MCP Servers

A collection of Model Context Protocol servers, templates, tools and more.

U
Utcp MCP Gateway
by @Xuleileon
GitHub Repo(3 stars)

Save 90%+ Token Cost with Smart MCP Proxy

Created 12/3/2025
Updated 12 days ago
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utcp-mcp-gateway

🚀 The Smarter Way to Use MCP — Save 90%+ Tokens with Code Mode

English | 中文

Inspired by Claude Skills' Progressive Disclosure

claude skills alternative · mcp token optimization · progressive tool discovery · mcp response filtering

The Story / 故事

I noticed Claude Skills works amazingly well — Claude dynamically discovers and loads only the skills it needs, avoiding context overload.

But most agent frameworks? They dump 500+ tool definitions into the context at once. And MCP responses? Often 10,000+ chars of raw data that waste your tokens.

Then I discovered UTCP — exactly what I needed! Progressive tool discovery built-in. And Cloudflare's Code Mode? One code block executes everything. Brilliant!

So I built on top of UTCP: Progressive Discovery + Code Mode + LLM Filtering — the smarter way to use MCP.

The best part? Just add one config block. Works with Claude Desktop, Windsurf, Cursor, Dify, and any MCP-compatible client. No extra servers needed.

Looking for...?

  • ✅ Claude Skills alternative for your MCP setup
  • ✅ Better MCP tool management without context overload
  • ✅ Progressive tool discovery — load tools on demand
  • ✅ OpenAPI/Swagger to tools — register REST APIs as tools
  • ✅ MCP token optimization — reduce 90%+ token waste
  • ✅ MCP response filtering — smart summarization

You're in the right place.

What is this?

LLMs are great at writing code, but terrible at tool calling.

Traditional MCP exposes tools directly to LLMs — but LLMs struggle with:

  • Too many tools (500+ definitions = confusion)
  • Huge responses (10,000+ chars = wasted tokens)
  • Multiple round trips (15+ API calls = slow & expensive)

utcp-mcp-gateway fixes all of this:

| Problem | Solution | |---------|----------| | 500+ tool definitions | Progressive Discovery — load only what's needed | | 10,000+ char responses | LLM Filtering — smart summarization (97% smaller!) | | 15+ API round trips | Code Mode — one code block, one execution |

Traditional:  User → LLM → Tool1 → LLM → Tool2 → LLM → Tool3 → Result
              (15+ calls, $26/day, slow)

Code Mode:    User → LLM writes code → Execute all at once → Result  
              (1 call, $0.87/day, fast)

Result: $9,536/year savings (benchmark source)

Features

| Feature | Description | |---------|-------------| | 🔌 Universal MCP | Connect any HTTP or stdio MCP server | | 📜 OpenAPI Support | Register REST APIs directly from OpenAPI/Swagger specs | | 🧠 LLM Filtering | Intelligent summarization (97% response reduction!) | | 🔍 Progressive Discovery | search_tools - find tools without loading all 500 definitions | | ⚡ Code Mode | Execute TypeScript tool chains in one call | | 🔒 Secure Sandbox | Code runs in isolated environment | | 📦 Zero Config | Environment variables only, no config files |

Quick Start

Zero config files needed! Just add to Claude Desktop config:

Mode 1: HTTP MCP (Remote)

{
  "mcpServers": {
    "gateway": {
      "command": "npx",
      "args": ["-y", "utcp-mcp-gateway"],
      "env": {
        "MCP_URL": "https://mcp.context7.com/mcp",
        "MCP_NAME": "context7",
        "LLM_API_KEY": "sk-xxx",
        "LLM_BASE_URL": "https://api.openai.com/v1",
        "LLM_MODEL": "gpt-4o-mini"
      }
    }
  }
}

Mode 2: stdio MCP (Local)

{
  "mcpServers": {
    "gateway": {
      "command": "npx",
      "args": ["-y", "utcp-mcp-gateway"],
      "env": {
        "MCP_COMMAND": "npx",
        "MCP_ARGS": "-y,@anthropic/mcp-server-filesystem",
        "MCP_NAME": "filesystem",
        "MCP_TRANSPORT": "stdio",
        "LLM_API_KEY": "sk-xxx"
      }
    }
  }
}

⚠️ Windows Users: Use cmd /c npx instead of npx:

"command": "cmd",
"args": ["/c", "npx", "-y", "utcp-mcp-gateway"]

Environment Variables

| Variable | Required | Description | |----------|----------|-------------| | MCP_URL | HTTP mode | MCP server URL | | MCP_COMMAND | stdio mode | Command to run MCP | | MCP_ARGS | stdio mode | Arguments (comma-separated) | | MCP_NAME | ✅ | MCP namespace | | MCP_TRANSPORT | No | http (default) or stdio | | LLM_API_KEY | For filtering | Any OpenAI-compatible API key | | LLM_BASE_URL | For filtering | API endpoint (default: OpenAI) | | LLM_MODEL | For filtering | Model name (default: gpt-4o-mini) |

That's it! Restart Claude Desktop and try: "Search for React useState examples"

How It Works

┌──────────────┐     ┌─────────────────────────────────┐     ┌─────────────┐
│   Your AI    │────▶│      utcp-mcp-gateway           │────▶│ Any MCP     │
│ (Claude etc) │     │  ┌─────────┐  ┌─────────────┐   │     │ (Context7)  │
└──────────────┘     │  │  UTCP   │  │ LLM Filter  │   │     └─────────────┘
                     │  │ search  │  │ 10K→300char │   │
                     │  └─────────┘  └─────────────┘   │
                     └─────────────────────────────────┘

Gateway exposes 4 tools to your AI:

| Tool | Parameters | What it does | |------|------------|--------------| | search_tools | query, limit | Find tools by keyword. Returns tools with TypeScript interfaces | | list_tools | - | List all registered tools from connected MCPs | | tool_info | tool_name | Get detailed TypeScript interface for a specific tool | | call_tool_chain | code, timeout, max_output_size, filter_response, purpose | Execute TypeScript code that calls multiple tools in one shot |

Context-Aware Summarization

When using call_tool_chain with filter_response: true, you can provide a purpose parameter to guide the LLM summarization:

call_tool_chain({
  code: "const docs = await context7.context7_get_library_docs({...}); return docs;",
  filter_response: true,
  purpose: "Find React useState usage examples"
})

The LLM will extract only information relevant to your purpose, instead of generic summarization.

Example Flow

User: "How do I use React useState?"

1. AI calls search_tools("react")        → Returns tools with TypeScript interfaces
2. AI calls call_tool_chain with code:
   const id = await context7.context7_resolve_library_id({ libraryName: "react" });
   const docs = await context7.context7_get_library_docs({ libraryId: id, topic: "useState" });
   return docs;
3. Gateway executes code and returns result
4. AI receives structured response

Token Savings Benchmarks

| MCP Service | Original | Filtered | Savings | |-------------|----------|----------|---------| | Context7 (docs) | 10,625 chars | 326 chars | 97% | | DeepWiki (wiki) | 3,318 chars | 400 chars | 88% |

Configuration

Single MCP

MCP_URL=https://mcp.context7.com/mcp
MCP_NAME=context7

Multiple MCPs (Recommended: Numbered Style)

Use numbered environment variables for clear configuration:

{
  "mcpServers": {
    "gateway": {
      "command": "npx",
      "args": ["-y", "utcp-mcp-gateway"],
      "env": {
        "MCP_1_NAME": "context7",
        "MCP_1_URL": "https://mcp.context7.com/mcp",
        
        "MCP_2_NAME": "filesystem",
        "MCP_2_COMMAND": "npx",
        "MCP_2_ARGS": "-y,@anthropic/mcp-server-filesystem,/path/to/dir",
        
        "LLM_API_KEY": "sk-xxx",
        "MAX_RESPONSE_CHARS": "10000"
      }
    }
  }
}

Numbered variables:

  • MCP_1_NAME, MCP_1_URL - First MCP (HTTP mode)
  • MCP_2_NAME, MCP_2_COMMAND, MCP_2_ARGS - Second MCP (stdio mode)
  • Up to MCP_20_* supported

Multiple MCPs (Alternative: Semicolon Style)

{
  "env": {
    "MCP_URL": "https://mcp.context7.com/mcp;https://mcp.deepwiki.com/mcp",
    "MCP_NAME": "context7;deepwiki"
  }
}

LLM Settings

| Variable | Default | Description | |----------|---------|-------------| | LLM_API_KEY | - | OpenAI/OpenRouter API key | | LLM_BASE_URL | OpenAI | Custom endpoint (OpenAI-compatible) | | LLM_MODEL | gpt-4o-mini | Model for summarization | | ENABLE_LLM_FILTER | true | Enable/disable filtering | | MAX_RESPONSE_CHARS | 10000 | Max response length before LLM summarization | | FORCE_LLM_FILTER | false | Force LLM summarization for all responses |

Smart Router Settings (v0.1.20+)

| Variable | Default | Description | |----------|---------|-------------| | ENABLE_LLM_SEARCH | true | Use LLM for intelligent tool search | | ROUTER_MODEL | (uses LLM_MODEL) | Model for tool routing (recommend faster model) |

How it works:

  • list_tools returns brief summaries instead of full schemas (saves tokens)
  • search_tools uses LLM to understand intent and recommend relevant tools (uses full descriptions for accuracy)
  • Falls back to keyword search if LLM is unavailable
  • Fuzzy matching for tool names in case LLM returns slightly different names
  • Pre-truncates large responses (>200k chars) before LLM summarization

OpenAPI Settings (v0.1.24+)

Register REST APIs directly from OpenAPI/Swagger specs:

# Example: APIs.guru (public API directory)
OPENAPI_1_NAME=apisguru
OPENAPI_1_URL=https://api.apis.guru/v2/openapi.yaml

# Example with authentication
OPENAPI_2_NAME=my_api
OPENAPI_2_URL=https://api.example.com/openapi.json
OPENAPI_2_AUTH_TYPE=api-key
OPENAPI_2_AUTH_TOKEN=sk-xxx
OPENAPI_2_AUTH_VAR=X-Api-Key
OPENAPI_2_AUTH_LOCATION=header

| Variable | Required | Description | |----------|----------|-------------| | OPENAPI_N_NAME | ✅ | Tool source name | | OPENAPI_N_URL | ✅ | OpenAPI spec URL | | OPENAPI_N_AUTH_TYPE | ❌ | api-key, bearer, basic, or none | | OPENAPI_N_AUTH_TOKEN | ❌ | Auth token/key | | OPENAPI_N_AUTH_VAR | ❌ | Header name (default: Authorization) | | OPENAPI_N_AUTH_LOCATION | ❌ | header, query, or cookie |

How it works:

  • Downloads and parses OpenAPI 2.0/3.0 specifications
  • Converts each operation to a UTCP tool
  • Tool name = operationId from OpenAPI spec
  • Tool description = summary or description from OpenAPI spec
  • Supports API Key, Bearer, Basic, and OAuth2 authentication

Requirements:

  • Each API operation must have an operationId (operations without it are skipped)
  • File upload parameters (type: file) are not supported by UTCP SDK

故事

我发现 Claude Skills 效果惊人 — Claude 动态发现并只加载需要的技能，避免上下文过载。

但大多数 Agent 框架？一次性把 500+ 工具定义塞进上下文。而且 MCP 响应？经常是 10,000+ 字符的原始数据，白白浪费 Token。

然后我惊喜地发现了 UTCP — 正是我需要的！内置渐进式工具发现。还有 Cloudflare 的 Code Mode？一段代码执行全部。太妙了！

于是我在 UTCP 基础上做了这个：渐进式发现 + Code Mode + LLM 过滤 — 更聪明的 MCP 使用方式。

最棒的是？只需加一段配置。兼容 Claude Desktop、Windsurf、Cursor、Dify 等所有 MCP 客户端。无需额外服务器。

你在找...？

  • ✅ Claude Skills 替代方案 — 用于你的 MCP 配置
  • ✅ 更好的 MCP 工具管理 — 无上下文过载
  • ✅ 渐进式工具发现 — 按需加载工具
  • ✅ OpenAPI 转工具 — 直接从 OpenAPI/Swagger 注册 REST API
  • ✅ MCP Token 优化 — 减少 90%+ Token 浪费
  • ✅ MCP 响应过滤 — 智能摘要

你来对地方了。

这是什么？

LLM 擅长写代码，但不擅长调用工具。

传统 MCP 直接把工具暴露给 LLM — 但 LLM 面临：

  • 工具太多（500+ 定义 = 困惑）
  • 响应太大（10,000+ 字符 = 浪费 Token）
  • 往返太多（15+ 次 API 调用 = 慢且贵）

utcp-mcp-gateway 一次解决所有问题：

| 问题 | 解决方案 | |------|----------| | 500+ 工具定义 | 渐进式发现 — 只加载需要的 | | 10,000+ 字符响应 | LLM 过滤 — 智能摘要（缩小 97%！）| | 15+ 次 API 往返 | Code Mode — 一段代码，一次执行 |

传统方式:   用户 → LLM → 工具1 → LLM → 工具2 → LLM → 工具3 → 结果
            (15+ 次调用, $26/天, 慢)

Code Mode:  用户 → LLM 写代码 → 一次执行全部 → 结果
            (1 次调用, $0.87/天, 快)

结果：每年节省 $9,536 (基准测试来源)

核心功能

| 功能 | 说明 | |------|------| | 🔌 通用 MCP | 连接任意 HTTP 或 stdio MCP | | 📜 OpenAPI 支持 | 直接从 OpenAPI/Swagger 规范注册 REST API | | 🧠 LLM 过滤 | 智能摘要（响应缩小 97%！）| | 🔍 渐进式发现 | search_tools - 按需搜索，无需加载全部 500 个工具 | | ⚡ Code Mode | 一次调用执行 TypeScript 代码链 | | 🔒 安全沙箱 | 代码在隔离环境运行 | | 📦 零配置 | 只需环境变量，无需配置文件 |

快速开始

零配置文件！ 直接添加到 Claude Desktop 配置：

模式 1：HTTP MCP（远程）

{
  "mcpServers": {
    "gateway": {
      "command": "npx",
      "args": ["-y", "utcp-mcp-gateway"],
      "env": {
        "MCP_URL": "https://mcp.context7.com/mcp",
        "MCP_NAME": "context7",
        "LLM_API_KEY": "sk-xxx",
        "LLM_BASE_URL": "https://api.openai.com/v1",
        "LLM_MODEL": "gpt-4o-mini"
      }
    }
  }
}

模式 2：stdio MCP（本地）

{
  "mcpServers": {
    "gateway": {
      "command": "npx",
      "args": ["-y", "utcp-mcp-gateway"],
      "env": {
        "MCP_COMMAND": "npx",
        "MCP_ARGS": "-y,@anthropic/mcp-server-filesystem",
        "MCP_NAME": "filesystem",
        "MCP_TRANSPORT": "stdio",
        "LLM_API_KEY": "sk-xxx"
      }
    }
  }
}

⚠️ Windows 用户： 使用 cmd /c npx 代替 npx：

"command": "cmd",
"args": ["/c", "npx", "-y", "utcp-mcp-gateway"]

环境变量

| 变量 | 必填 | 说明 | |------|------|------| | MCP_URL | HTTP 模式 | MCP 服务器 URL | | MCP_COMMAND | stdio 模式 | 运行 MCP 的命令 | | MCP_ARGS | stdio 模式 | 参数（逗号分隔）| | MCP_NAME | ✅ | MCP 命名空间 | | MCP_TRANSPORT | 否 | http（默认）或 stdio | | LLM_API_KEY | 过滤用 | 任意 OpenAI 兼容的 API Key | | LLM_BASE_URL | 过滤用 | API 端点（默认 OpenAI）| | LLM_MODEL | 过滤用 | 模型名称（默认 gpt-4o-mini）|

配置好后重启 Claude Desktop，试试："搜索 React useState 用法"

工作原理

┌──────────────┐     ┌─────────────────────────────────┐     ┌─────────────┐
│   你的 AI    │────▶│      utcp-mcp-gateway           │────▶│  任意 MCP   │
│ (Claude 等)  │     │  ┌─────────┐  ┌─────────────┐   │     │ (Context7)  │
└──────────────┘     │  │  UTCP   │  │ LLM 过滤器  │   │     └─────────────┘
                     │  │  搜索   │  │ 10K→300字符 │   │
                     │  └─────────┘  └─────────────┘   │
                     └─────────────────────────────────┘

Gateway 向你的 AI 暴露 4 个工具：

| 工具 | 参数 | 作用 | |------|------|------| | search_tools | query, limit | 按关键词搜索工具，返回带 TypeScript 接口的工具列表 | | list_tools | - | 列出所有已注册的工具 | | tool_info | tool_name | 获取特定工具的详细 TypeScript 接口 | | call_tool_chain | code, timeout, max_output_size, filter_response, purpose | 执行 TypeScript 代码，一次调用多个工具 |

上下文感知摘要

使用 call_tool_chain 时，设置 filter_response: true 并提供 purpose 参数，LLM 会根据你的目的智能提取相关信息：

call_tool_chain({
  code: "const docs = await context7.context7_get_library_docs({...}); return docs;",
  filter_response: true,
  purpose: "查找 React useState 的用法示例"
})

LLM 会只提取与你目的相关的信息，而不是泛泛的摘要。

调用流程示例

用户: "React useState 怎么用？"

1. AI 调用 search_tools("react")        → 返回带 TypeScript 接口的工具
2. AI 调用 call_tool_chain 执行代码：
   const id = await context7.context7_resolve_library_id({ libraryName: "react" });
   const docs = await context7.context7_get_library_docs({ libraryId: id, topic: "useState" });
   return docs;
3. Gateway 执行代码并返回结果
4. AI 收到结构化响应

Token 节省实测

| MCP 服务 | 原始响应 | 过滤后 | 节省 | |----------|----------|--------|------| | Context7 | 10,625 字符 | 326 字符 | 97% | | DeepWiki | 3,318 字符 | 400 字符 | 88% |

配置说明

单个 MCP

MCP_URL=https://mcp.context7.com/mcp
MCP_NAME=context7

多个 MCP（推荐：编号方式）

使用编号环境变量进行清晰配置：

{
  "mcpServers": {
    "gateway": {
      "command": "npx",
      "args": ["-y", "utcp-mcp-gateway"],
      "env": {
        "MCP_1_NAME": "context7",
        "MCP_1_URL": "https://mcp.context7.com/mcp",
        
        "MCP_2_NAME": "filesystem",
        "MCP_2_COMMAND": "npx",
        "MCP_2_ARGS": "-y,@anthropic/mcp-server-filesystem,/path/to/dir",
        
        "LLM_API_KEY": "sk-xxx",
        "MAX_RESPONSE_CHARS": "10000"
      }
    }
  }
}

编号变量：

  • MCP_1_NAME, MCP_1_URL - 第一个 MCP（HTTP 模式）
  • MCP_2_NAME, MCP_2_COMMAND, MCP_2_ARGS - 第二个 MCP（stdio 模式）
  • 最多支持 MCP_20_*

多个 MCP（备选：分号方式）

{
  "env": {
    "MCP_URL": "https://mcp.context7.com/mcp;https://mcp.deepwiki.com/mcp",
    "MCP_NAME": "context7;deepwiki"
  }
}

LLM 配置

| 变量 | 默认值 | 说明 | |------|--------|------| | LLM_API_KEY | - | OpenAI/OpenRouter API 密钥 | | LLM_BASE_URL | OpenAI | 自定义端点（兼容 OpenAI 格式）| | LLM_MODEL | gpt-4o-mini | 摘要用的模型 | | ENABLE_LLM_FILTER | true | 开启/关闭过滤 | | MAX_RESPONSE_CHARS | 10000 | 超过此长度时使用 LLM 摘要 | | FORCE_LLM_FILTER | false | 强制所有响应都经过 LLM 摘要 |

智能路由配置 (v0.1.20+)

| 变量 | 默认值 | 说明 | |------|--------|------| | ENABLE_LLM_SEARCH | true | 使用 LLM 智能搜索工具 | | ROUTER_MODEL | (复用 LLM_MODEL) | 路由用的模型（建议用更快的模型）|

工作原理：

  • list_tools 返回精简摘要而非完整 schema（节省 token）
  • search_tools 使用 LLM 理解意图并推荐相关工具（使用全量描述确保准确性）
  • LLM 不可用时回退到关键词搜索
  • 工具名模糊匹配，容错 LLM 返回格式不完全一致的情况
  • 超大响应（>200k 字符）预截断后再进行 LLM 摘要

OpenAPI 配置 (v0.1.24+)

直接从 OpenAPI/Swagger 规范注册 REST API：

# 示例：APIs.guru（公开的 API 目录）
OPENAPI_1_NAME=apisguru
OPENAPI_1_URL=https://api.apis.guru/v2/openapi.yaml

# 带认证的示例
OPENAPI_2_NAME=my_api
OPENAPI_2_URL=https://api.example.com/openapi.json
OPENAPI_2_AUTH_TYPE=api-key
OPENAPI_2_AUTH_TOKEN=sk-xxx
OPENAPI_2_AUTH_VAR=X-Api-Key
OPENAPI_2_AUTH_LOCATION=header

| 变量 | 必填 | 说明 | |------|------|------| | OPENAPI_N_NAME | ✅ | 工具源名称 | | OPENAPI_N_URL | ✅ | OpenAPI 规范地址 | | OPENAPI_N_AUTH_TYPE | ❌ | api-key、bearer、basic 或 none | | OPENAPI_N_AUTH_TOKEN | ❌ | 认证令牌/密钥 | | OPENAPI_N_AUTH_VAR | ❌ | 头名称（默认 Authorization）| | OPENAPI_N_AUTH_LOCATION | ❌ | header、query 或 cookie |

工作原理：

  • 下载并解析 OpenAPI 2.0/3.0 规范
  • 每个操作转换为一个 UTCP 工具
  • 工具名 = OpenAPI 规范中的 operationId
  • 工具描述 = OpenAPI 规范中的 summary 或 description
  • 支持 API Key、Bearer、Basic 和 OAuth2 认证

注意事项：

  • API 操作必须有 operationId（没有的会被跳过）
  • 文件上传参数（type: file）不被 UTCP SDK 支持

Credits / 致谢

This project is built on top of amazing open-source work:

License

MIT © 2025 reinn

Quick Setup
Installation guide for this server

Install Package (if required)

npx @modelcontextprotocol/server-utcp-mcp-gateway

Cursor configuration (mcp.json)

{ "mcpServers": { "xuleileon-utcp-mcp-gateway": { "command": "npx", "args": [ "xuleileon-utcp-mcp-gateway" ] } } }