Browse the demand-side entity catalog — every service, tool and standard companies are measured against, ranked by how many carry it. Free. This is the left-hand side of the inversion: pick an entity here, then call entity_demand to see WHO.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Substring match on the entity name. | |
| kind | No | ||
| page | No | ||
| limit | No | ||
| min_companies | No | Only entities carried by at least this many companies. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry behavioral context. It mentions 'Free' and hints at a read-only browsing operation, but does not state side effects, rate limits, or pagination behavior. The description does not contradict any annotations (none exist), but could be more explicit about operational characteristics.
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 well-structured, using two sentences to convey purpose, context, and a pointer to a complementary tool. It avoids verbosity while providing relevant information without 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?
The description gives enough to understand the core functionality and relationship to another tool, but it lacks details about output format, ordering, or parameter effects. Since no output schema is provided, some return expectations are unspecified, but the description is not severely incomplete for a simple browse tool.
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 coverage is low (only 2 of 5 parameters have descriptions: q and min_companies). The description does not explain kind, page, or limit, nor does it elaborate on how ranking works or how parameters affect results. With less than 50% coverage, the description should compensate but does not, leaving these parameters ambiguous.
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: browsing the demand-side entity catalog, which includes services, tools, and standards, ranked by how many companies carry them. It distinguishes this tool from siblings by focusing on entities and explicitly mentions the inversion relationship with entity_demand.
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 provides usage context: it mentions the 'left-hand side of the inversion' and suggests calling entity_demand afterward, which guides when to use this tool. However, it does not explicitly contrast with other find_* tools, but the focused entity scope gives sufficient 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.
