FeedRecall MCP Server
Turns likes, bookmarks, and saved links into persistent project-aware memory, enabling AI agents to search, import, and process personal content through a local SQLite-backed MCP server.
README
FeedRecall
Your feed is an inbox, not a knowledge base.
FeedRecall turns likes, bookmarks, saved links, repositories, videos, and articles into persistent project-aware memory for AI agents. It runs locally, keeps an evidence trail, and exposes the same memory to Codex, Claude Code, Cursor, and other MCP clients.

Why
Saving a discovery is only the beginning:
capture -> enrich -> classify -> connect to projects -> verify -> test -> adopt or reject
FeedRecall remembers both what a source claimed and what you or your agents later observed. Six months later, an agent can avoid recommending a tool that already failed your Windows test.
Quick start
npm install
npm run build
node dist/cli.js init
node dist/cli.js import examples/discoveries.json
node dist/cli.js import-projects examples/projects.json
node dist/cli.js search "Roblox MCP"
node dist/cli.js serve
The canonical database is ~/.feedrecall/feedrecall.db, so local agents share one memory even when
they run from different project folders. Set FEEDRECALL_HOME only when you intentionally want an
isolated vault.
Optional local enrichment requires Ollama and a local model:
ollama pull qwen3:4b
node dist/cli.js process --model qwen3:4b
Then connect the local MCP server:
node dist/cli.js install-client codex
node dist/cli.js install-client claude
node dist/cli.js install-client cursor
What v0.1 includes
- Local SQLite storage and full-text search.
- Idempotent JSON and URL imports.
- Processing, evidence, and decision lifecycles.
- Projects with transparent relevance scores.
- Timeline queries using published, first-seen, and last-seen dates.
- Read/write MCP tools with explicit annotations.
- Local dashboard and browser capture extension.
- Optional local enrichment through Ollama; core features work without it.
Capture without the X API
FeedRecall does not require the paid X API. Load the unpacked extension from extension/, open your Likes or Bookmarks page while already signed in, and export the posts visible in your browser. FeedRecall never asks for your X password and never stores browser cookies.
The extension captures only posts currently rendered in the page. Scroll, scan, export, then import
the JSON. Repeating the flow is safe: stable source identifiers prevent duplicate memories while
firstSeenAt and lastSeenAt preserve the timeline.
Public post URLs can also be enriched with local collectors such as gallery-dl and yt-dlp. Network sources are treated as untrusted data, never executable instructions.
Privacy
- Local-first by default; no telemetry.
- No remote model provider in the core application.
- Tokens, cookies, and passwords are not accepted in imports.
- The public repository contains only synthetic examples.
- Destructive social cleanup is intentionally outside v0.1.
Roadmap
- v0.1 Memory: import, enrich, classify, search, timeline, MCP.
- v0.2 Projects: project objects, context packs, collections, stronger relevance.
- v0.3 Intelligence: opportunities, experiments, decisions, agent feedback loop.
- v1 Knowledge OS: discover, understand, suggest, test, implement, remember.
See docs/PRODUCT_PLAN.md for the product model and release plan, and docs/ROADMAP.md for acceptance criteria and boundaries.
Architecture
Browser / JSON / URL connectors
|
v
SQLite + FTS5
|
+-------+--------+
| |
Local cockpit MCP server
|
Codex / Claude Code / Cursor
ChatGPT desktop, Codex CLI, and the Codex IDE extension share the same local MCP configuration, as described in the official Codex MCP documentation. ChatGPT web requires a future remote, authenticated plugin and is intentionally outside this local-first release.
Contributing and security
See CONTRIBUTING.md and SECURITY.md. Never attach a real personal vault or browser export to a public issue.
License
Apache-2.0
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。