The TypeScript framework for MCP servers: typed tools with Zod, dependency injection, auth, testing, agents and jobs, and builds for Node, Vercel, AWS Lambda and Cloudflare Workers.

🦀⚡ Community skill pack & CLI for Genspark Claw, OpenClaw & Hermes agents — 5 production-ready SKILL.md skills (deep research, reports, slides, browser automation, AI call prep), zero-dependency Node.js installer, MCP server, one-line install

The ripgrep of AI context: a zero-dependency C++23 CLI + MCP server for coding agents. Find what you want without reading the repo, then check you built what you meant — blast radius, tests-to-run, quality deltas. Signatures at 74.7% fewer bytes than bodies; every guess labelled, every loss published. Paddle out with a map.

Multi-language repository map for agents and humans: MCP tools, dependency graphs, symbol indexes, impact review, and bounded source evidence.

Diff two npm/PyPI versions or lockfiles: new install scripts, network/shell/eval calls, new deps.

Deterministic, persistent graph server for tracking workflow state, decisions, and blockers.

Framework-aware code intelligence MCP server — 91 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity

Open source (AGPL-3.0) evidence-grounded repository audit CLI - deterministic scanner, MCP server, live dashboard, and a GitHub Action that posts PR diffs.

Simple, modular, and observable Go framework for backend applications.

Cross platform Node.js Utility to open urls in browser

One zero-dependency CLI for every MCP server and agent skill. Token optimization, tool discovery and context compression: 71,929 -> 581 tokens (-99.2%, measured), schemas stay out of context. One config for Claude Code, Codex, Cursor, every agent. 341KB, pure Python. | 零依赖 CLI：管所有 MCP 工具与技能，工具发现省 99.2% token。

Enables AI coding agents to extract structural C# contracts from files and build solution-wide dependency graphs, so they can navigate code and assess change impact without reading entire files.

Gives AI agents one MCP interface to inspect CI health and flaky tests, pull request flow, delivery/DORA metrics, dependency risks, community health, insights, and org portal data across GitHub repositories. It exposes 11 core tools by default and all 35 with --tools all.

Virtual keyboard and gamepad for AI agents on Linux / Steam Deck — zero-dependency uinput daemons with an MCP server interface.

Enables AI IDEs and assistants to perform AST analysis, map dependency graphs and call chains, run semantic code search, and optionally sync cloud-backed memory — all locally so source code never leaves the machine.

Enables an AI assistant to inspect project dependency and runtime files, determine each component's support status and end dates, and rank upgrade urgency using bundled endoflife.date data.

Enables coding agents to get exact go-to-definition, find-references, hover, diagnostics, and file outline answers from existing language servers instead of grep. It is read-only and dependency-free.

A CLI framework built on one declaration: help, --json, --schema, MCP, completions and types are projected from it, with exit codes and fix: lines an agent can act on. No dependency outside the burgee family. Drop-in paths for commander, yargs and meow.

Enables an LLM to triage hard-to-extract Vue components by feeding it structured coupling analysis, dependency graphs, and design tokens, then generating decoupling suggestions with before/after code, composable wrappers for store-bound components, and full rewrite-and-extract operations.

Enables agents to validate enterprise travel policies, audit hotel rate parity, and check carbon ESG compliance through a zero-dependency MCP server. It exposes policy and expense-auditing tools over STDIO JSON-RPC for use in Claude Desktop, Cursor, or other MCP runtimes.

Enables agents to forecast dynamic airline ticket fare volatility and produce optimal buy-now versus wait-and-freeze recommendations through a zero-dependency MCP STDIO interface.

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Enables building zero-dependency Model Context Protocol servers over the Streamable HTTP transport, exposing typed tools, resources and URI templates, prompts, background tasks, and OAuth-protected endpoints. It can be mounted into FastAPI, Starlette, Flask, Django or any ASGI/WSGI app, or run standalone on the Python standard library alone.