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。
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。
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。
# 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"
// 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

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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
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[!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.
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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.
| Mode | What it is | Access |
|---|---|---|
| **Standalone** | General-purpose AI assistant — search, code, KB | Portal |
| **Copilot** | AI embedded in a host system's UI | iframe / widget / embed |
| **Hub** | Central AI orchestration across all connected systems | Portal / API |
#### 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 bridge — BaseChannel 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
```bash git clone https://github.com/fim-ai/fim-one.git cd fim-one
cp example.env .env
Dashboard — stats, activity trends, token usage, and quick access to agents and conversations.

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

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

Using Agents
Using Planner Mode
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 ```
FIM One works with any OpenAI-compatible provider:
| Provider | LLM_API_KEY | LLM_BASE_URL | LLM_MODEL |
|---|---|---|---|
| **OpenAI** | sk-... | *(default)* | gpt-4o |
| **DeepSeek** | sk-... | https://api.deepseek.com/v1 | deepseek-chat |
| **Anthropic** | sk-ant-... | https://api.anthropic.com/v1 | claude-sonnet-4-6 |
| **Ollama** (local) | ollama | http://localhost:11434/v1 | qwen2.5:14b |
Minimal .env:
```bash LLM_API_KEY=sk-your-key
Common questions about deployment, LLM providers, system requirements, and more — see the FAQ.
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。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
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。
| 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 |
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。
选择 Agent 类型,复制安装指令后粘贴到对应客户端