Given any doc (type + slug), the most related content across the whole network, ranked by shared-tag relevance (rare, specific tags weigh most). "More like this," across stories, areas, guidance, papers, conversations, and more.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| type | Yes | ||
| limit | No | ||
| types | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations or output schema, the description carries the full transparency burden. It does disclose a useful behavioral detail: results are ranked by shared-tag relevance with rare/specific tags weighted more. But it does not describe output shape, pagination, error behavior, or whether the source doc is excluded from results.
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 compact and front-loaded with the core input and output, followed by the ranking heuristic. There is slight redundancy between 'across the whole network' and the later list of content types, but overall every sentence earns its place.
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 4-parameter tool with no annotations, no output schema, and 0% schema coverage, the description covers the essential invocation and ranking behavior but leaves valid gaps: the meaning of 'limit' and 'types' is not stated, and the return structure is not described. It is adequate for basic use but not fully complete.
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 0%, so the description must explain the parameters. It usefully clarifies that 'type' and 'slug' together identify a document, but it provides no explanation for the 'limit' or 'types' parameters, leaving significant ambiguity for optional filtering and result size.
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 defines a specific operation: given a doc identified by type and slug, return the most related content across the network. It also distinguishes itself from sibling find_* tools by emphasizing cross-type relatedness ('across stories, areas, guidance, papers, conversations, and more') rather than a single content type.
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 phrase 'More like this' implies the intended use case: when you have a document and want related content across types. However, it does not explicitly contrast with sibling tools like find_papers, find_conversations, or find_tags, nor does it state when not to use it.
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.
