Tianji MCP Server

What does “Tianji” MCP Server do?

The Tianji MCP (Model Context Protocol) Server is designed to connect AI assistants with external data sources, APIs, or services, thereby enhancing development workflows and enabling more dynamic AI capabilities. By acting as a bridge between AI models and real-world resources, Tianji MCP Server empowers AI systems to perform a range of tasks, such as executing database queries, managing files, or interacting with various APIs. This facilitates seamless integration of external data and functionalities into AI-driven applications, making it easier for developers to build intelligent systems that require up-to-date information, automation, or operational context from outside sources.

List of Prompts

No prompt templates were specified in the available files or documentation.

List of Resources

No explicit resources were listed in the available documentation or files.

List of Tools

No tools were detailed in the server.py or the available files in the repository path provided.

Use Cases of this MCP Server

No specific use cases were described in the available repository documentation.

How to set it up

Windsurf

1. Ensure Node.js and npm are installed.
2. Locate the Windsurf configuration file (e.g., windsurf.config.json).
3. Add the Tianji MCP Server to the mcpServers object:

   {
     "mcpServers": {
       "tianji": {
         "command": "npx",
         "args": ["@tianji/mcp-server@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking the MCP server connection status.

Claude

1. Ensure Node.js is installed.
2. Open Claude’s configuration file.
3. Insert the Tianji MCP Server configuration:

   {
     "mcpServers": {
       "tianji": {
         "command": "npx",
         "args": ["@tianji/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Check the logs to confirm successful connection.

Cursor

1. Install Node.js if not already present.
2. Find the cursor.config.json or relevant config file.
3. Add Tianji MCP Server:

   {
     "mcpServers": {
       "tianji": {
         "command": "npx",
         "args": ["@tianji/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Validate integration via the UI or logs.

Cline

1. Make sure Node.js is installed.
2. Open the Cline configuration file.
3. Add the following JSON snippet:

   {
     "mcpServers": {
       "tianji": {
         "command": "npx",
         "args": ["@tianji/mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Cline.
5. Confirm the MCP server is running.

Securing API Keys

Use environment variables to manage secrets. Example configuration:

{
  "mcpServers": {
    "tianji": {
      "command": "npx",
      "args": ["@tianji/mcp-server@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "apiKey": "${API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{ “tianji”: { “transport”: “streamable_http”, “url”: “https://yourmcpserver.example/pathtothemcp/url" } }

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “tianji” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above, the Tianji MCP Server appears to be in an early or undocumented state regarding MCP-specific features such as prompts, resources, and tools. Its setup instructions are clear, but its practical capabilities are not documented in the examined code or README.

MCP Score

I would rate this MCP server a 2/10 based on the available documentation and feature completeness, as it lacks practical details or examples for users and developers, and there is no evidence of implemented MCP primitives or tools in the provided files.