Open Source Model Context Protocol (MCP) Servers - Page 4
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Model Context Protocol (MCP) Servers
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Laravel Boost
Laravel-focused MCP server for augmenting AI powered local development
Boost is a Laravel-first toolkit that supercharges AI-assisted development by giving assistants structured, Laravel-aware context. At its core it runs as an MCP server that exposes a battery of Laravel-specific tools, so an AI agent can explore your app, inspect code and config, and take targeted actions instead of guessing. It ships opinionated, composable guidelines tuned for popular Laravel packages, which helps keep generated code idiomatic and consistent with framework norms. The package also curates a large body of vectorized Laravel ecosystem knowledge that’s scoped to what you’ve actually installed, improving retrieval precision and response quality. It’s designed to fit naturally into existing projects, supporting current Laravel releases and modern PHP runtimes with minimal setup. Rather than trying to replace your editor or framework, Boost acts like an intelligent layer that understands Laravel’s conventions and reduces the “explain my app to the AI” friction. -
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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 Filesystem Server
Go server implementing Model Context Protocol (MCP) for filesystem
Filesystem MCP Server is a Go-based server implementing the Model Context Protocol (MCP) for filesystem operations. It allows for various file and directory manipulations, including reading, writing, moving, and searching files, as well as retrieving file metadata. -
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MCP HFSpace
MCP Server to Use HuggingFace spaces, easy configuration
The MCP-HFSpace server is an MCP server designed to connect AI assistants with Hugging Face Spaces. It simplifies the configuration process and is compatible with Claude Desktop mode, allowing for seamless integration and utilization of Hugging Face's machine learning models and applications. -
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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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Unla
Gateway service that instantly transforms existing MCP Servers
Unla is a lightweight, highly available MCP gateway written in Go that turns existing MCP servers or ordinary HTTP APIs into MCP-compliant services through configuration, not code changes. Its goal is to let teams “wire up” tools they already run—internal REST endpoints, third-party APIs, or local MCP servers—and present a single, reliable MCP interface to clients like Claude Desktop, Cursor, and IDEs. The gateway focuses on operational concerns you’d expect in production: multi-instance availability, health checking, and declarative routing that maps upstreams to MCP tools and resources. A quick-start and CLI make it easy to stand up an API server, while the package structure exposes helpers for people who want to embed or extend the gateway. Because it is itself MCP-speaking, Unla can sit in front of mixed fleets and normalize transports and schemas for clients. Documentation and pkg.go.dev pages reinforce the positioning as a stable, Go-native building block for MCP deployments. -
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kMCP
Kubernetes Controller for building, testing and deploying MCP servers
KMCP is a companion toolchain for building, testing, and deploying MCP servers with a workflow that spans local development through Kubernetes production deployments. It includes a CLI for day-to-day development tasks like scaffolding new MCP projects, managing tools, building container images, and running an MCP server locally for validation. For cluster operations, it includes a Kubernetes controller that manages MCP server lifecycles using a dedicated Custom Resource Definition (CRD), allowing MCP servers to be represented as native Kubernetes objects you can operate with familiar kubectl-driven patterns. A key component is the transport adapter, which fronts MCP servers to provide routing and multi-transport support without requiring code changes in your server implementation. The project is geared toward consistency, aiming to reduce the “glue work” of writing Dockerfiles, hand-rolling manifests, and manually wiring networking and deployment details for each MCP server. -
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mcpo
A simple, secure MCP-to-OpenAPI proxy server
mcpo is a minimal bridge that exposes any MCP tool as an OpenAPI-compatible HTTP server. Instead of writing glue code, you point mcpo at an MCP server command and it generates REST endpoints and an OpenAPI spec that other systems (or LLM agent frameworks) can call immediately. This design lets you reuse a growing library of MCP servers with platforms that only understand HTTP+OpenAPI, unifying tool access across ecosystems. The project emphasizes “dead-simple” setup and pairs with Open WebUI documentation that shows end-to-end integration. It supports running multiple tools and makes them discoverable to clients that expect Swagger/JSON schemas. In practice, mcpo shortens the path from a local MCP tool to a shareable, network-accessible microservice. -
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ADX MCP Server
A Model Context Protocol (MCP) server that enables AI assistants
The Azure Data Explorer MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to query and analyze Azure Data Explorer databases through standardized interfaces. It allows the execution of Kusto Query Language (KQL) queries and exploration of data within Azure Data Explorer clusters. -
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Azure CLI MCP
Talk with Azure using MCP
An MCP server that enables AI assistants to interact with Microsoft Azure via the Azure CLI, allowing tasks such as listing resources, checking configurations, fixing issues, and creating new resources through conversational commands. -
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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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Box MCP Server
A Box model context protocol server to search, read and access files
Box MCP Server is a Model Context Protocol (MCP) server designed to facilitate searching, reading, and accessing files stored on the Box cloud storage platform. It enables seamless integration with Box, allowing for efficient file management and retrieval through MCP-compatible clients. -
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BrowserTools MCP
Monitor browser logs directly from Cursor
Browser Tools MCP is an MCP server and Chrome extension that gives AI agents safe, structured access to your live browser for debugging and automation. It can capture console/network logs, DOM snapshots, and screenshots, and expose them as typed resources the agent can query or act on. The design aims to make IDE agents (e.g., Cursor, Claude Desktop) more “web-aware,” enabling workflows like reproducing a bug, collecting evidence, and proposing fixes without copy-pasting. Documentation and community guides outline a quick setup, including the extension, the MCP server process, and common troubleshooting steps. The project is actively maintained, with public commit activity and discussions around connectivity and reliability in different IDEs. Overall, it bridges the gap between local coding agents and the real browser they need to observe and manipulate. -
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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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CodeMirror MCP
CodeMirror extension to hook up a Model Context Provider (MCP)
The codemirror-mcp project is a CodeMirror extension that integrates the Model Context Protocol (MCP) into the CodeMirror editor. This extension enhances the editor's capabilities by providing features such as autocompletion for resource mentions and prompt commands, as well as visual styling for these elements. It aims to streamline the user experience when working with MCP within the CodeMirror environment. -
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Contentful MCP
MCP (Model Context Protocol) server for the Contentful Management API
The Contentful MCP Server is an MCP server implementation that integrates with Contentful's Content Management API, providing comprehensive content management capabilities. It allows AI assistants to interact with Contentful, facilitating tasks such as content retrieval and management. -
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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 K8s Eye
MCP Server for kubernetes management and analyze workload status
A tool designed to manage Kubernetes clusters and analyze workload statuses, providing insights and operational capabilities to enhance cluster performance and reliability. -
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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 Language Server
mcp-language-server gives MCP enabled clients access semantic tools
mcp-language-server gives MCP-enabled clients semantic code-navigation powers—go-to-definition, find references, rename, and diagnostics—by brokering requests to language servers. It is not “a language server for MCP,” but an MCP server that exposes language-server–style capabilities to agents and chat IDEs through typed tools. The README demonstrates a streamlined setup: install the Go server, plug in one or more language servers per language, and the MCP client gains editor-grade navigation across the workspace. This helps agents reason precisely about symbols and files instead of guessing via grep-like prompts, enabling safer edits and better refactoring proposals. The project maintains active issues and PRs, indicating ongoing polish around multi-language routing and robustness. It’s also listed in community indexes, reflecting adoption across MCP clients. -
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MCP Linear
MCP server that enables AI assistants to interact with Linear project
The MCP Linear server is a Model Context Protocol (MCP) implementation that enables AI assistants to interact with the Linear project management system through natural language. It allows users to retrieve, create, and update issues, projects, and teams within Linear, facilitating seamless integration between AI models and project management workflows. -
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MCP Server Playwright
MCP server for browser automation using Playwright
An MCP (Model Context Protocol) server that leverages Playwright to provide browser automation capabilities, enabling large language models (LLMs) to interact with web pages, take screenshots, and execute JavaScript within a real browser environment. -
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MCP Timeplus
Execute SQL queries and manage databases seamlessly with Timeplus
An MCP server designed for integration with Timeplus, enabling real-time data streaming and analytics through natural language interactions. -
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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.
