Open Source MCP Servers
Model Context Protocol (MCP) servers connect AI assistants and agents to external tools, data sources, and services through a shared interface. They can expose capabilities such as searching files, querying databases, accessing web content, or controlling applications. By standardizing how these connections are described and used, MCP servers reduce the need for custom integrations and help assistants work with information beyond their built-in knowledge.
53 repositories · updated October 4, 2026
ksail: Create and Operate Kubernetes Clusters
KSail is a Go-based toolkit for creating and operating Kubernetes clusters across local, nested, and cloud providers. It brings provisioning, GitOps, secrets, cluster operations, and AI interfaces into one tool, for developers and platform teams who want a unified workflow.
meshtastic-mcp: Connect AI Agents to Meshtastic Devices
A Python MCP server and agent skills for discovering, operating, observing, and testing Meshtastic devices and apps. Its portable core works over serial or TCP, with optional capabilities for firmware builds, mobile-app testing, replay, and hardware benches.
browser-rs-mcp: Share Chrome with AI Agents
A Rust MCP server that lets multiple AI agents control separate tabs in one shared Chrome profile. It provides browser automation through Chrome DevTools Protocol without requiring a Node.js runtime.
dcc-mcp-blender: Control Blender with AI Agents
An embedded MCP server that lets AI agents operate Blender through typed tools for modeling, UVs, rigging, materials, animation, and rendering. Suited to creators and pipeline teams seeking structured, cross-DCC automation.
open-index: Build Searchable Context Graphs for AI Agents
Open Index is a Python tool for building structured, searchable domain knowledge that agents can retrieve and maintain. It suits teams that need validated context across files, connectors, and MCP-enabled agents.
ferret-mcp: Analyze Codebases for Architecture and Patterns
Ferret MCP gives AI assistants static and LLM-powered insight into a codebase, including its architecture, dependencies, conventions, and interfaces. It suits developers exploring unfamiliar repositories or preparing focused technical analysis.
agenticschema: Turn Structured Web Data Into Agent Tools
AgenticSchema converts Schema.org data already present in web pages into tools for AI agents. It supports browser WebMCP registration and a Node MCP server, without requiring a new backend or page API.
cli: Connect AI Agents to Models and APIs with MCP
SandBase CLI connects supported AI clients to a catalog of AI models and APIs through a local MCP bridge. It is aimed at developers who want one integration for discovery and execution instead of configuring individual services in each client.
deja-vu: Search Coding Agent Session History
deja-vu indexes existing coding-agent conversations into a shared, local search tool. It helps developers recover past decisions, fixes, and commands across supported agents without requiring an LLM or embedding service.
flint-chart: Create Polished Charts from Compact Specs
Flint turns semantic, human-editable chart specifications into polished visualizations for several charting backends. It is aimed at developers and AI-agent builders who want consistent chart creation without manually configuring every layout detail.
codex-mcp-server: Connect Claude Code to Codex CLI
This TypeScript MCP server lets Claude Code call OpenAI Codex CLI for code analysis, generation, and review. It suits developers who want to use Codex alongside Claude in an MCP-compatible editor.
metamcp: Combine MCP Servers Behind One Gateway
MetaMCP combines configured MCP servers into unified endpoints and adds controls for authentication, namespaces, and middleware. It is aimed at teams and developers who want to host and manage reusable MCP tool collections.