About Higress OPS MCP Server
Higress OPS MCP Server is an MCP server implementation for configuring and managing Higress. The repository also includes an MCP client built on LangGraph and LangChain MCP Adapters that interacts with the server through an agent flow. It is aimed at developers who manage Higress through MCP.
Key features
- 01Configures and manages Higress through MCP
- 02Tool classes for routes, service sources and more
- 03Human confirmation for sensitive write operations
- 04Example client built on LangGraph
Use cases
- 01Managing Higress routes and service sources from an MCP client
- 02Calling Higress configuration tools in an agent flow
- 03Extending the server with new Higress management tools
MCP support
The project is an MCP server that provides tools for configuring and managing Higress to MCP clients. It also includes an example MCP client built on LangGraph and LangChain MCP Adapters.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEHigress OPS MCP Server
A Model Context Protocol (MCP) server implementation that enables comprehensive configuration and management of Higress. This repository also provides an MCP client built on top of LangGraph and LangChain MCP Adapters, facilitating interaction with the Higress MCP Server through a well-designed agent flow architecture.
Demo
https://github.com/user-attachments/assets/bae66b77-a158-452e-9196-98060bac0df7
Config Environment Variables
Copy the .env.example file to .env and fill in the corresponding values.
Start MCP Client and MCP Server
In stdio mode, the MCP server process is started by the MCP client program. Run the following command to start the MCP client and MCP server:
uv run client.py
Add a new tool
Step 1: Create a new tool class or extend an existing one
- Create a new file in the tools directory if adding a completely new tool category
- Or add your tool to an existing class if it fits an existing category
from typing import Dict, List, Any
from fastmcp import FastMCP
class YourTools:
def register_tools(self, mcp: FastMCP):
@mcp.tool()
async def your_tool_function(arg1: str, arg2: int) -> List[Dict]:
"""
Your tool description.
Args:
arg1: Description of arg1
arg2: Description of arg2
Returns:
Description of the return value
Raises:
ValueError: If the request fails
"""
# Implementation using self.higress_client to make API calls
return self.higress_client.your_api_method(arg1, arg2)
Step 2: Add a new method to HigressClient if your tool needs to interact with the Higress Console API
- Add methods to utils/higress_client.py that encapsulate API calls
- Use the existing HTTP methods (get, put, post) for actual API communication
def your_api_method(self, arg1: str, arg2: int) -> List[Dict]:
"""
Description of what this API method does.
Args:
arg1: Description of arg1
arg2: Description of arg2
Returns:
Response data
Raises:
ValueError: If the request fails
"""
path = "/v1/your/api/endpoint"
data = {"arg1": arg1, "arg2": arg2}
return self.put(path, data) # or self.get(path) or self.post(path, data)
Step 3: Register your tool class in the server
- Add your tool class to the tool_classes list in server.py
- This list is used by ToolsRegister to instantiate and register all tools
- The ToolsRegister will automatically set logger and higress_client attributes
tool_classes = [
CommonTools,
RequestBlockTools,
RouteTools,
ServiceSourceTools,
YourTools # Add your tool class here
]
Step 4: Add your tool to SENSITIVE_TOOLS if it requires human confirmation
- Tools in this list will require human confirmation before execution
# Define write operations that require human confirmation
SENSITIVE_TOOLS = [
"add_route",
"add_service_source",
"update_route",
"update_request_block_plugin",
"update_service_source",
"your_tool_function" # Add your tool name here if it requires confirmation
]
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.