How far a standards body actually got: repositories published, how many graduated past unclassified and the resulting share, release cadence, contributor count — with the provenance of every figure and an explicit list of what is NOT measured. Free, because a specification is a public good.
| 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 full burden, and it excels by explicitly mentioning provenance of every figure and an explicit list of what is NOT measured. This is a strong form of transparency about data limitations, though it doesn't cover auth, errors, or side effects, which are less critical for a read-only metric tool.
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?
Two sentences, front-loaded with a clear question-like lead-in ('How far a standards body actually got:') and then a list of content elements. It is concise and each clause adds value, though the 'Free...' tagline is somewhat non-essential. Overall very efficient.
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 simple tool with one required string parameter and no output schema, the description covers the main purpose, output contents, and even limitations (what's not measured). It gives an agent enough context to know what to expect, though it could be clearer about the slug format and possible error conditions.
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 description does not explain what the required 'slug' parameter represents, despite the schema having zero description coverage and no helpful information. The description focuses on the output and context, leaving the parameter semantics entirely to inference. The tool name and description imply slug is a standards body identifier, but this is not made explicit.
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 this tool measures a standard body's progress, providing specific metrics (repositories, graduation, share, release cadence, contributors) and adds provenance and exclusion context. This goes well beyond a generic label and differentiates it from sibling tools like 'standard_repositories' by focusing on adoption and transparency.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of sibling tools, conditions that favor this tool, or any exclusions. The line 'Free, because a specification is a public good' hints at accessibility but doesn't help with selection.
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.
