"Using llama.cpp and Searxng with local models" matching MCP servers:

An MCP server that wraps the @apitomy/data-models library for querying, validating, and editing OpenAPI and AsyncAPI documents.

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

Middleware that converts a serverless handler with OpenAPI spec into an MCP endpoint following stateless MCP principles.

An open standard server implementation that enables AI assistants to directly access APIs and services through Model Context Protocol, built using Cloudflare Workers for scalability.

Enables natural language interaction with FastAPI applications through Google's Gemini AI using MCP tools. Provides CRUD operations for user management and application health monitoring through conversational prompts.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Parses Swagger 2.0 and OpenAPI 3.x specifications, exposing API endpoints, schemas, and authentication through MCP tools with local caching to reduce token usage.

Enables LLMs to send HTTP requests to a local development server, allowing interaction with APIs running on localhost.

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

An MCP server that provides tools for exploring large OpenAPI schemas without loading entire schemas into LLM context. Perfect for discovering and analyzing endpoints, data models, and API structure efficiently.

An MCP server that connects to a Swagger specification and helps an AI to build all the required models to generate a MCP server for that service.

Local-first backend developer workspace exposing API debugging, SSH, database, workspace, and diagnostics tools to AI agents through a local MCP server

Enables AI agents to create, manage, and execute API collections, requests, and environments in Insomnia-compatible formats. It supports direct synchronization with the local Insomnia app database and importing from OpenAPI, Postman, and cURL.

Enables interaction with Google Ads through the Google Ads API using GCloud authentication. Supports GAQL query execution, performance data retrieval, account management, and resource discovery with pagination and multiple output formats.

Automatically converts Swagger 2.0 API specifications into MCP tools, enabling AI assistants to directly call REST APIs. Supports both local Swagger files and remote URLs with comprehensive parameter validation and error handling.

An MCP server that allows users to query and retrieve request and response JSON schemas directly from Swagger/OpenAPI specifications. It supports automatic reference resolution and path parameter matching to help AI models interact with API interfaces.

Enables running Postman collections using Newman for conducting API tests and obtaining detailed result analysis via a standardized interface.

A standalone Model Context Protocol server that enables AI models to access the Australian Pharmaceutical Benefits Scheme (PBS) API data, including medicine listings, pricing, and availability information.

A Model Context Protocol server for executing GraphQL queries, allowing AI models to interact with GraphQL APIs through introspection and query execution.