project

内容来源：README.md（说明文档） · 原始地址 · 查看安装指南

原始内容

# Project Overview

MCP servers + OpenAI agents.

## Setting up the Environment

1. **Create and Activate a Virtual Environment**:
   - Run the provided `setup.sh` script to establish a virtual environment using Python 3.13.
     ```bash
     ./setup.sh
     ```
   - The script creates a virtual environment and installs all required Python packages from `requirements.txt`.

2. **Install Project Dependencies**:
   - Ensure you have the necessary external tools and services for the MCP servers you intend to run:
     - **Lynx**: Required for the `lynx` server, which interfaces with the Lynx terminal web search tool.
     - **Docker**: Required for the `puppeteer` and `github` servers, enabling Chrome control and GitHub interactions, respectively.
     - **Node.js**: Required for the `fs` server, using the `npx` command.


# Usage Guide

## Running `agent.py`

### Basic Execution

- You can run the agent using all the servers you have configured in `servers.yaml` like this

  ```bash
  python agent.py
  ```

- You can choose which servers the agent will have access to with:

  ```bash
  # gives agent access to file system (for local file browsing) and lynx (for websearch)
  python agent.py --servers fs lynx
  ```

  In my experience this results in better performance since the agent is less likely to pick the wrong tool

- **Debug Mode**:

  - Use the `--debug` flag to initialize servers without executing the agent. This is useful if you want to make sure the servers are set up correctly
    ```bash
    python agent.py --debug
    ```

### Configuration

The servers are configured in `servers.yaml`, where each server entry includes:

- **Name**: Identifier for the server.
- **Description**: Brief description of the server's functionality.
- **Command & Arguments**: Command-line instructions to start the server.
- **Environment Variables**: Any additional environment configurations needed.

You'll typically find these in github repos for MCP servers

### Roll your own
You can roll your own MCP server in the `mcp-servers` directory

# What do I use this for?
## Link finding
If I am trying to figure out how to do something (like setting up QEMU) I will often use `agent.py` to do the initial research for me

```bash
"find links relevant to QEMU setup on ubuntu" | python agent.py --servers lynx
```

### MCP notes...
#### High level
Host: Programs (Desktop, IDE, AI tool) that wants to use MCP
Client: Client that maintains 1:1 connection with servers
Server: Server exposing MCP capabilities

Project Overview

MCP servers + OpenAI agents.

Setting up the Environment

Create and Activate a Virtual Environment:
- Run the provided setup.sh script to establish a virtual environment using Python 3.13.
```
./setup.sh
```
- The script creates a virtual environment and installs all required Python packages from requirements.txt.
Install Project Dependencies:
- Ensure you have the necessary external tools and services for the MCP servers you intend to run:
  - Lynx: Required for the lynx server, which interfaces with the Lynx terminal web search tool.
  - Docker: Required for the puppeteer and github servers, enabling Chrome control and GitHub interactions, respectively.
  - Node.js: Required for the fs server, using the npx command.

Usage Guide

Running `agent.py`

Basic Execution

You can run the agent using all the servers you have configured in servers.yaml like this
```
python agent.py
```
You can choose which servers the agent will have access to with:
```
# gives agent access to file system (for local file browsing) and lynx (for websearch)
python agent.py --servers fs lynx
```
In my experience this results in better performance since the agent is less likely to pick the wrong tool
Debug Mode:
- Use the --debug flag to initialize servers without executing the agent. This is useful if you want to make sure the servers are set up correctly
```
python agent.py --debug
```

Configuration

The servers are configured in servers.yaml, where each server entry includes:

Name: Identifier for the server.
Description: Brief description of the server's functionality.
Command & Arguments: Command-line instructions to start the server.
Environment Variables: Any additional environment configurations needed.

You'll typically find these in github repos for MCP servers

Roll your own

You can roll your own MCP server in the mcp-servers directory

What do I use this for?

Link finding

If I am trying to figure out how to do something (like setting up QEMU) I will often use agent.py to do the initial research for me

"find links relevant to QEMU setup on ubuntu" | python agent.py --servers lynx

MCP notes...

High level

Host: Programs (Desktop, IDE, AI tool) that wants to use MCP Client: Client that maintains 1:1 connection with servers Server: Server exposing MCP capabilities