Open Source Linux Model Context Protocol (MCP) Servers - Page 4
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Model Context Protocol (MCP) Servers for Linux
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Quarkus MCP Server
This extension enables developers to implement the MCP server
The quarkus-mcp-server is a Quarkus extension that enables developers to implement Model Context Protocol (MCP) server features easily. It provides both declarative and programmatic APIs, simplifying the process of integrating MCP functionalities into Quarkus applications. This extension is part of the Quarkiverse, a hub for Quarkus extensions contributed by the 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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Wren Engine
The Semantic Engine for Model Context Protocol(MCP)
Wren Engine is a semantic engine designed to empower Model Context Protocol (MCP) clients and AI agents by providing accurate, contextual, and governed access to business data. It serves as a bridge between large language models (LLMs) and enterprise systems, facilitating seamless integration and interaction. -
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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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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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DBHub
Universal database MCP server connecting to MySQL, PostgreSQL
DBHub is a universal database gateway that implements the MCP server interface so assistants and IDEs can explore and query databases through typed tools. It supports multiple transports—stdio for desktop clients and HTTP for networked scenarios—making it flexible to embed or deploy. Configuration is environment-variable driven, with a DSN and per-engine settings covering Postgres, MySQL, MariaDB, SQL Server, and SQLite. Operational flags include read-only mode, row limits, and even SSH tunneling options for secure access into private networks. A demo mode ships with an in-memory SQLite “employee” dataset so users can try the tools immediately without provisioning a database. The project lives in the Bytebase org alongside database DevSecOps tooling, underscoring a production focus on safe and auditable DB interaction. -
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Deep Research
Use any LLMs (Large Language Models) for Deep Research
Deep Research is a local-first research agent that orchestrates multiple LLMs to generate in-depth reports in minutes. It combines “thinking” and “task” model roles with live internet access to plan, search, read, and synthesize findings into structured outputs. The project emphasizes privacy: processing and storage happen locally, avoiding server-side retention of your queries and notes. A simple web UI lets you enter topics and configure models, while the backend streams progress as sources are fetched and arguments are weighed. It offers MCP server support and SSE APIs, so IDEs and agent clients can drive the same workflow programmatically. The result is a repeatable process for scoping questions, collecting evidence, and producing a concise report with citations and reasoning steps. -
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ENScan Go
ENScan_GO is an enterprise information reconnaissance tool
ENScan_GO is an enterprise information reconnaissance tool focused on Chinese corporate data sources. It aggregates official and third-party APIs to pull records like ICP filings, affiliated/holding companies, apps, mini-programs, and WeChat official accounts, then exports merged results for analysis. The tool targets analysts who need one-click collection and normalized output to reduce manual lookups across registries and platforms. Recent releases added a reworked task model with queueing, resumable searches via cached progress, export format options, and a public API surface for custom keyword strategies. Documentation and issues discuss operational concerns such as rate limits, verification challenges, and use of proxies to reduce bans. The project is maintained under Apache-2.0 and is positioned for both single-shot queries and batch investigations. -
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EVM MCP Server
MCP server that provides LLM with tools for interacting with EVM
EVM MCP Server is a comprehensive Model Context Protocol (MCP) server that provides blockchain services across multiple EVM-compatible networks. It enables AI agents to interact with Ethereum, Optimism, Arbitrum, Base, Polygon, and many other EVM chains through a unified interface. -
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InfluxDB MCP Server
An MCP Server for querying InfluxDB
An MCP server that provides access to InfluxDB instances using the InfluxDB OSS API v2, enabling operations such as data writing, query execution, and database object management. -
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Logfire MCP
The Logfire MCP Server is here
The Logfire MCP Server is a Model Context Protocol server that allows AI applications to access OpenTelemetry traces and metrics sent to Logfire. It enables retrieval and analysis of telemetry data, enhancing debugging and observability workflows. -
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MCP GraphQL
Model Context Protocol server for GraphQL
The MCP-GraphQL server is a Model Context Protocol implementation that enables Large Language Models (LLMs) to interact with GraphQL APIs. It provides schema introspection and query execution capabilities, allowing models to dynamically discover and utilize GraphQL APIs. -
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MCP Monitor
A system monitoring tool that exposes system metrics
The MCP System Monitor is a tool that exposes system metrics via the Model Context Protocol (MCP), allowing Large Language Models (LLMs) to retrieve real-time system information through an MCP-compatible interface. -
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MCP Server Simulator iOS IDB
Enables LLMs to interact with iOS simulators
The MCP Server Simulator for iOS IDB provides a bridge between Large Language Models (LLMs) and iOS simulators, offering comprehensive control through natural language commands. It enables operations such as launching apps, interacting with the UI, and retrieving device information, enhancing the capabilities of AI-driven development tools. -
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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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Context7 Platform
Up-to-date code documentation for LLMs and AI code editors
Context7 is a system that aims to inject fresh, version-specific documentation and code snippets into language model prompts, thereby avoiding reliance on outdated training data or hallucinated APIs. It’s designed to integrate with tools that support the Model Context Protocol (MCP), such as Cursor, Windsurf, and other LLM clients. When a user writes a prompt and appends something like “use context7,” the system detects the libraries or frameworks being asked about, fetches the latest docs/snippets from the source repositories, filters and packages relevant context, and injects them into the LLM’s prompt to guide it toward accurate, up-to-date code. The upstream codebase provides an MCP server implementation, enabling clients to easily interface with the Context7 service over standard channels (HTTP, stdio) and treat it as an external “knowledge tool.” -
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Ghidra MCP
Socket based MCP Server for Ghidra
GhidraMCP is a Ghidra plugin implementing the Model Context Protocol, bridging Ghidra's reverse engineering capabilities with AI assistants to enhance binary analysis tasks. -
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MCP Kibela
MCP server implementation that enables AI assistants
The MCP-Kibela server is a Model Context Protocol server implementation that enables AI assistants to search and reference content stored in Kibela. This integration allows AI models to securely access information within Kibela, enhancing their contextual understanding and response generation. -
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MCP Server Amazon Bedrock
Model Context Procotol(MCP) server for using Amazon Bedrock
The Amazon Bedrock MCP Server is an MCP server that integrates with Amazon Bedrock's Nova Canvas model for AI image generation. It allows users to generate high-quality images from text descriptions using Amazon's AI capabilities. -
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MCP Server Giphy
An implementation of Giphy integration with Model Context Protocol
The MCP Server Giphy is a Model Context Protocol (MCP) server that enables AI models to search, retrieve, and utilize GIFs from the Giphy platform. It facilitates seamless integration of Giphy's vast GIF library into AI applications, enhancing their expressive capabilities. -
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MCP YouTube
A Model-Context Protocol Server for YouTube
The YouTube MCP Server uses yt-dlp to download subtitles from YouTube videos and connects to claude.ai via the Model Context Protocol. It enables AI assistants to summarize YouTube videos by accessing their subtitles. -
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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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PPTAgent
PPTAgent: Generating and Evaluating Presentations
PPTAgent is a research system for generating and evaluating slide decks that goes beyond simple text-to-slides. It follows a two-stage, edit-based workflow: first it analyzes reference presentations to infer slide roles and structure, then it drafts an outline and iteratively performs editing actions to produce new slides. The project includes both the generation agent and an evaluation framework, PPTEval, to score content quality, design, and coherence. The repository highlights the EMNLP 2025 paper and provides links to resources for replication and study. The approach reflects human presentation practice—plan, draft, then refine with edits—yielding more coherent decks than direct one-shot generation. Community interest and stars suggest strong uptake for research and tooling around presentation automation. -
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Replicate Flux MCP
MCP for Replicate Flux Model
The Replicate Flux MCP is an advanced Model Context Protocol server that empowers AI assistants to generate high-quality images and vector graphics. It leverages Black Forest Labs' Flux Schnell model for raster images and Recraft's V3 SVG model for vector graphics via the Replicate API. -
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Simple MCP
A simple TypeScript library for creating MCP servers
simple-mcp is a TypeScript library designed to facilitate the creation of Model Context Protocol (MCP) servers. It offers a straightforward API, enabling developers to set up MCP servers with minimal code. The library emphasizes type safety through full TypeScript integration and incorporates parameter validation using Zod. It fully implements the MCP, ensuring compatibility with MCP clients.
