Learning Coach

Provides a personal learning coach MCP server with content ingestion, progress tracking, and personalized daily insights.

python

GitHub Stars

python

Language

6 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "pevansh-learning_coach_mcp": {
      "command": "python",
      "args": [
        "-m",
        "src.server"
      ],
      "env": {
        "GROQ_API_KEY": "YOUR_GROQ_API_KEY",
        "SUPABASE_KEY": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
        "SUPABASE_URL": "https://your-supabase-url.supabase.co",
        "MAX_DAILY_INSIGHTS": "7",
        "DEFAULT_EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
      }
    }
  }
}

You run a personal MCP server that builds a learning coach experience. It ingests your content sources, tracks your progress, and generates daily personalized insights using retrieval-augmented generation so you can learn more efficiently.

How to use

You interact with the Learning Coach MCP server through an MCP client or desktop integration. Start the server locally and connect your client to run actions such as updating your learning progress, ingesting content, generating today’s insights, and searching past insights. The core workflow is: set your current week and topics, add content sources, trigger daily digest generation, and review or search through your insights.

Key actions you can perform with your MCP client include: updating your current learning week and topics, adding new content sources (RSS feeds, blogs, Reddit), generating a personalized daily digest, searching past insights, ingesting content on demand, and retrieving today’s insights.

To connect your client, use the standard stdio configuration that runs the server locally. This keeps the server and client on the same machine for quick iteration and testing.

{
  "mcpServers": {
    "learning_coach": {
      "type": "stdio",
      "name": "learning_coach",
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/learning_coach_mcp"
    }
  }
}

How to install

Prerequisites: you need Python installed on your system and a working network to reach the required data sources. You will also use a local environment to run the MCP server and connect a client.

Install Python dependencies for the MCP server.

pip install -r requirements.txt

Prepare environment variables. Copy the template and fill in credentials and configuration values for your Supabase and embedding model settings.

cp .env.example .env

Edit ".env" with your credentials:

SUPABASE_URL=your_supabase_project_url
SUPABASE_KEY=your_supabase_anon_key
GROQ_API_KEY=your_groq_api_key
DEFAULT_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
MAX_DAILY_INSIGHTS=7

Run the MCP server

Start the MCP server locally so you can connect a client and begin using the features.

python -m src.server

Available tools

generate_daily_digest

Create today’s personalized learning insights using the current week and topics, leveraging the RAG pipeline to fetch relevant content.

add_content_source

Add new content sources such as RSS feeds, blogs, or Reddit threads to enrich your content pool.

update_progress

Update your current learning week and topics to tailor recommendations and difficulty.

search_insights

Search through past daily insights to recall concepts and topics you’ve learned.

get_progress

View your current learning progress, including week, topics, and goals.

ingest_content_from_sources

Manually trigger content ingestion from configured sources to refresh embeddings and content.

get_today_insights

Retrieve today’s generated insights for quick review.

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VeilStrat

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