Compare two OpenAPI versions and classify every change as breaking, non-breaking, or an addition — at path, operation, parameter, request-body, and response granularity.
| Name | Required | Description | Default |
|---|---|---|---|
| new | Yes | The CANDIDATE version to compare against the baseline. Aliases: `to`, `revision`. | |
| old | Yes | The BASELINE version of the document. Aliases: `from`, `base`. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It omits critical behaviors: how inputs are supplied (file paths, content strings, objects), the output format (since no output schema exists), or whether the operation is read-only. The description only states the what, not the how.
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, well-structured sentence that front-loads the primary action and outcome. No filler or redundant phrases; every clause adds useful context.
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 (multiple granularities) and the absence of an output schema, the description should clarify both input format and expected output structure. It does not explain how to pass the OpenAPI versions (string or object meaning) or what the classification result looks like, leaving the agent with gaps for successful invocation.
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 100%, and both parameters (old and new) already have descriptive schemas with aliases. The description adds no parameter-specific detail beyond reinforcing that they are OpenAPI versions, so it does not meaningfully enhance the schema's semantics.
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 a specific action ('Compare') on a specific resource ('two OpenAPI versions') and the classification outcome (breaking, non-breaking, addition) with granularity levels. This distinguishes it from other API-related siblings and leaves no ambiguity about what the tool does.
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 the use case (comparing API versions) clearly, and the context is unambiguous. However, it does not explicitly mention when not to use it or point to alternative tools like validate_api or api_coverage, so it lacks explicit exclusions.
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.
