Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@enterprise-knowledge-mcp search the knowledge base for our remote work policy"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An enterprise knowledge management service based on Model Context Protocol (MCP).
Provides three core capabilities for large language models:
Tool | Description |
| Semantic search of the enterprise knowledge base |
| Retrieve the full document by document ID |
| Search enterprise project cases |
enterprise-knowledge-mcp/
├── src/
│ └── enterprise_knowledge_mcp/
│ ├── __init__.py # 包入口
│ ├── server.py # MCP 工具注册（协议层）
│ └── retriever.py # 检索逻辑（业务层）
├── tests/
│ ├── test_server.py # 单元测试
│ └── test_e2e.py # MCP Client 端到端测试
├── scripts/
│ ├── seed_data.py # 导入示例数据到 ChromaDB
│ └── download_model.py # 下载嵌入模型（离线环境用）
├── pyproject.toml
├── server.json # MCP 服务描述清单
├── README.md
└── LICENSERelated MCP server: Innovaas KMS MCP Server
┌─────────────────────────────────────────┐
│ server.py (协议层) │
│ - 注册 MCP Tool │
│ - 参数校验 & 输出格式化 │
└──────────────┬──────────────────────────┘
│ 调用
┌──────────────▼──────────────────────────┐
│ retriever.py (业务层) │
│ - KnowledgeRetriever 知识库检索 │
│ - DocumentRetriever 文档检索 │
│ - CaseRetriever 案例检索 │
└──────────────┬──────────────────────────┘
│ 替换实现
┌──────────────▼──────────────────────────┐
│ 数据层（可插拔） │
│ - MockRetriever 开发/测试用 │
│ - ChromaRetriever ChromaDB 向量检索 │
│ - 自定义 Retriever ES / 数据库 / ... │
└─────────────────────────────────────────┘The retriever adopts a pluggable design of base class + implementation. In server.py, you only need to replace the instantiated object to switch backends; the tool registration code requires no changes.
# 克隆仓库
git clone https://github.com/your-username/enterprise-knowledge-mcp.git
cd enterprise-knowledge-mcp
# 创建虚拟环境
python -m venv .venv
.venv\Scripts\activate # Windows
# source .venv/bin/activate # Linux/macOS
# 安装（开发模式）
pip install -e ".[dev]"# 导入示例企业知识数据（知识库 5 条、文档 3 篇、案例 4 个）
python scripts/seed_data.py# Mock 模式（无需 ChromaDB 数据）
python -m enterprise_knowledge_mcp.server
# ChromaDB 模式（需先执行 seed_data.py）
RETRIEVER_BACKEND=chroma python -m enterprise_knowledge_mcp.serverpytest tests/ -vAdd the following to claude_desktop_config.json:
{
"mcpServers": {
"enterprise-knowledge": {
"command": "python",
"args": ["-m", "enterprise_knowledge_mcp.server"],
"cwd": "C:/path/to/enterprise-knowledge-mcp"
}
}
}from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
server_params = StdioServerParameters(
command="python",
args=["-m", "enterprise_knowledge_mcp.server"],
)
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
result = await session.call_tool(
"search_knowledge", {"query": "人工智能"}
)
print(result.content[0].text)Inherit the base class and implement the retrieve / get_document / search_cases methods:
from enterprise_knowledge_mcp.retriever import (
KnowledgeRetriever,
SearchResult,
)
class MyRetriever(KnowledgeRetriever):
def retrieve(self, query: str, top_k: int = 5) -> list[SearchResult]:
# 你的检索逻辑
return [SearchResult(text="...", source="...", score=0.9)]Then replace it in server.py:
from .retriever import MyRetriever
knowledge_retriever = MyRetriever()This server cannot be deployed
Make your knowledge agent-ready. One MCP endpoint, 5 connectors, 3 search modes.
Knowledge base MCP for AI agents on iknow.dev. Search, read, and maintain via OAuth.
Knowledge coverage map and health score. Ingest docs into a governed knowledge graph via MCP.
Governed company knowledge over MCP. Access by contract for authorized tenants.
