PRO — a company's investment dimensions, each read AGAINST the measured cohort: the cohort median, mean and max, this company's rank and percentile, and an index where 1.0 is exactly typical. A raw dimension score is unreadable on its own; these are the columns that make it mean something.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It explains the output concept (comparative metrics) but does not disclose any side effects, data freshness, or access requirements. It adds value by explaining the meaning of the output, but lacks behavioral details like whether it's read-only or requires specific permissions.
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 concise and front-loaded with the key purpose. It uses a clear structure and avoids fluff. The final sentence adds context but is not redundant. It earns its place by explaining why the output matters.
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's complexity (comparative metrics) and lack of output schema, the description provides a good conceptual overview but omits practical details like the format of the index, how to interpret rank/percentile, or any prerequisites. It is adequate but not complete for an agent to fully understand the return value.
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% and the only parameter 'slug' is undocumented. The description does not explain what 'slug' refers to (e.g., company identifier) or how to obtain it. With a single required parameter and zero coverage, the description should compensate but does not.
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 provides a company's investment dimensions with comparative metrics (median, mean, max, rank, percentile, index) against a measured cohort. It distinguishes from siblings like company_peers and company_signals by focusing on dimension scores relative to a cohort, though it doesn't explicitly name alternatives.
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 implies usage when one needs to interpret raw dimension scores, but it does not explicitly state when to use this tool versus alternatives like company_peers or insights_dimensions. It lacks explicit when-not-to-use guidance or alternative tool references.
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.
