LearnMCP Server

A standalone MCP server that enhances Forest with learning content extraction and summarization capabilities.

Overview

LearnMCP extracts and summarizes learning content from various sources (YouTube videos, PDFs, web articles) and makes those summaries available to Forest's HTA builder for more informed task generation.

Features

• Content Extraction: YouTube videos (with transcripts), PDF documents, web articles

• Background Processing: Async content processing with queue management

• Smart Summarization: Content chunking and summarization with relevance scoring

• Forest Integration: Optional integration with Forest's HTA tree builder

• Standalone Operation: Can be enabled/disabled independently of Forest

Architecture

Installation

1. Install Dependencies:

   cd learn-mcp-server npm install

2. Configure MCP: Add to your mcp-config.json:

   { "mcpServers": { "learn-mcp": { "command": "node", "args": ["server.js"], "cwd": "learn-mcp-server", "env": { "FOREST_DATA_DIR": "<same as Forest>" } } } }

3. Start Server: The server starts automatically when Claude Desktop loads the MCP config.

Available Tools

add_learning_sources

Add learning sources (URLs) to a project for content extraction.

Parameters:

• project_id (string): Project ID to add sources to

• urls (array): Array of URLs (YouTube, PDF, articles)

Example:

process_learning_sources

Start background processing of pending learning sources.

Parameters:

• project_id (string): Project ID to process sources for

list_learning_sources

List learning sources for a project, optionally filtered by status.

Parameters:

• project_id (string): Project ID

• status (string, optional): Filter by status (pending, processing, completed, failed)

get_learning_summary

Get learning content summary for a project or specific source.

Parameters:

• project_id (string): Project ID

• source_id (string, optional): Specific source ID (if not provided, returns aggregated summary)

• token_limit (number, optional): Maximum tokens for aggregated summary (default: 2000)

delete_learning_sources

Delete learning sources and their summaries.

Parameters:

• project_id (string): Project ID

• source_ids (array): Array of source IDs to delete

get_processing_status

Get current processing status for learning sources.

Parameters:

• project_id (string): Project ID

Supported Content Types

YouTube Videos

• Extracts video metadata (title, author, duration, etc.)

• Downloads transcripts when available

• Falls back to description if no transcript

PDF Documents

• Extracts text content from remote PDF URLs

• Preserves document metadata

• Handles various PDF formats

Web Articles

• Uses Mozilla Readability for clean content extraction

• Extracts metadata (title, author, publish date, etc.)

• Estimates reading time

Data Storage

LearnMCP stores data in <FOREST_DATA_DIR>/learn-content/:

Forest Integration

When both LearnMCP and Forest are active, Forest's HTA builder can optionally include learning content summaries in its task generation prompts. This happens automatically when:

1. LearnMCP has processed learning sources for a project

2. Forest builds an HTA tree for the same project

3. Learning content summaries are injected into the HTA generation prompt

Workflow Examples

Basic Learning Content Workflow

1. Add Sources:

   add_learning_sources(project_id="learn_python", urls=["https://youtube.com/watch?v=python_tutorial"])

2. Process Content:

   process_learning_sources(project_id="learn_python")

3. Check Status:

   get_processing_status(project_id="learn_python")

4. Get Summary:

   get_learning_summary(project_id="learn_python")

Integrated with Forest

1. Add and process learning sources in LearnMCP

2. Build HTA tree in Forest - it will automatically include learning content context

3. Generated tasks will be informed by the processed learning materials

Configuration

Environment Variables

• FOREST_DATA_DIR: Shared data directory with Forest (required)

• LOG_LEVEL: Logging level (debug, info, warn, error)

• NODE_ENV: Environment (development, production)

Background Processor Settings

• Max Queue Size: 50 tasks

• Max Concurrent: 2 simultaneous extractions

• Processing Interval: 3 seconds

• Retry Attempts: 3 per source

• Timeout: 5 minutes per extraction

Error Handling

• Graceful Degradation: Failed extractions don't block other sources

• Retry Logic: Automatic retries with exponential backoff

• Comprehensive Logging: Detailed logs for debugging

• Status Tracking: Clear status indicators for each source

Development

Running Tests

Linting

Debugging

Set LOG_LEVEL=debug for detailed logging.

Troubleshooting

Common Issues

1. YouTube extraction fails: Check if video has transcripts enabled

2. PDF extraction fails: Ensure PDF is publicly accessible

3. Article extraction fails: Some sites block automated access

Logs

Check logs in <FOREST_DATA_DIR>/logs/:

• learn-mcp.log: General operations

• learn-mcp-errors.log: Error details

License

MIT License - Same as Forest MCP Server