Enables local, evidence-led analysis of a master recording into inspectable stems, timing, beats, harmony, lyrics, MIDI and sonic evidence, then finds complete-bar loopable sections for Shorts and other short-form video. Results stay traceable through source hashes, manifests, JSON/TSV/NPZ artifacts and portable Sonic Visualiser sessions, and it can run as a CLI, Python library, web service, container or MCP plugin.

385 AI skills, 76 expert agents, and 906 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit. Installs on Claude Code, Cursor, Codex, Gemini, Copilot, and 6 other AI assistants.

A local Python + SQLite MCP memory service that stores persona, shared experiences, facts, agreements, and unfinished work, then restores them within a context budget so a new model can continue the same conversation. It supports offline original-text persistence, Chinese keyword retrieval, provenance-tracked memories, persona versioning/rollback, and progressive fading of older events.

Enables an AI agent to drive a remote Linux machine over the LAN through a single TCP connection, exposing tools for shell commands, jobs, interactive PTYs, file read/write/search/transfer, process and service management, networking, packages, and power control, with optional multi-host switching. Operates without SSH or any Linux-side installation beyond one stdlib-only Python daemon, using a state-free binary protocol.

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.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

Enables coding agents to run diff-scoped mutation testing on Python code, returning mutation scores and surviving mutants with actionable hints so they can improve tests.

Enables an AI assistant to convert PDF and EPUB chess books into faithful PGN by reading pages and diagrams, validating moves with python-chess, preserving variations and commentary, and reporting verifiable issues.

Enables AI agents to query a Python codebase as a knowledge graph over MCP, exposing symbol search plus callers, callees, and module dependency tools.

MCP server that checks a website's AI-search (GEO) readiness: robots.txt access for 16 AI crawlers (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, Google-Extended, Bingbot...), JSON-LD schema, llms.txt, sitemap and basic SEO tags. Python, stdio, MIT.

Enables creating, inspecting, and stopping Google Colab sessions, executing Python code or files, retrieving session URLs, and reading logs through the local Colab CLI.

Enables coding agents to perform semantic Python AST diffing across git boundaries, detect breaking changes, and generate concise PR triage summaries for reviewers via the Model Context Protocol.

CLI and MCP server for driving the Unreal Engine 5 editor with AI — create and edit materials, blueprints, Niagara, PCG, StateTrees, data tables, widgets, and Enhanced Input. Single Go binary, no Python, no dependencies.

lightweight Python-based MCP (Model Context Protocol) server for local ComfyUI

CoexistAI is a modular, developer-friendly research assistant framework . It enables you to build, search, summarize, and automate research workflows using LLMs, web search, Reddit, YouTube, and mapping tools—all with simple MCP tool calls or API calls or Python functions.

US parcel, zoning, ordinance and permit data, with a Python sandbox to query and map it

Check an npm or Python package, or a package.json, for vulnerabilities and upkeep.

Run sandboxed Python for calculations and PDFs; no other languages, installs or network.

Run A/B experiments through your AI assistant. SDKs for Ruby, Python, JS, Node, React Native, Go.