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MCP Waifu Queue (Gemini Edition)

@waifuai

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT-0

传输方式

stdio

作者

waifuai

关于 MCP Waifu Queue

FastMCP server for asynchronous conversational AI using OpenRouter API via Redis queue/worker architecture. GPU inference support, environment-variable configuration, and deployment-ready setup.

#mcp#waifu#queue#conversational-ai#text-generation

MCP Waifu Queue 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-waifu-queue": {
      "command": "python",
      "args": [
        "-m",
        "uv",
        "venv",
        ".venv"
      ]
    }
  }
}

MCP Waifu Queue 工具

1

** Sends a text generation request to the OpenRouter API via the background queue.

概览

What is MCP Waifu Queue (Gemini Edition)?

MCP Waifu Queue (Gemini Edition) is an MCP server for a conversational AI “waifu” character. It uses the OpenRouter API (via a Redis queue) for asynchronous text generation and is built with the FastMCP library.

How to use MCP Waifu Queue (Gemini Edition)?

Install dependencies and configure OpenRouter API key and model selection (via environment variables or home‑directory files). Start a Redis server, then run the RQ worker (python -m mcp_waifu_queue.worker) and the MCP server (e.g., with uvicorn mcp_waifu_queue.main:app). Clients interact with the generate_text tool and the job://{job_id} resource.

Key features of MCP Waifu Queue (Gemini Edition)

  • Text generation via OpenRouter using a configurable model.
  • Request queuing with Redis for asynchronous processing.
  • MCP‑compliant API using the FastMCP library.
  • Job status tracking through MCP resources.
  • API key loading from environment variables or file fallbacks.
  • Pydantic‑based request and response validation.

Use cases of MCP Waifu Queue (Gemini Edition)

  • Building a conversational AI assistant with a predefined character.
  • Queuing long‑running text generation requests without blocking the client.
  • Integrating a waifu chatbot into MCP‑compatible tools and agents.
  • Offloading generation workloads to a background worker for scalability.

常见问题

1

What models can be used?

The server uses the model specified in `~/.model-openrouter` or defaults to `openrouter/free`. You can use any model available via the OpenRouter API.

2

How do I get an OpenRouter API key?

Obtain a key from [openrouter.ai](https://openrouter.ai/) and set it as the `OPENROUTER_API_KEY` environment variable or place it in `~/.api-openrouter`.

3

What is Redis used for?

Redis is required to queue text generation requests. The RQ worker picks up jobs from the queue and processes them asynchronously.

4

How can I check the status of a submitted job?

After calling the `generate_text` tool, you receive a `job_id`. Use the `job://{job_id}` MCP resource to retrieve the job status and result.

5

What are the runtime dependencies?

Python 3.7+, a running Redis server, and the packages listed in `requirements.txt`. The server also needs an OpenRouter API key and network access to OpenRouter.

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