PRO — the companies most comparable to this one (same sub-sector where the data supports it, same industry otherwise), each with signal totals and top dimensions. The response says which basis it used.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does a good job: it explains the sub-sector-to-industry fallback behavior and the fact that the response will state which basis was used. It also tells the agent that each result carries signal totals and top dimensions, which goes beyond the minimal input schema.
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 information-dense sentence with no filler. It packs purpose, the selection fallback, output composition, and a note about the response basis into minimal words, and the key idea is front-loaded.
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?
The description is reasonably complete for a small input-schema tool: it tells the agent what data is returned and how peer selection works. The main gap is that limit is not described and there is no explicit guidance about when to choose this tool over related sibling tools.
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 neither the input schema nor the description clarifies the limit parameter's role. 'slug' is somewhat inferable from 'this one', and the general purpose implies it is a company identifier, but limit is left completely undocumented in prose.
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 what the tool returns: the companies most comparable to a given company. It goes well beyond a tautology by explaining the comparability rule (same sub-sector where supported, same industry otherwise) and by naming output content like signal totals and top dimensions, which distinguishes it from sibling company_* tools.
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 usage context is implied rather than explicit: it seems intended for retrieving peer companies for a given company, but it does not say when to prefer this over siblings like company_dimensions, company_signals, or find_related. It also provides no exclusions, prerequisites, or 'not for X' guidance.
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.
