An MCP server that enables semantic discovery and execution of OpenAPI endpoints using in-memory embeddings and FAISS indexing. It consumes a remote OpenAPI JSON document (OPENAPI_JSON_DOCS_URL) and builds an endpoint-centric index where each endpoint (path + method) is stored as its own chunk with parameter-aware embeddings and response schema context. A fast, asynchronous FastAPI server exposes tools named by MCP_API_PREFIX (default "any_openapi"), such as {prefix}_api_request_schema to retrieve endpoint details and {prefix}_make_request to perform API requests. The server supports an optional GLOBAL_TOOL_PROMPT to influence tool descriptions for NLP agents and can be deployed in multi-instance configurations to serve multiple API ecosystems. Endpoints can be queried using natural language; the internal pipeline is: query -> Embedding -> FAISS TopK -> OpenAPI docs -> MCP Client -> construct and execute request. It emphasizes in-memory speed, with endpoint-based chunking to handle large specs while preserving context, and requires no local OpenAPI file access. This design is intended to work with Claude Desktop workflows and supports configurable tool namespaces for flexible integration.