This is an MCP (Model Context Protocol) server that auto-generates AI-agent-friendly tools from the Notion REST API OpenAPI specification. It runs as a CLI tool (stdio transport) or an HTTP server (Streamable HTTP transport), exposing 24 tools covering users, search, blocks, pages, comments, data sources, and databases — including dedicated markdown read/write endpoints. The architecture follows a clean three-layer design: an OpenAPI-to-MCP converter (`parser.ts`) that transforms each operation into a tool with JSON Schema input/output; an MCP proxy (`proxy.ts`) that handles tool listing/calling and deserializes double-encoded JSON parameters (a known MCP client issue); and an HTTP client (`http-client.ts`) that executes the actual API calls using axios and openapi-client-axios. Notable engineering highlights include per-operation `Notion-Version` header sourcing from the spec (so each endpoint can pin its required API version), a per-request token passthrough mechanism for multi-tenant deployments, and comprehensive test coverage with snapshot tests locking the tool surface. The repo is well-structured TypeScript with clear separation of concerns, though the README signals that this local MCP server may be sunset in favor of a newer remote MCP offering.
This repository is a monorepo of 'skills' for the Minis AI assistant platform, designed to extend the assistant's capabilities with domain-specific workflows, API integrations, and automation scripts. Each skill is a self-contained directory with a SKILL.md (instruction set for the AI), executable scripts (Python/shell), reference documentation, and evaluation test cases. The skills cover a wide range of tasks: social media management (Weibo, Twitter/X, Xiaohongshu, Telegram, Bilibili), media processing (YouTube downloader, PDF conversion, TTS, image generation), financial data retrieval (US/Korea/China stocks, fund flows), productivity (Notion API, todoocard e-paper, whenpeak energy prediction), video composition (HyperFrames framework), and system utilities (Android dev environment, Cloudflare DNS, qBittorrent). The architecture is modular and agent-driven: the AI selects a skill based on user intent, reads its SKILL.md, and invokes the provided scripts via shell_execute. Output is typically JSON for machine consumption. The codebase demonstrates strong engineering practices with structured skill definitions, evaluation frameworks, and reusable patterns like cookie-based auth, SQLite message stores, and deterministic animation analysis. However, many skills depend on reverse-engineered or unofficial APIs, hardcoded paths tied to the Minis iOS environment, and external tools (ffmpeg, pandoc, typst), introducing maintenance and portability risks.