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Peeper MCP Server

@maskedsaqib

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maskedsaqib

Peeper MCP Server について

Model Control Panel server for Peeper application

Peeper MCP Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

Peeper MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Peeper MCP Server?

Peeper MCP Server is a Model Control Panel (MCP) server that provides a unified API to interact with various language models. It is designed for developers who need a single endpoint for model discovery and text completion across different AI providers.

How to use Peeper MCP Server?

Clone the repository, run npm install, copy .env.example to .env, add your API keys, then start the server with npm start (or npm run dev for auto‑restart). The server exposes two endpoints: GET /api/models to list available models and POST /api/completion to generate text completions.

Key features of Peeper MCP Server

  • Unified API for different AI model providers
  • Model discovery endpoint (/api/models)
  • Text completion endpoint (/api/completion)
  • Easy to extend for additional models

Use cases of Peeper MCP Server

  • Building a single backend gateway for multiple language models
  • Rapidly prototyping AI‑powered applications with switchable providers
  • Centralizing model access and management across a team
  • Experimenting with different models without changing client code

よくある質問

1

What does Peeper MCP Server provide that alternatives do not?

It offers a unified API layer for multiple AI model providers, allowing you to switch models without rewriting integration code.

2

What are the runtime requirements?

Node.js and npm. The server is installed via `npm install` and run with `npm start`.

3

Where does my data live?

The server runs locally on your machine; no data is sent to a third‑party except to the model providers when you make completion requests.

4

Are there any known limitations?

The README does not mention specific limits, but you must provide your own API keys for the models you wish to use.

5

What authentication is required?

Authentication is handled via API keys stored in a `.env` file; the server itself does not implement additional auth for its own endpoints.

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