate limits, or side effects. It lacks behavioral transparency beyond the core action.
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, with the purpose front-loaded and a concise rationale for the ruleset. It is efficient and avoids unnecessary detail, though the second sentence could be more actionable by specifying how to pass the ruleset.
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?
The description leaves several gaps: the ruleset parameter is mentioned but not defined (property name, type, format); the role of 'apis.json' is ambiguous (is it a type of document or additional input?); the output format beyond 'score and letter grade' is unspecified; and there is no mention of error handling or prerequisites. Given the tool's complexity (multiple scoring dimensions) and lack of output schema, the description is insufficient for an agent to reliably call it.
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 describes the 'document' parameter well (types and formats), giving 100% coverage for that parameter. However, the description adds crucial information about a 'ruleset' parameter that is not represented in the schema (it would be an additional property). This guidance is valuable for an agent to correctly invoke the tool, thus the description enriches parameter understanding.
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 the tool's function: scoring an API's maturity on a 0-100 scale with a letter grade, and enumerates the specific dimensions (design, governance coverage, documentation, discoverability, agent-readiness, operations). This is a distinct verb-resource pair that separates it from sibling tools like api_coverage (which might measure coverage) and validate_api (which might check validity).
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 instructs the user to pass a custom ruleset and argues why that matters, which is a usage directive. However, it does not explicitly state when to use this tool over alternatives (e.g., certify_api, validate_api) or provide exclusions. The context is implied but not explicit.
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.
