能力标签
开源MCP工具
🔌
MCP工具

开源MCP工具

基于 Python · 让 AI 助手直接操作你的系统与工具
英文名:fim-one
⭐ 1.2k Stars 🍴 133 Forks 💻 Python 📄 NOASSERTION 🏷 AI 7.5分
7.5AI 综合评分
aiai-agentconnectordagpython
⚙️ 配置说明
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,开源MCP工具 获评「推荐使用」。已获得 1.2k 颗 GitHub Star,这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。

📚 深度解析

开源MCP工具 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 开源MCP工具,你的 AI 助手将获得额外的工具调用能力,可以用自然语言直接操控该工具的功能,无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置,永久增强"——配置完成后,每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上,MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信,工具的功能以"工具列表"的形式暴露给 AI 模型,AI 可以按需调用。开源MCP工具 提供了结构化的工具调用接口,使 AI 模型能够精确地理解和使用每个功能点,显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比,MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON,即可让 AI 获得全新能力。AI Skill Hub 将 开源MCP工具 评为 AI 评分 7.5 分,属于同类工具中的优质选择。

📋 工具概览

开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

GitHub Stars
⭐ 1.2k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
正常维护,社区驱动
开源协议
NOASSERTION
AI 综合评分
7.5 分
工具类型
MCP工具
Forks
133

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

📌 核心特色
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
🎯 主要使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/fim-ai/fim-one

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "--mcp--": {
      "command": "npx",
      "args": ["-y", "fim-one"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 开源MCP工具 执行以下任务...
Claude: [自动调用 开源MCP工具 MCP 工具处理请求]

# 查看可用工具列表
# 在 Claude 中输入:"列出所有可用的 MCP 工具"
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "__mcp__": {
      "command": "npx",
      "args": ["-y", "fim-one"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 README 深度解析 真实文档 完整度 81/100 含工作流图 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

FIM One Banner

Python 3.11+ CI License Discord Follow on X

🌐 English | 🇨🇳 中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch | 🇫🇷 Français

All-in-One Agent Platform for Global × China Enterprises. Wire every system you already run — global SaaS to the China stack — through one agent core.

🌐 Website · 📖 Docs · 📋 Changelog · 🐛 Report Bug · 💬 Discord · 🐦 Twitter · 🏆 Product Hunt

</div>

[!TIP] ☁️ Skip the setup — try FIM One on Cloud. A managed version is live at cloud.fim.ai — no Docker, no API keys, no config. Sign in and start connecting your systems in seconds. Early access, feedback welcome.

---

Overview

Global enterprises run a sprawl of systems that don't talk to each other — ERP, CRM, OA, HR, finance, databases, IM platforms across regions. FIM One is the all-in-one agent platform that wires every system you already run into one agent core — global SaaS on one side, the full China stack (Feishu, WeCom, DingTalk, DM, Kingbase, etc.) on the other. One brain. Every system. Global SaaS × China Stack.

ModeWhat it isAccess
**Standalone**General-purpose AI assistant — search, code, KBPortal
**Copilot**AI embedded in a host system's UIiframe / widget / embed
**Hub**Central AI orchestration across all connected systemsPortal / API
graph LR ERP <--> Hub["🧠 FIM One Agent Core"] Database <--> Hub Lark <--> Hub Hub <--> CRM Hub <--> OA Hub <--> API[Custom API]

Key Features

#### Cross-Border Connectivity - Three delivery modes — Standalone assistant, embedded Copilot, or central Hub; same agent core. - Any system, one pattern — Connect APIs, databases, MCP servers. Actions auto-register as agent tools with auth injection. Progressive disclosure meta-tools reduce token usage by 80%+ across all tool types. - Database connectors — PostgreSQL, MySQL, Oracle, SQL Server, and enterprise databases common in China (DM, KingbaseES, GBase, Highgo) that most global platforms can't reach. Schema introspection and AI-powered annotation. - Three ways to build — Import OpenAPI spec, AI chat builder, or connect MCP servers directly.

#### Planning & Execution - Dynamic DAG planning — LLM decomposes goals into dependency graphs at runtime. No hard-coded workflows. - Concurrent execution — Independent steps run in parallel via asyncio; auto re-plan up to 3 rounds. - ReAct agent — Structured reasoning-and-acting loop with automatic error recovery. - Agent harness — Production-grade execution environment: ContextGuard for 5-layer token-budget management, progressive-disclosure meta-tools to keep the tool surface tractable, and self-reflection loops to counter goal drift. - Hook System — Deterministic enforcement that runs outside the LLM loop. First shipped: FeishuGateHook gates sensitive tool calls behind a human approval card posted to a Feishu group. Extensible to audit logging, read-only-mode guards, and rate limits (v0.9). - Content guardrails — Three-layer safety: tool-permission hooks (actions), credential / SSRF / MCP-auth checks (protocols), and content guardrails (input/output text). Default jailbreak-phrase detector aborts the turn before the LLM is invoked, saving tokens and surfacing a clear blocked notice in chat. Output guardrails optional via FIM_GUARDRAILS_OUTPUT. - Auto-routing — Classifies queries and routes to optimal mode (ReAct or DAG). Configurable via AUTO_ROUTING. - Extended thinking — Chain-of-thought for OpenAI o-series, Gemini 2.5+, Claude. - Prompt-cache observability — Anthropic prompt-cache read/create token counts captured per turn, surfaced in the chat done payload and logged so operators can verify cache hits and detect relay stations that don't honor the discount.

#### Workflow & Tools - Visual workflow editor — 12 node types, drag-and-drop canvas (React Flow v12), import/export as JSON. - Smart file handling — Uploaded files auto-inlined into context (small) or readable on-demand via read_uploaded_file tool. Intelligent document processing: PDFs, DOCX, and PPTX files get vision-aware processing with embedded image extraction when the model supports vision. Smart PDF mode extracts text from text-rich pages and renders scanned pages as images. - Universal document conversion — Built-in convert_to_markdown tool turns PDF / Word / Excel / PowerPoint / HTML / images / audio / Outlook .msg / EPUB / YouTube transcripts into clean Markdown via Microsoft MarkItDown. Vision-capable LLMs OCR embedded images and scanned pages — works with Claude, Gemini, Bedrock, and any LiteLLM-supported provider, no per-provider adapter code. - Pluggable tools — Python, Node.js, shell exec with optional Docker sandbox (CODE_EXEC_BACKEND=docker). - V4A patch editing — Beyond find_replace, agents can apply line-hunk patches with fuzzy whitespace matching via file_ops.apply_patch — robust to multi-line edits where exact-substring match would be brittle. - Full RAG pipeline — Jina embedding + LanceDB + hybrid retrieval + reranker + inline [N] citations. Vision-aware ingestion routes scanned PDFs and Office embedded images through the workspace's default vision LLM for OCR. - Tool artifacts — Rich outputs (HTML previews, files) rendered in-chat.

#### Messaging Channels (v0.8) - Org-scoped IM bridgeBaseChannel abstraction for outbound messaging across Slack, Microsoft Teams, Discord, Feishu (Lark), WeCom, and DingTalk. First shipping implementation is Feishu; Slack / Teams / WeCom / Email are next on the v0.9 roadmap. - Fernet-encrypted credentials — App secrets and encrypt keys encrypted at rest; every inbound callback signature-verified. - Interactive approval cards — Channel-native GateHook (Feishu today, Slack/Teams next) posts an Approve / Reject card to your group when a sensitive tool call fires; the tool blocks until a group member taps a verdict. Human-in-the-loop approval without a custom workflow engine. - Configurable approval routing per agent — Three modes (Auto / Inline only / Channel only) with an approver-scope selector (initiator / agent owner / any org member). One audit path stamps approver_user_id and decided_at whether the verdict came from chat or from the channel. Auto mode falls back to inline if no channel is linked, so agents always get a real approval UX. - Task-completion notifications — Long-running ReAct or DAG agents can push a summary card to the org's channel when work finishes. Configurable per-agent in Settings → Agent → Notifications. - Browse-and-pick UI — No copying raw channel IDs from the vendor console; the portal calls the IM platform's API and shows a group picker.

#### Platform - Multi-tenant — JWT auth, org isolation, admin panel with usage analytics and connector metrics. Multi-worker support via WORKERS=N with a Redis interrupt broker for cross-worker relay. - Marketplace — Publish and subscribe to agents, connectors, KBs, skills, workflows. - Global skills (SOPs) — Reusable operating procedures loaded for every user; progressive mode cuts tokens ~80%. - Stripe billing & per-user quotas — Optional Pro-plan upgrade via Stripe Checkout + Customer Portal. Quota chain (per-user override → plan tier → system default) with 0 for unlimited. Admin feature flag gates the entire pipeline; private deployments without Stripe stay clean. - Evaluation Center — Test-dataset management, parallel eval runs with LLM-graded judgments, per-case pass/fail/latency/token results viewer with auto-polling. - Conversation recovery — Synthetic tool_result rows persist after interrupted turns; clients auto-reconnect dropped SSE streams via /chat/resume with exponential backoff and a "Reconnecting…" indicator. - 6 languages — EN, ZH, JA, KO, DE, FR. Translations are fully automated — single glossary drives every LLM translation call (JSON, MDX, README), pre-commit hook refuses manual edits to generated locale files. - First-run setup wizard, dark/light theme, command palette, streaming SSE, DAG visualization.

Deep dive: Architecture · Hook System · Channels · Execution Modes · Why FIM One · Competitive Landscape

Quick Start

Screenshots

Dashboard — stats, activity trends, token usage, and quick access to agents and conversations.

Dashboard

Agent Chat — ReAct reasoning with multi-step tool calling against a connected database.

Agent Chat

DAG Planner — LLM-generated execution plan with parallel steps and live status tracking.

DAG Planner

Demo

Using Agents

Using Agents

Using Planner Mode

Using Planner Mode

Edit .env: set LLM_API_KEY (and optionally LLM_BASE_URL, LLM_MODEL)

docker compose up --build -d


Open http://localhost:3000 — on first launch you'll create an admin account. That's it.
bash docker compose up -d # start docker compose down # stop docker compose logs -f # view logs ```

Configuration

FIM One works with any OpenAI-compatible provider:

ProviderLLM_API_KEYLLM_BASE_URLLLM_MODEL
**OpenAI**sk-...*(default)*gpt-4o
**DeepSeek**sk-...https://api.deepseek.com/v1deepseek-chat
**Anthropic**sk-ant-...https://api.anthropic.com/v1claude-sonnet-4-6
**Ollama** (local)ollamahttp://localhost:11434/v1qwen2.5:14b

Minimal .env:

```bash LLM_API_KEY=sk-your-key

LLM_BASE_URL=https://api.openai.com/v1 # default

FAQ

Common questions about deployment, LLM providers, system requirements, and more — see the FAQ.

⚡ 核心功能

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 使用场景

  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站

⚖️ 优点与不足

✅ 优点
  • +标准化 MCP 协议,生态互联性强
  • +与 Claude 官方生态无缝对接
  • +即插即用,配置简单快捷
⚠️ 不足
  • 依赖 Claude 客户端,非 Claude 用户无法使用
  • MCP 协议仍在持续演进,接口可能变更
  • 需要一定的配置步骤
⚠️ 使用须知

该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。

🔗 相关工具推荐

🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

MCP是多功能连接平台
💡 AI Skill Hub 点评

AI Skill Hub 点评:开源MCP工具 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。

⬇️ 获取与下载
📚 深入学习 开源MCP工具
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 fim-one
原始描述 开源MCP工具:Open-source agent platform for Global × China enterprises — wire every system th。⭐1.2k · Python
Topics aiai-agentconnectordagpython
GitHub https://github.com/fim-ai/fim-one
License NOASSERTION
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/fim-ai/fim-one 🌐 官方网站  https://one.fim.ai

收录时间:2026-05-31 · 更新时间:2026-05-31 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。