MCP tool: let you point at DOM elements for your favorite agentic coding tool. Let AI see what you see.

The graph based agentic IDE

Rust MCP server for comprehensive code intelligence - 90 tools, 32 languages, security scanning, call graphs, and more

An MCP server that allows users to dynamically add custom tools/functions at runtime

100% Rust implementation of code graphRAG with blazing fast AST+FastML parsing, surrealDB backend and advanced agentic code analysis tools through MCP for efficient code agent context management

Semantic codebase indexing and search for OpenCode, Claude, Codex, Pi, Jcode, and MCP hosts. Powered by Rust and tree-sitter.

Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.

Define task-specific AI sub-agents in Markdown for any MCP-compatible tool.

Extract domain knowledge from codebases to reduce LLM token consumption by 20x and time in agentic search by 10x — gathers and makes concepts, naming conventions, and vocabulary queryable via MCP.

Save 30% token costs when using Claude Code, Codex, OpenCode for free - with open source, local semantic search. Works for small and large codebases and monorepos! Enterprise-ready and fully compliant via Ollama and SQLite-vec.

Adds MCP server to Blockbench

Roslyn-based MCP server giving AI agents deep semantic understanding of .NET/C# codebases — 67 tools for navigation, call graphs, diagnostics & code fixes, safe refactoring, code-quality auditing, test intelligence, DI graphs, and IL/external-assembly inspection.

Moondream MCP Server in Python

A MCP server for our beloved terminal multiplexer tmux.

MCP for 1C:EDT

An MCP server for interacting with Sentry via LLMs.

Baidu Map MCP Server

The filesystem for AI agents

Natural voice conversations with Claude Code

Model Context Protocol For R

👩‍💻 MCP server to index external repositories

k6 MCP server

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

Let LLMs control embedded devices via the Model Context Protocol.