Discover our curated collection of MCP servers for api development. Browse 7960servers and find the perfect MCPs for your needs.
Orchestrates automated workflows by connecting AI models with various tools and resources using MCP.
Provides a self-hosted web interface and API for interacting with large language models via llama.cpp.
Automates web interactions for AI agents and applications without managing infrastructure.
A curated list of Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources.
Build AI-powered applications with this open-source framework for Node.js and Go.
Provides an all-in-one, intelligent automation testing solution for API, UI, and performance testing across multiple platforms.
Empowers LLMs with advanced web scraping capabilities for content extraction, crawling, and search functionalities.
Leverage any Large Language Model to generate comprehensive, privacy-focused research reports.
Empowers AI agents with autonomous offensive cybersecurity capabilities by integrating over 70 professional security tools.
Provides an open-source implementation of the ChatGPT Code Interpreter using LangChain for sandboxed Python code execution.
Provides a streamlined Python interface for easily interacting with AI chat models like ChatGPT and GPT-4, emphasizing efficiency and minimal code complexity.
Exposes any MCP tool as an OpenAPI-compatible HTTP server.
Implements a Model Context Protocol (MCP) server for the Notion API, enabling AI-driven interaction with Notion content.
Provides access to large language and vision-language models based on linear attention for various natural language and multimodal tasks.
Enables AI assistants like Claude to perform web searches using the Exa AI Search API.
Generates images, text, and audio from text prompts using a free and open-source API.
Maintains a curated list of Model Context Protocol (MCP) servers.
Enables n8n workflows to interact with Model Context Protocol (MCP) servers for accessing resources, executing tools, and utilizing prompts.
Programmatically assembles prompts for LLMs using JavaScript to orchestrate LLMs, tools, and data in code.
Enables Java applications to interact with AI models and tools through a standardized interface.
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