Which integrations are available for this server?

Provides a self-maintaining knowledge base for AI agents, enabling persistent context and knowledge management across sessions when used with GitHub Copilot via the Model Context Protocol.

How do I use factsets?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@factsets what facts do you have about my project?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

factsets

by joshua-auchincloss

Overview Schema Related Servers Score Discussions

TypeScript

Local

Factsets


Package
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

Related MCP server: UseCortex MCP Server

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

Concept	Description
Facts	Atomic knowledge units (1-3 sentences), tagged and timestamped
Resources	External content (files, URLs, APIs) with cached snapshots and retrieval methods
Skills	Markdown documents for procedural knowledge, stored on filesystem
Execution Logs	Command history with success/failure tracking for skill validation
Tags	Flexible categorization for all content types

MCP Tools

Facts

Tool	Description
`submit_facts`	Add facts with tags and source tracking
`search_facts`	Query facts by tags, content, or filters
`verify_facts`	Mark facts as verified by ID
`verify_facts_by_tags`	Bulk verify facts by tags
`update_fact`	Update fact content, metadata, or tags
`delete_facts`	Remove facts by criteria
`restore_facts`	Restore soft-deleted facts

Resources

Tool	Description
`add_resources`	Register resources with retrieval methods
`search_resources`	Find resources by tags, type, or URI
`get_resources`	Get resources by ID or URI with freshness
`update_resource_snapshot`	Update cached content for single resource
`update_resource_snapshots`	Bulk update cached content
`update_resource`	Update resource metadata (not content)
`delete_resources`	Remove resources
`restore_resources`	Restore soft-deleted resources

Skills

Tool	Description
`create_skill`	Create markdown skill document
`update_skill`	Update skill metadata/references
`search_skills`	Find skills by tags or query
`get_skills`	Get skills by name with content
`link_skill`	Link skill to facts/resources/skills
`sync_skill`	Sync skill after file edit
`delete_skills`	Remove skills
`get_dependency_graph`	Get skill dependency tree
`restore_skills`	Restore soft-deleted skills

Execution Logs

Tool	Description
`submit_execution_logs`	Record command/test/build executions
`search_execution_logs`	Find executions by query, tags, success
`get_execution_log`	Get execution details by ID

Configuration

Tool	Description
`get_config`	Get a configuration value by key
`set_config`	Set a configuration value
`delete_config`	Delete a configuration value
`list_config`	List all configuration with schema
`get_config_schema`	Get available options with descriptions

User Preferences

Tool	Description
`get_preference_prompt`	Get natural language preference prompt
`get_user_preferences`	Get structured preference data
`infer_preference`	Update preference from user behavior
`reset_preferences`	Reset preferences to defaults

Maintenance

Tool	Description
`check_stale`	Find stale resources and dependencies
`mark_resources_refreshed`	Mark resources as current

Context & Guides

Tool	Description
`get_knowledge_context`	Build context from tags (facts/resources/skills)
`build_skill_context`	Get skill with formatted content and refs
`get_maintenance_report`	Generate staleness/maintenance report
`get_refresh_guide`	Get instructions for refreshing a resource
`get_agent_guide`	Get the agent workflow guide (call first)
`get_concept_guide`	Get conceptual overview and design philosophy
`get_config_guide`	Get configuration guide with all options

MCP Prompts

Prompt	Description
`setup`	Guided setup for new project integration
`user_preferences`	Get user preferences for output formatting
`knowledge_context`	Build context from tags
`recall_skill`	Get skill with references
`maintenance_report`	Staleness summary
`refresh_guide`	Instructions to refresh a resource
`agent_guide`	Agent workflow guide (call first)
`concept`	Conceptual overview and philosophy
`config`	Configuration guide with all options

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

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

1dRelease cycle

4Releases (12mo)

Commit activity

Resources

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Tool	Description
`create_tags`	Create organizational tags
`list_tags`	List tags with usage counts
`update_tags`	Update tag descriptions
`prune_orphan_tags`	Clean up unused orphan tags

Factsets

Features

Installation

Quick Start

As MCP Server

First-Time Setup

CLI Commands

Core Concepts

MCP Tools

Facts

Resources

Skills

Execution Logs

Tags

Configuration

User Preferences

Maintenance

Context & Guides

MCP Prompts

Documentation

Development

Maintenance

Resources

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Latest Blog Posts

MCP directory API