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

User → LearnMCP Tools → LearnService → BackgroundProcessor ⇄ Extractors ⇄ Summarizer → DataPersistence
                                                                                              ↓
                                                                                    <DATA_DIR>/learn-content/
                                                                                              ↓
                                                                              Forest HTA Builder (optional)

Installation

Install Dependencies:
```
cd learn-mcp-server
npm install
```

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>"
      }
    }
  }
}

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:

{
  "project_id": "my_project",
  "urls": [
    "https://youtube.com/watch?v=example",
    "https://example.com/document.pdf",
    "https://blog.example.com/article"
  ]
}

`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/:

learn-content/
├── <project_id>/
│   ├── sources.json          # Source registry
│   └── summaries/
│       ├── <source_id>.json  # Individual summaries
│       └── ...

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:

LearnMCP has processed learning sources for a project
Forest builds an HTA tree for the same project
Learning content summaries are injected into the HTA generation prompt

Workflow Examples

Basic Learning Content Workflow

Add Sources:

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

Process Content:

process_learning_sources(project_id="learn_python")

Check Status:

get_processing_status(project_id="learn_python")

Get Summary:

get_learning_summary(project_id="learn_python")

Integrated with Forest

Add and process learning sources in LearnMCP
Build HTA tree in Forest - it will automatically include learning content context
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

npm test

Linting

npm run lint
npm run lint:fix

Debugging

Set LOG_LEVEL=debug for detailed logging.

Troubleshooting

Common Issues

YouTube extraction fails: Check if video has transcripts enabled
PDF extraction fails: Ensure PDF is publicly accessible
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

LearnMCP Server

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

Overview

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

User → LearnMCP Tools → LearnService → BackgroundProcessor ⇄ Extractors ⇄ Summarizer → DataPersistence
                                                                                              ↓
                                                                                    <DATA_DIR>/learn-content/
                                                                                              ↓
                                                                              Forest HTA Builder (optional)

Installation

Install Dependencies:
```
cd learn-mcp-server
npm install
```

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>"
      }
    }
  }
}

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:

{
  "project_id": "my_project",
  "urls": [
    "https://youtube.com/watch?v=example",
    "https://example.com/document.pdf",
    "https://blog.example.com/article"
  ]
}

`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/:

learn-content/
├── <project_id>/
│   ├── sources.json          # Source registry
│   └── summaries/
│       ├── <source_id>.json  # Individual summaries
│       └── ...

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:

LearnMCP has processed learning sources for a project
Forest builds an HTA tree for the same project
Learning content summaries are injected into the HTA generation prompt

Workflow Examples

Basic Learning Content Workflow

Add Sources:

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

Process Content:

process_learning_sources(project_id="learn_python")

Check Status:

get_processing_status(project_id="learn_python")

Get Summary:

get_learning_summary(project_id="learn_python")

Integrated with Forest

Add and process learning sources in LearnMCP
Build HTA tree in Forest - it will automatically include learning content context
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

npm test

Linting

npm run lint
npm run lint:fix

Debugging

Set LOG_LEVEL=debug for detailed logging.

Troubleshooting

Common Issues

YouTube extraction fails: Check if video has transcripts enabled
PDF extraction fails: Ensure PDF is publicly accessible
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