MCP servers that give coding agents real developer tooling: linters, package registries, test runners, language docs and local build commands. 13.985 servers in dieser Kategorie.
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Converts raw crash output from multiple languages into compact root-cause reports and maps them to your local source code.
Converts pasted crash output into root-cause reports by parsing tracebacks, chained exceptions, and multi-language stack frames.
Allows an AI client to audit GitHub Actions workflows for YAML pitfalls, security injections, permission issues, and cron syntax errors.
Provides ultra-low-latency Rust-based guardrails and context compression for AI coding agents like Claude Code and Cursor.
Lints MCP servers against agent-friendly design principles to ensure tools are easy for AI agents to use and interact with.
Indexes local code repositories by symbol to provide precise context spans for LLMs using a hybrid lexical and embedding retrieval engine.
Audits World of Warcraft addons for performance, usability, API existence, and security issues to assist AI coding agents.
Provides local-first memory and offline output verification for AI agents to prevent forgetting and hallucination.
Exposes command-line applications as structured MCP tools with automatic command discovery and argument validation.
Provides a production-ready template for building Model Context Protocol servers in Python with built-in testing, logging, and deployment configurations.
Provides shared memory for AI agents by syncing Claude Code, Codex, Obsidian, and GitHub history into a searchable local SQLite database via MCP.
Provides a local dashboard to browse, test, and configure MCP servers across various AI coding assistants like Claude, Cursor, and VS Code.
Enables AI coding agents to read, create, edit, run, and test NI LabVIEW VIs via ActiveX server.
Generates interactive architecture diagrams from codebases using static analysis and local or cloud LLMs.
Enables AI coding agents to call your phone via voice to request approvals, report task completion, or receive new spoken commands.
Lets an AI operate a CAD viewer to zoom, pan, click, toggle layers, and measure DXF, DWG, and Gerber files.
Analyzes codebases and git history to provide impact analysis, dependency mapping, and ownership information for developers and AI agents.
Enables AI agents to parse and consume diverse file formats including PDF, Office, images, video, databases, and archives into structured text or JSON.
Allows an AI agent to remotely control a Linux machine via a custom TCP protocol for executing commands, managing files, and monitoring processes.
Converts browser HAR recordings into OpenAPI 3.1 specifications, typed Python clients, and standalone MCP servers for AI agents.
