Who is actually behind a standard: the governing bodies and their membership, the working groups, the member companies, and the named people. Carries the caveats where our machine-readable source disagrees with the body's own published roster. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It adds valuable context by stating that caveats are carried when the machine-readable source disagrees with the body's published roster, and it notes the tool is free. It does not describe return shape or error behavior, but for a simple reference lookup this is a moderate gap.
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 concise sentences, front-loaded with the tool's purpose and followed by a useful caveat. The word 'Free' is minor, but every sentence serves a purpose and there is no fluff.
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 one-parameter lookup, the description covers the domain and gives an important data-quality caveat. However, because there is no output schema and the slug parameter is unexplained, the agent is left to infer both the input semantics and the response structure.
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 input schema has only 'slug: string' with 0% description coverage, and the description never mentions slug, its format, or an example. The only implicit clue is that slug likely refers to a standard identifier, but this is not stated, leaving the agent to guess.
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 identifies the resource as the coalition/backing behind a standard and enumerates the content: governing bodies, working groups, member companies, and named people. It is not a tautology and implies a lookup/read operation, though it lacks an explicit verb such as 'retrieves' or 'lists.'
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 no explicit when-to-use guidance or exclusions. The phrasing implies it is for identifying who governs or sponsors a standard, but it does not distinguish this from sibling standard-related tools like standard_adoption or standard_repositories.
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.
