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team-evolve-http-mcpserver

team-evolve-http-mcpserver

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github.com/qianping-saraDeveloper Tools0Updated 81w ago

About team-evolve-http-mcpserver

team-evolve-http-mcpserver is an MCP server project built with mcp-framework. It provides an example tool and a starter structure for developers adding tools and building a server for use with MCP clients such as Claude Desktop.

Key features

  1. 01Built with mcp-framework
  2. 02Example tool in TypeScript
  3. 03CLI-based tool scaffolding
  4. 04Build with npm scripts
  5. 05Claude Desktop configuration guidance

Use cases

  1. 01Start an MCP server project
  2. 02Add a custom MCP tool
  3. 03Build and test a server locally
  4. 04Configure the server for Claude Desktop

MCP support

Implements an MCP server using mcp-framework and documents how to connect it to Claude Desktop or run it through npx after publishing.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

team-evolve-http-mcpserver

A Model Context Protocol (MCP) server built with mcp-framework.

Quick Start

# Install dependencies
npm install

# Build the project
npm run build

Project Structure

team-evolve-http-mcpserver/
├── src/
│   ├── tools/        # MCP Tools
│   │   └── ExampleTool.ts
│   └── index.ts      # Server entry point
├── package.json
└── tsconfig.json

Adding Components

The project comes with an example tool in src/tools/ExampleTool.ts. You can add more tools using the CLI:

# Add a new tool
mcp add tool my-tool

# Example tools you might create:
mcp add tool data-processor
mcp add tool api-client
mcp add tool file-handler

Tool Development

Example tool structure:

import { MCPTool } from "mcp-framework";
import { z } from "zod";

interface MyToolInput {
  message: string;
}

class MyTool extends MCPTool<MyToolInput> {
  name = "my_tool";
  description = "Describes what your tool does";

  schema = {
    message: {
      type: z.string(),
      description: "Description of this input parameter",
    },
  };

  async execute(input: MyToolInput) {
    // Your tool logic here
    return `Processed: ${input.message}`;
  }
}

export default MyTool;

Publishing to npm

  1. Update your package.json:

    • Ensure name is unique and follows npm naming conventions
    • Set appropriate version
    • Add description, author, license, etc.
    • Check bin points to the correct entry file
  2. Build and test locally:

    npm run build
    npm link
    team-evolve-http-mcpserver  # Test your CLI locally
    
  3. Login to npm (create account if necessary):

    npm login
    
  4. Publish your package:

    npm publish
    

After publishing, users can add it to their claude desktop client (read below) or run it with npx


## Using with Claude Desktop

### Local Development

Add this configuration to your Claude Desktop config file:

**MacOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`
**Windows**: `%APPDATA%/Claude/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "team-evolve-http-mcpserver": {
      "command": "node",
      "args":["/absolute/path/to/team-evolve-http-mcpserver/dist/index.js"]
    }
  }
}
After Publishing

Add this configuration to your Claude Desktop config file:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "team-evolve-http-mcpserver": {
      "command": "npx",
      "args": ["team-evolve-http-mcpserver"]
    }
  }
}

Building and Testing

  1. Make changes to your tools
  2. Run npm run build to compile
  3. The server will automatically load your tools on startup

Learn More

Comments

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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.