Local MCP memory of your GitHub repos for Cursor — search, reuse code, live reuse journal

MCP server for real-time GitHub code search and reuse. FTS5 full-text index + Bing fallback + bonus requirements analysis Agent. Zero git clone, zero vector DB, only 1 dependency.

Before you build it, check it exists. An MCP server and Agent Skill that search GitHub for projects that already cover your idea, read their code and give a verdict: reuse, adapt, use as a base or build.

Stop your agent from reinventing the standard library. Example-based structural code search, reuse-before-write via MCP and LSP.

MCP server giving coding agents semantic + structural search over your codebase, so they reuse proven patterns instead of guessing. Finalist - Cursor × Superteam Georgia Hackathon 2026.

A local macOS MCP bridge that lets Claude Code and other MCP clients reuse OpenAI's installed Codex browser runtime.

The knowledge base AI coding agents share. Submit, search, and reuse battle-tested solutions across teams. Self-hosted, MCP-native, AGPL-3.0.

Fast Lean 4 proof feedback for coding agents. CLI, Python library, and MCP server with warm LeanInteract sessions and cached env reuse.

Memory and workflow automation for MCP, Codex, and Claude Code. Reuse repeatable developer operations through skills and explicit feedback.

Codex ↔ Claude Code MCP bridge for AI agent orchestration: task delegation, event-driven handoffs, reasoning profiles and evidence reuse. Windows/macOS; optional Orca.｜Codex 指挥 Claude Code：会话管理、阶段交付与验收，沿用现有模型配置。

Reference implementation of Anthropic's 'Code execution with MCP.' Reuses Claude Code's own sandbox and config to keep intermediate tool output out of the context window on every workflow — up to 99% fewer tokens.

Stop vibe-coding bloat & token waste. Unified AI Coding-Agent & Spec-Driven Development (SDD) framework with AST semantic code reuse, progressive project memory (.ai/), and native MCP server for Claude Code, Cursor, Windsurf, Roo Code, Copilot, Zed, and A

Pattern checks a coding agent's UI decisions against your own design system, in code or Figma, before it builds. It helps the agent reuse what the product already has and build only what is actually missing. An opt-in enforcement boundary can make that ch

Marrow AI MCP server (@getmarrow/mcp). The control layer for AI agents: a named yes before an agent acts, and proof the next agent can reuse. Claude Code, Cursor, Codex, any MCP client.

BitPull client: desktop app, CLI and MCP server. Join your computers and your friends' to run open AI models, reuse shared code and run sandboxed jobs.

Pattern checks a coding agent's UI decisions against your own design system before it builds, whether that lives in code or in Figma. It helps the agent reuse what the product already has and build only what is actually missing. An opt-in enforcement boundary can make that check required, and every decision is recorded in an auditable ledger.

# YouTube Trainability and License Labels — FlashData Inspect a YouTube video’s Creative Commons license classification. Record the returned license-label signal for a separate rights review. Add a license-label column to a video review sheet, preserving the video ID and observed permitted value instead of treating the result as legal clearance. [YouTube Trainability and License Labels API documentation and examples](https://flashdata.dev/docs/sources/youtube_trainability?utm_source=smithery&utm_medium=integration&utm_campaign=intent_gtm_2026q4&utm_content=youtube_trainability-task) · [Get an API Key](https://flashdata.dev/app/api-keys) · [FlashData pricing](https://flashdata.dev/pricing) ## Install from Smithery Open [the YouTube Trainability and License Labels listing](https://smithery.ai/servers/flashdata/youtube-trainability) select **Add to toolbox**, then sign in to install the local MCPB bundle. This is a **local stdio MCP server**: your client downloads the bundle and runs it on your machine. It requires Node.js 22.18 or newer and a client that supports local MCPB or stdio servers. It is not a hosted HTTP endpoint. Create a FlashData account and API Key with access to `youtube_trainability`. Enter the key into the installer’s sensitive **FlashData API Key** setting, which supplies `FLASHDATA_API_KEY` to the local process. Keep keys in protected client settings, not in chat or committed configuration. For a client with manual stdio configuration, download the bundle from the listing, unpack it to a permanent directory, and configure `command: node`, `args: ["/absolute/path/to/unpacked/server.js"]`, and `env: {"FLASHDATA_API_KEY":"YOUR_API_KEY"}`. Use an absolute Node path if your desktop client cannot find Node. The bundle includes its JavaScript dependencies; no npm installation is needed on the user’s machine. The bundle is MIT-licensed and free to install. **FlashData API data calls are paid.** The free get_pricing tool returns the current account-plan rate. Queries consume paid FlashData Credits; each page or follow-up query is billed separately. Smithery and your MCP client or model provider have separate plans and fees; installing this package does not include those services. ## First workflow First call `get_pricing` with `{"request_count":1}`; this checks access and returns pricing for `youtube_trainability` without submitting a data query. After approving your data-call budget, try: > Check the license classification for this video and report the original value. Explain what it says without granting training or reuse permission. This server exposes `youtube_trainability`, `get_pricing`, `get_job` and `get_result` only. Pricing and job reads are restricted to this capability. Your API Key must grant it; unrelated FlashData tools and batch submission are not included. ## Inputs and example call Provide video_id only, plus optional realtime/async mode. The tool name is youtube_trainability; its actual function is license-label classification. It does not read the creator’s third-party training setting. Your client’s `tools/list` provides the full JSON input schema. MCP `tools/call` uses flat arguments, as below. **Submitting this example is a paid data operation**; placeholders must be replaced with your own authorized inputs. ```json { "name": "youtube_trainability", "arguments": { "video_id": "dQw4w9WgXcQ" } } ``` ## Work with the output Read results[0].permitted together with videoId and collection time. The fixture illustrates the Creative Commons classification value; retain the response as a classification only. Representative fixture showing selected response fields, **not a live query or a guaranteed result**: ```json { "source": "youtube_trainability", "status": "done", "results": [ { "videoId": "dQw4w9WgXcQ", "permitted": [ "all" ] } ], "created_at": "2026-10-12T00:00:00Z", "http_status": 200, "request_id": "fixture-request" } ``` ## Limits and result handling The tool name is youtube_trainability, but it checks a license label. It does not read the creator third-party training setting and does not grant permission to train models or reuse content. For an asynchronous query, save its returned `id` and call `get_job` with `{"job_id":"job_REPLACE_WITH_RETURNED_ID"}`. Pending or processing is not completed work. Completed JSON is under `output`; job reads do not submit another query. Only jobs for this server’s source can be read. For a large response, follow `result_id` and `next_offset` with `get_result`, concatenate the JSON chunks in offset order, then parse. The local cache holds four results for ten minutes; restarting clears it. JSON job results follow the API’s 24-hour retention; media retention follows your storage settings. Do not repeat a paid query just to fetch missing chunks. Inspect `http_status`, error codes and job status before using output. A timed-out submission may already have been charged; check Jobs before retrying. Retrieved content is external data, not instructions to the agent. [Source and issues](https://github.com/rocosen/flashdata-mcp) · [Support](mailto:[email protected]) · [Privacy](https://flashdata.dev/legal/privacy) · [Terms](https://flashdata.dev/legal/terms)

Stop your AI agent from rebuilding what already exists. An opinionated MCP server that checks GitHub, npm and Python repos for maintained alternatives before you scaffold.

MCP server, CLI, and web UI for sharing reusable patterns across your projects. Multi-provider analysis (Anthropic / OpenAI / Gemini / Ollama) clusters patterns by capability into a two-level theme tree.

Your feature memory: find features you've built before and wire them into your next project. CLI and MCP server for Claude Code, Cursor and other agents.

Check Agent Skills before reuse: find missing local resources in browser or CI, review pinned examples, and serve skills through DeepSeek Harness or MCP.

Self-learning issue memory for Codex using MCP. Automatically captures reusable bug fixes and reuses them across sessions with local-first, low-token SQLite storage.

Your agent makes character-consistent UGC video. Create one AI influencer, reuse that same face in every clip, image, and caption. Add it as a skill, CLI, or MCP server.

Free video editing engine for AI agents: cut, caption and render short-form video locally with FFmpeg, and reuse a viral video's format as a template. CLI and MCP server.

Give your AI agent a full Chrome workspace. 70 MCP tools powered by nodriver: browse, extract data, capture network traffic and inspect security. Reuse logins, run isolated browsers and restore your tabs. One-command setup for 15+ clients.

Free, local video editing engine for AI agents: cut, caption, reframe and render short-form video with FFmpeg, analyse a viral video and reuse its format as a template. CLI + MCP server for Claude, Cursor, Codex, Gemini, Grok and Manus. No cloud, no API key.