MCP Waifu Queue (Gemini Edition)

@waifuai

About MCP Waifu Queue (Gemini Edition)

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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

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

Overview

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.

FAQ from MCP Waifu Queue (Gemini Edition)

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.

How do I get an OpenRouter API key?

Obtain a key from openrouter.ai and set it as the OPENROUTER_API_KEY environment variable or place it in ~/.api-openrouter.

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.

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.

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.

Frequently asked questions

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.

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`.

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.

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.

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.

Comments

Basic information

More AI & Agents MCP servers

Perplexity Ask MCP Server

ppl-ai

The official MCP server implementation for the Perplexity API Platform

2.4K

欢迎来到智言平台

Shy2593666979

AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

797