MCP servers that give coding agents real developer tooling: linters, package registries, test runners, language docs and local build commands. 13,967 servers in this category.
Provides AI agents with persistent semantic memory and a hierarchical knowledge base through a unified MCP gateway.
Bridges AI assistants to the Gitee ecosystem with live activity tracking, repository intelligence, cross-lingual search, and Chinese-to-English translation.
Delegates coding loops from AI editors to the local Grok CLI within an isolated git worktree and returns a verified receipt of changes and test results.
Expose Streamlit applications as MCP servers, allowing AI agents to interact with widgets, read session state, and drive apps without browser automation.
Composes modular robot control components from TOML and compiles them into C-ABI kernels for embedded deployment.
Spawns isolated MCP servers from Python tools or OpenWebUI exports using individual virtual environments and Streamable HTTP.
Provides a searchable catalog of MCP servers with structured metadata, tools, resources, and installation configurations for AI agents.
Enables AI clients to author, validate, and convert Sigma detection rules for various security backends using a published rule corpus.
Converts JSON files and datasets into SQL INSERT statements for PostgreSQL, MySQL, and SQLite.
Benchmark local LLM inference speed and serve optimized quantized models to AI clients via MCP.
Automates local-first AI code reviews by driving existing coding agents to analyze staged, unstaged, or GitHub pull request changes.
Provides semantic search for codebases and architecture decision records using local GPU-accelerated hybrid retrieval.
Enforces a structured research, decision, and planning workflow for coding agents using Markdown records in your repository.
Transforms local hardware into an OpenAI-compatible LLM provider that routes requests to local models, remote GPUs, or cloud APIs through a single gateway.
Provides an inspection and organization layer for Claude Code to discover, inspect, and summarize MCP server tools locally.
Validates and lints Tonel formatted Smalltalk source code and method definitions.
Audit MCP server configurations to identify security risks like literal secrets, unpinned packages, and broad filesystem access.
Provides a unified memory surface combining semantic facts and a code knowledge graph to enable cross-linked recall between facts and code symbols.
Provides a reference implementation for building production-grade Python MCP servers with tool registries, multi-workspace routing, and safety gates.
Lets an AI client browse TestRail projects and add test results or file attachments without the ability to delete or modify existing data.