Open Source Python Model Context Protocol (MCP) Servers
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Python Model Context Protocol (MCP) Servers
View 13619 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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Simplify Purchasing For Your BusinessSimplify every aspect of buying for your business in Order.co. From sourcing products to scaling purchasing across locations to automating your AP and approvals workstreams, Order.co is the platform of choice for growing businesses.
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MicroStation by Bentley Systems is the trusted computer-aided design (CAD) software built specifically for infrastructure design.MicroStation is the only computer-aided design software for infrastructure design, helping architects and engineers like you bring their vision to life, present their designs to their clients, and deliver their projects to the community.
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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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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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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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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. -
Turn traffic into pipeline and prospects into customersDocket is an AI-powered sales enablement platform designed to unify go-to-market (GTM) data through its proprietary Sales Knowledge Lake™ and activate it with intelligent AI agents. The platform helps marketing teams increase pipeline generation by 15% by engaging website visitors in human-like conversations and qualifying leads. For sales teams, Docket improves seller efficiency by 33% by providing instant product knowledge, retrieving collateral, and creating personalized documents. Built for GTM teams, Docket integrates with over 100 tools across the revenue tech stack and offers enterprise-grade security with SOC 2 Type II, GDPR, and ISO 27001 compliance. Customers report improved win rates, shorter sales cycles, and dramatically reduced response times. Docket’s scalable, accurate, and fast AI agents deliver reliable answers with confidence scores, empowering teams to close deals faster.
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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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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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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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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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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. -
Award-Winning Medical Office Software Designed for Your SpecialtyRXNT is an ambulatory healthcare technology pioneer that empowers medical practices and healthcare organizations to succeed and scale through innovative, data-backed, AI-powered software.
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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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MCP Server Qdrant
An official Qdrant Model Context Protocol (MCP) server implementation
The Qdrant MCP Server is an official Model Context Protocol server that integrates with the Qdrant vector search engine. It acts as a semantic memory layer, allowing for the storage and retrieval of vector-based data, enhancing the capabilities of AI applications requiring semantic search functionalities. -
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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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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. -
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MCP Atlassian
MCP server that integrates Confluence and Jira
The MCP Atlassian server integrates Atlassian products like Confluence and Jira with the Model Context Protocol. It supports both Cloud and Server/Data Center deployments, enabling AI models to interact with these platforms securely. -
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MCP Teams Server
An MCP (Model Context Protocol) server implementation
An MCP server implementation for Microsoft Teams integration, providing capabilities to read messages, create messages, reply to messages, and mention members, facilitating AI-driven interactions within Teams. -
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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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Elasticsearch MCP Server
A Model Context Protocol (MCP) server implementation
This MCP server implementation provides interaction capabilities with Elasticsearch and OpenSearch, enabling functionalities such as document searching, index analysis, and cluster management through a set of tools. -
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MCP Agent
Build effective agents using Model Context Protocol
The MCP Agent is a framework that enables the construction of effective AI agents using the Model Context Protocol. It focuses on simple, composable patterns to build production-ready AI agents, facilitating seamless integration with various tools and services to enhance AI capabilities. -
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MaxKB
Open-source platform for building enterprise-grade agents
MaxKB (Max Knowledge Brain) is an open-source platform for building enterprise-grade AI agents with strong knowledge retrieval, RAG pipelines, and workflow orchestration. It focuses on practical deployments such as customer support, internal knowledge bases, research assistants, and education, bundling tools for data ingestion, chunking, embedding, retrieval, and answer synthesis. The system exposes flexible tool-use (including MCP), supports multi-model backends, and provides dashboards for dataset management and evaluation. It’s backed by an active org that also builds adjacent ops tooling, and there’s a dedicated documentation repo for configuration and contribution. Community posts describe “self-host your ChatGPT-style assistant” positioning, with integrations and workflows to move from demo to production. Security advisories are tracked publicly, with upgrade guidance when issues arise. -
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MindsDB
Making Enterprise Data Intelligent and Responsive for AI
MindsDB is an AI data solution that enables humans, AI, agents, and applications to query data in natural language and SQL, and get highly accurate answers across disparate data sources and types. MindsDB connects to diverse data sources and applications, and unifies petabyte-scale structured and unstructured data. Powered by an industry-first cognitive engine that can operate anywhere (on-prem, VPC, serverless), it empowers both humans and AI with highly informed decision-making capabilities. A federated query engine that tidies up your data-sprawl chaos while meticulously answering every single question you throw at it. MindsDB has an MCP server built in that enables your MCP applications to connect, unify and respond to questions over large-scale federated data—spanning databases, data warehouses, and SaaS applications. -
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Supabase MCP Server
Query MCP enables end-to-end management of Supabase via chat interface
An open-source MCP server that enables comprehensive management of Supabase projects through natural language interactions, providing capabilities such as SQL execution, schema management, and API integration. -
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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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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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Xiyan MCP Server
A Model Context Protocol (MCP) server
The XiYan MCP Server is a Model Context Protocol (MCP) server that enables natural language queries to databases, powered by XiYan-SQL, a state-of-the-art text-to-SQL model. It allows users to interact with databases using conversational language, simplifying data retrieval processes. -
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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.
