Generate runnable curl, JavaScript, and Python samples for every operation, with path params filled in and a realistic body.
| Name | Required | Description | Default |
|---|---|---|---|
| document | Yes | The API description to operate on — OpenAPI, AsyncAPI, Arazzo or JSON Schema. A YAML/JSON string or an already-parsed object; both are accepted. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden of behavioral disclosure. It does claim the output is 'runnable' and that path params are filled in, which indicates the tool performs substitution and validation. However, it does not mention potential failure modes (invalid input handling, timeouts, rate limits) or the exact structure of the returned samples. This is partial disclosure but not comprehensive.
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?
It is a single, dense sentence that front-loads the action and deliverable, with no filler words. Every clause adds information: the languages (curl, JS, Python), the scope (every operation), and the quality criteria (path params filled in, realistic body).
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 low-complexity tool with one fully documented parameter and no output schema, the description is sufficient: an agent knows exactly what to pass and what to expect. The only minor gap is that it doesn't explicitly state that the output is a mapping of operation→snippet, but that is implied by 'every operation'. Stylistically, it's near-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?
The input schema already has 100% coverage of the single parameter 'document' with a description explaining accepted types (string or object) and formats (OpenAPI, AsyncAPI, Arazzo, JSON Schema). The tool description adds no additional parameter-level detail, so the baseline of 3 for high schema coverage applies.
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 states a specific verb ('Generate'), a concrete deliverable ('runnable curl, JavaScript, and Python samples'), a scope ('for every operation'), and two quality guarantees ('path params filled in' and 'realistic body'). This is far from a tautology and clearly distinguishes the tool from all listed siblings, none of which hint at code generation.
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 purpose is self-evident: when you need code samples for API operations. However, there is no explicit statement of when to use this tool versus alternatives (e.g., mock_payloads for payload-only generation) or any guidance on when not to use it. The usage context is implied but not spelled out.
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.
