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SAG MCP Server

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Remote

SAG MCP Server

Python 3.10+ License: MIT MCP

English | 中文

Full read/write MCP server for SAG knowledge base. Exposes 18 tools over Streamable HTTP transport — compatible with Claude Desktop, Cursor, VS Code, and any MCP client.

Features

  • 18 tools: source/document CRUD, semantic search, knowledge graph queries, model config

  • Streamable HTTP: connect from any MCP client without local dependencies

  • Bearer auth: JWT token authentication built-in

  • Zero config: works with a running SAG instance out of the box

Related MCP server: LightRAG MCP Server

Quick Start

1. Prerequisites

A running SAG instance (Docker or native). See SAG deployment guide.

2. Install

pip install sag-mcp-server

Or from source:

git clone https://github.com/Zleap-AI/sag-mcp-server.git
cd sag-mcp-server
pip install -e .

3. Run

HTTP mode (recommended):

export SAG_API_URL=http://your-sag-host:9002
sag-mcp-server http 9003

stdio mode (for local integration):

sag-mcp-server stdio

4. Connect from Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "sag": {
      "type": "http",
      "url": "http://your-sag-host:9003/mcp/",
      "headers": {
        "Authorization": "Bearer <your-jwt-token>"
      }
    }
  }
}

Get a token:

curl -s -X POST http://your-sag-host:9002/api/v1/auth/login \
  -H 'Content-Type: application/json' -d '{"name":"Admin"}' \
  | python3 -c "import sys,json;print(json.load(sys.stdin)['access_token'])"

Tools

Category

Tool

Description

Sources

list_sources

List all knowledge base sources

create_source

Create a new source

delete_source

Delete a source and all its data

Documents

list_documents

List documents in a source

upload_document

Upload a local file (auto-parses + extracts)

ingest_text

Write text directly (no file needed)

reprocess_document

Re-process a document

pause_document

Pause processing

resume_document

Resume processing

delete_document

Delete a document

Retrieval

search

Semantic search with LLM summary

get_entity

Query knowledge graph entity

outline

Get document outline

grep

Keyword/regex search

read_document

Read full document text

Config

get_model_config

View model configuration

update_model_config

Update model settings

test_model_config

Test model connectivity

Configuration

All configuration via environment variables:

Variable

Default

Description

SAG_API_URL

http://localhost:9002

SAG API base URL

SAG_TOKEN

(empty = auto-login)

JWT Bearer token

SAG_LOGIN_NAME

Admin

Login username (when token is empty)

SAG_MCP_PORT

9003

HTTP server port

Client Configuration

Claude Desktop

{
  "mcpServers": {
    "sag": {
      "type": "http",
      "url": "http://your-sag-host:9003/mcp/",
      "headers": { "Authorization": "Bearer <token>" }
    }
  }
}

Cursor

~/.cursor/mcp.json:

{
  "mcpServers": {
    "sag": {
      "type": "http",
      "url": "http://your-sag-host:9003/mcp/",
      "headers": { "Authorization": "Bearer <token>" }
    }
  }
}

Dify

  • Type: Streamable HTTP

  • URL: http://your-sag-host:9003/mcp/

  • Header: Authorization: Bearer <token>

Docker

Run as a container alongside SAG:

# docker-compose.yml addition
services:
  mcp:
    build: .
    environment:
      SAG_API_URL: http://api:8000
      SAG_SECRET_KEY: ${SAG_SECRET_KEY}
    ports:
      - "9003:9003"
    command: ["sag-mcp-server", "http", "9003"]
    depends_on:
      api:
        condition: service_healthy

Development

git clone https://github.com/Zleap-AI/sag-mcp-server.git
cd sag-mcp-server
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check src/

API Compatibility

Requires SAG v0.7.x+ with the following endpoints:

  • POST /api/v1/auth/login — JWT authentication

  • GET/POST/DELETE /api/v1/sources — Source CRUD

  • GET/POST/DELETE /api/v1/sources/{id}/documents — Document CRUD

  • POST /api/v1/search — Semantic search

  • GET /api/v1/system/model-config — Model configuration

License

MIT License. See LICENSE for details.

中文文档

English | 中文

基于 SAG 知识库的读写 MCP Server。通过 Streamable HTTP 暴露 18 个工具，兼容 Claude Desktop、Cursor、VS Code 及所有 MCP 客户端。

特性

  • 18 个工具：信源/文档增删改查、语义检索、知识图谱查询、模型配置

  • Streamable HTTP：无需本地安装任何依赖，客户端通过 HTTP 直连

  • Bearer 鉴权：内置 JWT token 认证

  • 零配置：与运行中的 SAG 实例直接配合使用

快速开始

1. 前提条件

运行中的 SAG 实例（Docker 或原生部署）。参见 SAG 部署指南。

2. 安装

pip install sag-mcp-server

或从源码安装：

git clone https://github.com/Zleap-AI/sag-mcp-server.git
cd sag-mcp-server
pip install -e .

3. 启动

HTTP 模式（推荐）：

export SAG_API_URL=http://your-sag-host:9002
sag-mcp-server http 9003

stdio 模式（本地集成）：

sag-mcp-server stdio

4. 连接 Claude Desktop

编辑 claude_desktop_config.json：

{
  "mcpServers": {
    "sag": {
      "type": "http",
      "url": "http://your-sag-host:9003/mcp/",
      "headers": {
        "Authorization": "Bearer <your-jwt-token>"
      }
    }
  }
}

获取 token：

curl -s -X POST http://your-sag-host:9002/api/v1/auth/login \
  -H 'Content-Type: application/json' -d '{"name":"Admin"}' \
  | python3 -c "import sys,json;print(json.load(sys.stdin)['access_token'])"

工具清单

类别

工具

说明

信源

list_sources

列出所有知识库信源

create_source

创建新信源

delete_source

删除信源及全部数据

文档

list_documents

列出信源下文档

upload_document

上传本地文件（自动解析+抽取）

ingest_text

直接写入文本（无需文件）

reprocess_document

重新处理文档

pause_document

暂停处理

resume_document

恢复处理

delete_document

删除文档

检索

search

语义检索（含 LLM 摘要）

get_entity

查询知识图谱实体

outline

获取文档大纲

grep

关键词/正则检索

read_document

读取文档全文

模型

get_model_config

查看模型配置

update_model_config

更新模型设置

test_model_config

测试模型连通性

配置说明

所有配置通过环境变量：

变量

默认值

说明

SAG_API_URL

http://localhost:9002

SAG API 地址

SAG_TOKEN

（空=自动登录）

JWT Bearer token

SAG_LOGIN_NAME

Admin

登录用户名（token 为空时生效）

SAG_MCP_PORT

9003

HTTP 服务端口

客户端配置

Claude Desktop

{
  "mcpServers": {
    "sag": {
      "type": "http",
      "url": "http://your-sag-host:9003/mcp/",
      "headers": { "Authorization": "Bearer <token>" }
    }
  }
}

Cursor

~/.cursor/mcp.json：

{
  "mcpServers": {
    "sag": {
      "type": "http",
      "url": "http://your-sag-host:9003/mcp/",
      "headers": { "Authorization": "Bearer <token>" }
    }
  }
}

Dify

  • 类型：Streamable HTTP

  • URL：http://your-sag-host:9003/mcp/

  • 请求头：Authorization: Bearer <token>

Docker 部署

与 SAG 一起以容器方式运行：

# 添加到 docker-compose.yml
services:
  mcp:
    build: .
    environment:
      SAG_API_URL: http://api:8000
      SAG_SECRET_KEY: ${SAG_SECRET_KEY}
    ports:
      - "9003:9003"
    command: ["sag-mcp-server", "http", "9003"]
    depends_on:
      api:
        condition: service_healthy

开发指南

git clone https://github.com/Zleap-AI/sag-mcp-server.git
cd sag-mcp-server
pip install -e ".[dev]"

# 运行测试
pytest

# 代码检查
ruff check src/

API 兼容性

需要 SAG v0.7.x+，支持以下接口：

  • POST /api/v1/auth/login — JWT 认证

  • GET/POST/DELETE /api/v1/sources — 信源增删改查

  • GET/POST/DELETE /api/v1/sources/{id}/documents — 文档增删改查

  • POST /api/v1/search — 语义检索

  • GET /api/v1/system/model-config — 模型配置

许可证

MIT License。详见 LICENSE。

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