factsets

@joshua-auchincloss/factsets

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0 Stars 11 次浏览 joshua-auchincloss 更新于 2026-08-23

An MCP server that provides a local, persistent, self-maintaining knowledge base for AI agents

知识管理与记忆

MCP 服务配置

复制以下 JSON 到 OPClaw 或其他 MCP 客户端的配置文件中即可使用

{
  "mcpServers": {
    "factsets": {
      "args": [
        "factsets@0.2.0"
      ],
      "command": "npx"
    }
  }
}

可用工具（5 个）

该服务在 MCP 协议中暴露的工具，AI 可按需调用

tavily_search 14 个参数需填 1 项 Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

必填参数：query

tavily_extract 6 个参数需填 1 项 Extract content from URLs. Returns raw page content in markdown or text format.

Extract content from URLs. Returns raw page content in markdown or text format.

必填参数：urls

tavily_crawl 11 个参数需填 1 项 Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

必填参数：url

tavily_map 8 个参数需填 1 项 Map a website's structure. Returns a list of URLs found starting from the base URL.

Map a website's structure. Returns a list of URLs found starting from the base URL.

必填参数：url

tavily_research 2 个参数需填 1 项 Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

必填参数：input

服务介绍

Factsets

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| - -- -- -- | - -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- - |
| Package | NPM Downloads NPM Version |
| CI/CD | |

A self-maintaining knowledge base for AI agents, exposed via the
Model Context Protocol (MCP). Manages facts (atomic knowledge), resources
(cached external content), skills (procedural markdown), and execution logs (command history) using SQLite.

# Features

Persistent Context - Knowledge survives across sessions
Self-Maintaining - Staleness detection with refresh instructions
Tag-Based Organization - Flexible categorization and retrieval
Skill Documents - Markdown files for procedural knowledge
User Preferences - Configurable output style and agent behavior
MCP Protocol - Standard interface for AI tool/prompt definitions

# Installation

npm install - -global factsets
pnpm install - -global factsets
bun install - -global factsets

# Quick Start

# # As MCP Server

Add to your MCP client configuration (Claude Desktop, GitHub Copilot, Cursor, etc.), using bunx, npx or pnpm dlx accordingly:

{
  "mcpServers": {
    "factsets": {
      "command": "bunx",
      "args": ["factsets", "mcp-server"]
    }
  }
}

Or run directly:

bunx factsets mcp-server

# # First-Time Setup

After adding Factsets to your MCP client, run the setup prompt to integrate it into your project:

In a supported IDE: Type /mcp.factsets.setup in the chat to run the guided setup

In other clients: Call the get_setup_guide tool or use the setup prompt

The setup guide will:

Analyze your project structure and establish baseline facts
Configure the skills directory for your AI client
Create or update AGENTS.md with Factsets instructions
Migrate any existing skills with Factsets integration
Register key configuration files as resources

This one-time setup ensures agents have full context on every future interaction.

# # CLI Commands

#  Start MCP server (default command - auto-watches skill files and seeds starter content)
bunx factsets [- -database-url <path>] [- -client <type>]

#  Explicit mcp-server command (same as above)
bunx factsets mcp-server [- -database-url <path>] [- -client <type>]

#  Start without file watching
bunx factsets - -no-watch-skills

#  Start without seeding starter content
bunx factsets - -no-seed

#  Run file watcher standalone
bunx factsets watch-files [- -database-url <path>]

#  Run background maintenance worker
bunx factsets worker [- -database-url <path>]

#  Export database to JSON
bunx factsets dump backup.json

#  Restore database from JSON
bunx factsets restore backup.json

The - -client flag configures where skill files are stored (e.g., github-copilot -> .github/prompts/skills/). If you want to change clients / your skill directory, do so through your agent which will migrate skills for you.

See Configuration Guide for all options.

# Core Concepts

# MCP Tools

# # Facts

# # Resources

# # Skills

# # Execution Logs

# # Tags

# # Configuration

# # User Preferences

# # Maintenance

# # Context & Guides

# MCP Prompts

# Documentation

Configuration Guide - CLI flags, client setup, and skills directory
Design Reference - Full API documentation
Concept - Philosophy and design rationale
Agent Workflow - How agents use Factsets

# Development

#  Run tests
bun test

#  Run full e2e (tests + build + dry run)
bun e2e

#  Build distribution
bun dist

#  Format code
bun format

#  Lint
bun lint

#  Generate database migrations
bun migrations

#  Inspect MCP server with inspector
bun inspect

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