mcp-builder-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it fetches and reads response bodies from URLs (including specific examples like raw.githubusercontent.com), handles both read and download-style URLs, returns validation results with analysis and scaffolding, and outlines success/failure outcomes. However, it lacks details on rate limits, authentication needs, or error handling beyond validation failures.

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 well-structured and front-loaded with the core purpose, followed by usage details and outcomes. Most sentences add value, but it includes some redundancy (e.g., repeating URL examples) and could be slightly more concise without losing clarity.

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 complexity of a validation tool with 2 parameters, no annotations, and no output schema, the description is reasonably complete. It covers input types, behavioral aspects, and success/failure outcomes, though it could benefit from more details on error scenarios beyond validation failures and the optional parameter's role.

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%, so the description must compensate for undocumented parameters. It explains the semantics of 'openapi_input' by detailing acceptable values (file path, URL, or inline JSON/YAML) and URL examples, but does not clarify the purpose or usage of the optional 'input_type' parameter. This partial compensation results in a baseline score.

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 with specific verbs ('validates OpenAPI 2/3 specs') and resources (file path, URL, or inline JSON/YAML). It distinguishes itself from siblings by being explicitly labeled as 'Step 1 of 6' in a workflow, making its role unique among tools like generate_mcp_server or upload_to_github.

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 explicit usage guidance: it specifies when to use this tool ('Step 1 of 6'), what types of inputs are accepted (file path, URL, or inline JSON/YAML), and includes a clear directive for next steps ('You must present results to the user and get confirmation before calling step 2'). This gives strong context for when to use it versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.