LearnMCP Server

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

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:

{
  "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/
├── /
│   ├── 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:

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