api-to-mcp
Related Servers
Alternatives to api-to-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceA lightweight, zero-config MCP server that makes documentation and API specifications instantly accessible to AI models using the llms.txt standard. It enables searching and retrieving full documentation, OpenAPI, and AsyncAPI specs without requiring a complex RAG infrastructure or vector database.7 npm1Apache 2.0
- FlicenseNot gradedqualityBmaintenanceTransforms OpenAPI specs into governed MCP applications with a local-first studio, OAuth, simulation, and Docker deployment.-
- AlicenseNot gradedqualityDmaintenanceMCP server that converts OpenAPI documentation to Markdown with tolerant parsing, enabling LLMs to batch query and explore APIs.10 npm1MIT
- FlicenseNot gradedqualityDmaintenanceConverts any Swagger/OpenAPI specification into an MCP server, enabling AI assistants to intelligently query API endpoints, schemas, and generate code examples.18-
- AlicenseNot gradedqualityCmaintenanceEnables users to convert any OpenAPI or Swagger spec URL into a hosted MCP server on the Agentic Tools Platform, with tools for analyzing, trimming, and managing OpenAPI specifications.MIT
- AlicenseNot gradedqualityBmaintenanceConverts any OpenAPI 3.x spec into a live MCP server, making every endpoint a validated tool that AI agents can call without writing glue code.6 npmMIT
TDQS
Scored across 14 tools
Each tool maps to a distinct pipeline stage (preflight, discovery, doc ingestion, drafting, saving, linting, generation, testing, live verification), and descriptions clarify boundaries well. Minor overlap exists between ingest_openapi and draft_entry (both parse OpenAPI) and between try_tool and live_verify (both call real tools), but the debug-vs-full-verification distinction is spelled out.
All names use consistent snake_case, and most follow a verb_noun pattern (save_entry, read_docs, lint_entry, generate_server). A few deviate: catalog_search/catalog_get invert to noun_verb, and doctor/workspace are bare nouns, but everything remains readable and predictable.
14 tools sit comfortably in the well-scoped range and each corresponds to a genuine step in the authoring-to-verification pipeline. No tool appears redundant or filler.
The surface covers a full lifecycle: discover, ingest docs, draft, save, lint, generate, test, and live-verify, which is thorough for building API-to-MCP servers. Minor gaps like a delete/remove-entry operation or explicit category listing are absent but easily worked around.