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MCPolly MCP Server

@MCPolly

リポジトリ

基本情報

カテゴリ

開発者ツール

トランスポート

stdio

作者

MCPolly

投稿者

Nisarga Patel

MCPolly MCP Server について

Agent-native observability for AI agents. MCP server with web dashboard, alerting, and semantic search. Built with Rust, Axum, SQLite, and HTMX.

MCPolly MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcpolly": {
      "url": "http://localhost:3000/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key-here"
      }
    }
  }
}

MCPolly 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。

上のボタンでサーバーのエンドポイントに接続し、ツール一覧を取得できます。

概要

What is MCPolly?

MCPolly is an MCP (Model Context Protocol) server that allows AI agents to report their progress, errors, and state in real time. It provides a unified web dashboard for humans to monitor all agents, and includes a knowledge layer for indexing and semantically searching product documents. Built with Rust, Axum, SQLite, and HTMX, it is designed for self-hosting on minimal hardware.

How to use MCPolly?

Install via the install script or build from source, then start the server with PORT=3000 RUST_LOG=mcpolly=info ./mcpolly. On first run, a setup wizard in the browser provides a default API key. Configure your AI agent platform (e.g., Cursor, Claude Code) to connect to the server’s /mcp endpoint using HTTP Bearer auth or the optional stdio bridge binary. Agents can then use MCP tools like register_agent, post_status, and post_error to report activity.

Key features of MCPolly

  • Agent registration and real‑time status tracking (starting, running, error, completed, etc.)
  • Real‑time activity feed and global error feed across all agents
  • Configurable webhook alerts (Discord, Slack, generic) with retry logic
  • Dark‑mode web dashboard with live updates via HTMX polling
  • Vector semantic search over indexed documents using local Ollama embeddings
  • Agent spawning with contextual knowledge from indexed content

Use cases of MCPolly

  • Monitor multiple AI agents working on different tasks from a single dashboard
  • Automatically index product requirements, design docs, and reference materials for semantic search
  • Get instant alerts when an agent encounters an error or completes a critical task
  • Enable agents to self‑report progress and state without human prompting
  • Self‑host a lightweight observability platform for agent‑driven workflows

よくある質問

1

How does MCPolly communicate with AI agents?

MCPolly exposes an MCP Streamable HTTP endpoint (`/mcp`) using JSON‑RPC over HTTP with SSE streaming. Agents connect directly via HTTP; an optional stdio binary (`mcpolly_mcp`) is available for platforms that do not support HTTP transport.

2

Does MCPolly require Ollama to work?

No, Ollama is optional. Vector embeddings for semantic search and agent spawning with context require Ollama running locally with the `all‑minilm` model. Without it, the web dashboard and agent observability still function.

3

How do I get or reset my API key?

On first run, the server prints a default API key and opens a setup wizard. If you forget the key, click “Forgot API key? Reset your instance” on the login page to revoke all keys, generate a new one, and re‑enter the setup wizard.

4

Where is data stored?

All agent data, activity logs, error records, and vector embeddings are stored in a local SQLite database (`mcpolly.db`) created automatically on first run. No external database is required.

5

What authentication does MCPolly use?

MCPolly uses Bearer token authentication. The API key is passed in the `Authorization` header of HTTP requests. The server supports multiple API keys managed via the web dashboard’s settings page.

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