Open Source Python Model Context Protocol (MCP) Servers
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Python Model Context Protocol (MCP) Servers
View 13511 business solutionsBrowse free open source Python Model Context Protocol (MCP) Servers and projects below. Use the toggles on the left to filter open source Python Model Context Protocol (MCP) Servers by OS, license, language, programming language, and project status.
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AWS MCP Servers
Helping you get the most out of AWS, wherever you use MCP
AWS MCP Servers are a collection of remotely hosted, fully-managed Model Context Protocol (MCP) servers by AWS, providing AI applications with real-time access to AWS documentation, API references, best practices, and infrastructure-management capabilities via natural-language workflows. An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Q Developer, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models. -
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XHS-Downloader
GUI/CLI tool for downloading Xiaohongshu
XHS-Downloader is a GUI/CLI tool for downloading Xiaohongshu (Little Red Book) content without watermarks, supporting both graphics and video posts. Prebuilt packages for Windows and macOS are available from Releases and GitHub Actions artifacts, so most users can run it by unzipping and launching the included executable. The project offers two execution paths—run the compiled app or run from source—and documents default download and configuration paths to simplify first use. Recent releases add format support like JPEG and HEIC, clipboard-listening mode improvements, author-based archiving, SOCKS/HTTP proxy options, and the ability to set the file’s modification time to the post’s publish time for cleaner library organization. There is an active issues/discussions area with community tips, including approaches that use Selenium to acquire cookies and user agents for more reliable downloads. -
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HexStrike AI MCP Agents
HexStrike AI MCP Agents is an advanced MCP server
HexStrike AI is an MCP server that lets LLM agents autonomously operate a large catalog of offensive-security tools. Its goal is to bridge “language models” and practical pentest workflows—enumeration, exploitation, vulnerability discovery, and bug bounty reconnaissance—under safe, auditable controls. The server exposes typed tools and guardrails so agent prompts translate to concrete, parameterized actions rather than brittle shell strings. It ships with curated tool adapters, task orchestration, and guidance for connecting popular agent clients (Claude, GPT, Copilot) to a hardened execution environment. Documentation highlights the breadth of supported utilities and positions HexStrike as a research and red-team aid, not a point-and-click exploit kit. A public site and active repository activity signal an expanding community around autonomous security research agents. -
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Lemonade
Lemonade helps users run local LLMs with the highest performance
Lemonade is a local LLM runtime that aims to deliver the highest possible performance on your own hardware by auto-configuring state-of-the-art inference engines for both NPUs and GPUs. The project positions itself as a “local LLM server” you can run on laptops and workstations, abstracting away backend differences while giving you a single place to serve and manage models. Its README emphasizes real-world adoption across startups, research groups, and large companies, signaling a focus on practical deployments rather than toy demos. The repository highlights easy onboarding with downloads, docs, and a Discord for support, suggesting an active user community. Messaging centers on squeezing maximum throughput/latency from modern accelerators without users having to hand-tune kernels or flags. Releases further reinforce the “server” framing, pointing developers toward a service that can be integrated into apps and tools. -
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IDA Pro MCP
MCP Server for IDA Pro
The IDA Pro MCP Server is a Model Context Protocol (MCP) server designed to integrate with IDA Pro, a popular disassembler and debugger. It enables AI assistants to interact with IDA Pro, facilitating tasks such as code analysis and reverse engineering. -
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MarkItDown
Python tool for converting files and office documents to Markdown
MarkItDown is a lightweight Python utility developed by Microsoft for converting various files and office documents to Markdown format. It is particularly useful for preparing documents for use with large language models and related text analysis pipelines. -
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ArXiv MCP Server
A Model Context Protocol server for searching and analyzing arXiv
arxiv-mcp-server bridges AI assistants and the arXiv repository through a clean MCP interface, enabling search, metadata retrieval, and content access without bespoke scraping. With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and improving markdown conversion, reflecting active community use in research flows. It’s designed to be drop-in for MCP clients, giving them typed inputs/outputs and predictable errors around a well-known academic corpus. For developers building research copilots, it removes the glue work of wiring arXiv APIs into an agent toolchain. -
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Binary Ninja MCP
A Binary Ninja plugin, MCP server
The Binary Ninja MCP is a plugin and bridge that integrates Binary Ninja with Large Language Model clients via the Model Context Protocol, enhancing reverse engineering workflows with AI assistance. -
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ContextForge MCP Gateway
A Model Context Protocol (MCP) Gateway & Registry
MCP Context Forge is a feature-rich gateway and registry that federates Model Context Protocol (MCP) servers and traditional REST services behind a single, governed endpoint. It exposes an MCP-compliant interface to clients while handling discovery, authentication, rate limiting, retries, and observability on the server side. The gateway scales horizontally, supports multi-cluster deployments on Kubernetes, and uses Redis for federation and caching across instances. Operators can define virtual servers, wire multiple transports, and optionally enable an admin UI for management and monitoring. Packaged for quick starts via PyPI and Docker, it targets production reliability with health checks, metrics, and structured logs. The project positions itself as an integration hub so agentic apps can “connect once, use many” backends with consistent policy and lifecycle control. -
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Serena
Agent toolkit providing semantic retrieval and editing capabilities
Serena is a coding-focused agent toolkit that turns an LLM into a practical software-engineering agent with semantic retrieval and editing over real repositories. It operates as an MCP server (and other integrations), exposing IDE-like tools so agents can locate symbols, reason about code structure, make targeted edits, and validate changes. The toolkit is LLM-agnostic and framework-agnostic, positioning itself as a drop-in capability for different chat UIs, orchestrators, or custom agent stacks. It emphasizes symbol-level understanding rather than naive file-wide diffs, enabling more precise refactors and additions. The repository and ecosystem materials highlight rapid setup, agent interoperability, and examples that show agents iterating on a codebase with guardrails. It’s actively maintained by Oraios, with recent updates, community showcases, and third-party write-ups underscoring interest from the agent tooling community. -
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Upsonic
The most reliable AI agent framework that supports MCP
Upsonic is a reliability-focused AI agent framework designed for real-world applications. It enables the development of trusted agent workflows within organizations by incorporating advanced reliability features, such as verification layers and output evaluation systems. The framework supports the Model Context Protocol (MCP), facilitating integration with various tools and enhancing agent capabilities. -
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firerpa LAMDA
The most powerful Android RPA agent framework
lamda is an Android RPA agent framework that provides visual remote desktop control and automation at scale, geared toward testing, automation validation, and device management. It exposes a clean UI to monitor and interact with connected devices and includes tooling to script actions reliably across apps and OS versions. The project emphasizes low-friction setup and powerful control primitives so teams can move from interactive validation to repeatable automation. A public wiki, releases, and issue tracker show active development across areas like connectivity, instrumentation compatibility, and robustness under detection. Together with companion projects (e.g., a device hub), lamda is positioned as a next-generation mobile automation stack rather than a single tool. Its focus on remote control plus RPA primitives makes it useful for QA, operations, and large-scale device orchestration. -
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Browser Use MCP Server
Browse the web, directly from Cursor etc.
A browser automation server implementing the Model Context Protocol, designed to allow AI assistants to browse the web directly from applications like Cursor. It supports natural language commands for web navigation and interaction. -
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Claude-Flow
The leading agent orchestration platform for Claude
Claude-Flow v2 Alpha is an advanced AI orchestration and automation framework designed for enterprise-grade, large-scale AI-driven development. It enables developers to coordinate multiple specialized AI agents in real time through a hive-mind architecture, combining swarm intelligence, neural reasoning, and a powerful set of 87 Modular Control Protocol (MCP) tools. The platform supports both quick swarm tasks and persistent multi-agent sessions known as hives, facilitating distributed AI collaboration with persistent contextual memory. At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
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Colab-MCP
An MCP server for interacting with Google Colab
Colab-MCP is an open-source Model Context Protocol server developed by Google that enables AI agents to directly interact with and control Google Colab environments programmatically, transforming Colab into a fully automated, agent-accessible workspace. Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution. -
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Zettelkasten MCP
Implements the Zettelkasten knowledge management methodology
The Zettelkasten MCP Server is a Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology. It allows users to create, link, and manage notes, facilitating a structured and interconnected note-taking system. -
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Chroma MCP
A Model Context Protocol (MCP) server implementation
Chroma MCP Server is an implementation of the Model Context Protocol (MCP) designed to integrate large language model (LLM) applications with external data sources or tools. It offers a standardized framework to seamlessly provide LLMs with the context they require for effective operation. -
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Excel MCP Server
A Model Context Protocol server for Excel file manipulation
The Excel MCP Server is a Python-based implementation of the Model Context Protocol that provides Excel file manipulation capabilities without requiring Microsoft Excel installation. It enables workbook creation, data manipulation, formatting, and advanced Excel features. -
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Fantasy PL MCP
Fantasy Premier League MCP Server
Fantasy Premier League MCP Server is a Model Context Protocol (MCP) server that provides access to Fantasy Premier League (FPL) data and tools. It allows interaction with FPL data in MCP-compatible clients, enabling users to manage their fantasy teams effectively. -
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FastAPI-MCP
Expose your FastAPI endpoints as Model Context Protocol (MCP) tools
fastapi_mcp lets you expose existing FastAPI endpoints as Model Context Protocol (MCP) tools with minimal setup, so AI agents can call your app as first-class tools. Rather than acting as a thin converter, it’s built as a native FastAPI extension that understands dependency injection, so you can reuse Depends() for authentication and authorization across your MCP tools. The server speaks directly to your app over its ASGI interface, avoiding extra HTTP hops between the MCP layer and your API, which reduces latency and simplifies deployment. A tiny bootstrap is enough to stand up an MCP server and, if desired, mount an HTTP transport for remote clients. The docs emphasize a FastAPI-first workflow: keep your schemas, reuse your middleware, and surface endpoints to agents without rewriting controllers. The project is active, with examples and a dedicated site that shows getting started, security, and transport options. -
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FastMCP
The fast, Pythonic way to build Model Context Protocol servers
FastMCP is a fast, Pythonic framework for building servers and clients using the Model Context Protocol (MCP). It abstracts away protocol complexity like serialization, validation, and error handling, letting developers focus entirely on their business logic. With simple decorators, you can expose Python functions as tools, resources, or prompts that AI agents can safely and efficiently use. FastMCP introduces clear abstractions—components, providers, and transforms—that make it easy to control what agents see and how they interact with your system. The framework is opinionated by design, ensuring best practices and protocol compliance are the default rather than an extra burden. Actively maintained and widely adopted, FastMCP powers a majority of MCP servers and has become the de facto standard for production-ready MCP applications. -
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K8s MCP Server
K8s-mcp-server is a Model Context Protocol (MCP) server
An MCP server that enables AI assistants like Claude to securely execute Kubernetes commands, providing a bridge between language models and essential Kubernetes CLI tools for cluster management and deployments. -
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MCP Server DuckDB
A Model Context Protocol (MCP) server implementation for DuckDB
An MCP server implementation for DuckDB, providing database interaction capabilities through MCP tools, allowing operations like querying, table creation, and schema inspection. -
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Nerve
The Simple Agent Development Kit
Nerve is a developer-friendly Agent Development Kit (ADK) that utilizes YAML and a CLI to define, run, orchestrate, and evaluate LLM-driven agents. It supports declarative setups, tool integration, workflow pipelines, and both MCP client and server roles. Nerve is a simple yet powerful Agent Development Kit (ADK) to build, run, evaluate, and orchestrate LLM-based agents using just YAML and a CLI. It’s designed for technical users who want programmable, auditable, and reproducible automation using large language models. Define agents using a clean YAML format: system prompt, task, tools, and variables — all in one file. -
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UltraRAG
Less Code, Lower Barrier, Faster Deployment
UltraRAG 2.0 is a low-code, MCP-enabled RAG framework that aims to lower the barrier to building complex retrieval pipelines for research and production. It provides end-to-end recipes—from encoding and indexing corpora to deploying retrievers and LLMs—so users can reproduce baselines and iterate rapidly. The toolkit comes with built-in support for popular RAG datasets, large corpora, and canonical baselines, plus documentation that walks from “quick start” to debugging and case analysis. It encourages pipeline composition via configuration, enabling researchers to swap retrievers, rerankers, and generators without heavy refactoring. Community posts highlight its focus on reducing engineering overhead so more effort goes to experimental design. Backed by the OpenBMB org, it is actively maintained with tutorials and updates.
