Turn any API into an AI agent tool. Point at an OpenAPI spec → get a working MCP server in 10 seconds.
Single tool 'dynamic_tools_from_openapi' has critical definition gaps. Tool name lacks action verb (should be 'generate_tools_from_openapi' or similar). Description is present (94 chars) but generic and does not explain WHEN to use this tool or WHAT it returns. No parameter descriptions. No output schema documented. Tool definition is inferred from code rather than explicitly registered with full metadata.
Tools are dynamically generated from OpenAPI spec endpoints. Each endpoint becomes a tool with name derived from operationId, description from summary/description, and input schema from parameters and requestBody.
Input schema is empty object with no properties. Description states 'Schema varies per endpoint' but provides no actionable structure for the LLM.
Tool name 'dynamic_tools_from_openapi' does not start with action verb. Should be 'generate_tools_from_openapi', 'create_tools_from_openapi', or 'build_tools_from_openapi' to signal intent. LLMs rely on verb-first naming to infer what the tool does.
No output schema documented. LLMs cannot plan downstream calls or extract tool definitions without knowing what fields are returned (tool names, descriptions, input schemas, etc.).
Tool definition inferred from code rather than explicitly registered with full metadata in server setup. Combined with schema/description gaps, actual score is 18.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 27 | 2026-07-28+ | v2 |
Description does not explain prerequisites, when to call this tool, or what the LLM should do with the returned tools. Generic description wastes opportunity to guide tool selection.