The open-source tooling registry — 400+ tools in use across the companies API Evangelist tracks, each with adoption counts and technology-radar placement.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free text over title, summary, tags, and body. | |
| page | No | ||
| sort | No | ||
| tags | No | Tag slugs; match any by default. | |
| limit | No | ||
| match | No | any | |
| fields | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description must fully disclose behavioral traits, but it only describes the data content (adoption counts, radar placement). It does not mention that the tool performs a read-only search, any authentication or rate limitations, pagination behavior beyond parameter defaults, or the nature of the response. The lack of behavioral transparency leaves agents unaware of key operational expectations.
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 a single, concise sentence that front-loads the core purpose and key features (number of tools, adoption counts, radar placement). Every word serves to inform the agent, with no redundancy or unnecessary detail.
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?
Given the tool has 7 parameters and no output schema, the description is insufficient for complete understanding. It does not explain how parameters interact, what the return format looks like, or how to effectively narrow searches. Complex query options like 'match' and 'fields' remain unexplained, making the description inadequate for precise agent invocation.
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 only 29%, and the description adds no parameter meaning beyond the schema. Only the 'q' and 'tags' parameters have descriptions in the schema; others like 'page', 'limit', 'sort', 'match', and 'fields' are left undocumented. The description does not compensate for this gap, forcing agents to rely solely on parameter types and enum values without context on their effect.
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 tool as a registry for open-source tools, specifying it contains 400+ tools with adoption counts and technology-radar placement. It distinctly describes the resource and action (finding tools in the registry), distinguishing it from other 'find_*' siblings that deal with different entities like areas, posts, or services.
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 explicit guidance is provided on when to use this tool versus alternatives. With many sibling 'find_*' tools, the description does not differentiate usage contexts or explain when searching the tool registry is appropriate versus other registries. Users are left to infer from the tool name alone.
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.
