The API Evangelist video library — interviews and discussions from across the API space, searchable by transcript.
| 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?
With no annotations (0%), the description must fully disclose behavioral traits. It only indicates 'searchable by transcript', omitting details like pagination behavior (page/limit), sorting, field selection, or error handling. The agent lacks critical behavioral context for correct invocation.
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?
At 15 words, the description is concise and front-loaded. However, it sacrifices informativeness—for a 7-parameter tool, brevity becomes under-specification. It earns its sentence length but could convey more key details without being bloated.
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 no output schema, zero annotations, and 7 parameters, the description is incomplete. It fails to specify return format, result structure, or how parameters like 'fields', 'sort', and 'match' affect output. The agent lacks enough information to fully understand tool behavior.
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 low (29%). The description adds no parameter meaning beyond what the schema (partially) provides. It does not explain the 'tags', 'match', 'fields', or 'sort' parameters, failing to compensate for the schema's gaps.
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 purpose: searching a video library of API interviews and discussions by transcript. It explicitly names the resource ('video library') and the search capability, distinguishing it from siblings focused on other content types (e.g., conversations, papers).
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 usage guidelines are provided. The description does not mention when to use this tool over alternatives (e.g., 'find_conversations', 'find_papers') or specify any exclusions or prerequisites. The agent receives no guidance on selection context.
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.
