Factsets MCP Server

io.github.joshua-auchincloss由 GitHub 帳號 joshua-auchincloss 發布v0.2.0更新於 Nov 3, 2026

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

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

為 AI 代理提供以本機 SQLite 為基礎的持久知識庫，保存事實、快取資源、技能文件與執行紀錄，跨工作階段保留上下文。

可以做什麼
  • 新增、搜尋、驗證、更新、刪除與還原帶標籤的事實，並記錄來源
  • 註冊資源並快取快照，支援搜尋、更新快照與過期偵測
  • 建立、搜尋、連結與同步 Markdown 技能文件，查看技能相依關係
  • 記錄並查詢指令、測試與建置的執行紀錄
  • 管理標籤、設定值與使用者偏好
  • 依標籤建立上下文，並取得安裝、維護與更新指南
適用情境
適合希望助理在多次工作階段之間記住專案知識、為外部檔案與 API 保留附過期追蹤的筆記，並累積可重複使用操作技能的場合。長期專案反覆重建上下文時特別有用。
執行需求
以本機 stdio 程序執行，透過 npx、bunx 或 pnpm dlx 啟動，需要 Node.js 或 Bun 執行環境。使用本機 SQLite 資料庫，可用資料庫路徑參數指定。未宣告需要帳號、API 金鑰或環境變數。建議執行一次性的安裝提示或工具呼叫以整合至專案。
安裝前請注意
它會寫入本機資料庫與專案檔案：安裝流程可能建立或修改 AGENTS.md、設定技能目錄並移轉既有技能，技能檔案也會被監看。事實、資源與執行紀錄會長期保存在本機，應留意助理寫入的內容，並在破壞性操作前用匯出指令備份。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Factsets MCP Server，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Factsets

Package[NPM Downloads] [NPM Version]
CI/CD[Release] [Tests] [codecov]

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

bash
npm install --global factsetspnpm install --global factsetsbun 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:

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

Or run directly:

bash
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

bash
# 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 watchingbunx factsets --no-watch-skills
# Start without seeding starter contentbunx factsets --no-seed
# Run file watcher standalonebunx factsets watch-files [--database-url <path>]
# Run background maintenance workerbunx factsets worker [--database-url <path>]
# Export database to JSONbunx factsets dump backup.json
# Restore database from JSONbunx 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

ConceptDescription
FactsAtomic knowledge units (1-3 sentences), tagged and timestamped
ResourcesExternal content (files, URLs, APIs) with cached snapshots and retrieval methods
SkillsMarkdown documents for procedural knowledge, stored on filesystem
Execution LogsCommand history with success/failure tracking for skill validation
TagsFlexible categorization for all content types

MCP Tools

Facts

ToolDescription
submit_factsAdd facts with tags and source tracking
search_factsQuery facts by tags, content, or filters
verify_factsMark facts as verified by ID
verify_facts_by_tagsBulk verify facts by tags
update_factUpdate fact content, metadata, or tags
delete_factsRemove facts by criteria
restore_factsRestore soft-deleted facts

Resources

ToolDescription
add_resourcesRegister resources with retrieval methods
search_resourcesFind resources by tags, type, or URI
get_resourcesGet resources by ID or URI with freshness
update_resource_snapshotUpdate cached content for single resource
update_resource_snapshotsBulk update cached content
update_resourceUpdate resource metadata (not content)
delete_resourcesRemove resources
restore_resourcesRestore soft-deleted resources

Skills

ToolDescription
create_skillCreate markdown skill document
update_skillUpdate skill metadata/references
search_skillsFind skills by tags or query
get_skillsGet skills by name with content
link_skillLink skill to facts/resources/skills
sync_skillSync skill after file edit
delete_skillsRemove skills
get_dependency_graphGet skill dependency tree
restore_skillsRestore soft-deleted skills

Execution Logs

ToolDescription
submit_execution_logsRecord command/test/build executions
search_execution_logsFind executions by query, tags, success
get_execution_logGet execution details by ID

Tags

ToolDescription
create_tagsCreate organizational tags
list_tagsList tags with usage counts
update_tagsUpdate tag descriptions
prune_orphan_tagsClean up unused orphan tags

Configuration

ToolDescription
get_configGet a configuration value by key
set_configSet a configuration value
delete_configDelete a configuration value
list_configList all configuration with schema
get_config_schemaGet available options with descriptions

User Preferences

ToolDescription
get_preference_promptGet natural language preference prompt
get_user_preferencesGet structured preference data
infer_preferenceUpdate preference from user behavior
reset_preferencesReset preferences to defaults

Maintenance

ToolDescription
check_staleFind stale resources and dependencies
mark_resources_refreshedMark resources as current

Context & Guides

ToolDescription
get_knowledge_contextBuild context from tags (facts/resources/skills)
build_skill_contextGet skill with formatted content and refs
get_maintenance_reportGenerate staleness/maintenance report
get_refresh_guideGet instructions for refreshing a resource
get_agent_guideGet the agent workflow guide (call first)
get_concept_guideGet conceptual overview and design philosophy
get_config_guideGet configuration guide with all options

MCP Prompts

PromptDescription
setupGuided setup for new project integration
user_preferencesGet user preferences for output formatting
knowledge_contextBuild context from tags
recall_skillGet skill with references
maintenance_reportStaleness summary
refresh_guideInstructions to refresh a resource
agent_guideAgent workflow guide (call first)
conceptConceptual overview and philosophy
configConfiguration guide with all options

Documentation

Development

bash
# Run testsbun test
# Run full e2e (tests + build + dry run)bun e2e
# Build distributionbun dist
# Format codebun format
# Lintbun lint
# Generate database migrationsbun migrations
# Inspect MCP server with inspectorbun inspect

來源：README.md，提交 1cf149c

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包，因此其工具未知。

版本歷史

1
  1. v0.2.0最新Sep 16, 2026

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