What is Limitless Mcp

Limitless MCP is a server implementation of the Model Context Protocol (MCP) that connects your Limitless Pendant data to AI assistants like Claude, enabling seamless access to your Limitless API data.

Use cases

Use cases include analyzing conversation patterns, generating summaries of meetings, filtering content by speaker or timeframe, extracting key topics from lifelogs, and integrating with AI tools for improved decision-making.

How to use

To use Limitless MCP, you need to have Node.js 18 or higher, a Limitless account with a paired Pendant, and a Limitless API key. After installation, configure the server with your API key and start it to connect to compatible AI clients.

Key features

Key features include enhanced search with relevance scoring, semantic search using text embeddings, natural language time parsing, smart summarization, transcript generation, time analysis, content filtering, topic extraction, sentiment analysis, a plugin architecture, performance optimization, customizable settings, secure authentication, and seamless integration with various clients.

Where to use

Limitless MCP can be used in various fields such as personal data management, AI-assisted analytics, and enhancing user interactions with AI through personalized data insights.

🔮 Limitless MCP

Connect your Limitless Pendant data to Claude and other LLMs via the Model Context Protocol (MCP).

Limitless MCP is a server implementation of the Model Context Protocol that provides seamless access to your Limitless API data for AI assistants like Claude.

✨ Features

• 🔍 Enhanced Search with relevance-based scoring and content snippets
• 🔮 Semantic Search using text embeddings for concept-based retrieval
• 📅 Natural Language Time parsing for intuitive date filtering (e.g., “last week”)
• 📝 Smart Summarization at different detail levels and focus areas
• 📄 Transcript Generation in multiple formats for easy reading
• 📊 Time Analysis to understand your recording patterns
• 🔎 Content Filtering by speaker, type, or timeframe
• 🧠 Topic Extraction to identify key themes across lifelogs
• 😊 Sentiment Analysis for conversations with speaker breakdown
• 🔌 Plugin Architecture for extending functionality with custom features
• ⚡ Performance Optimization with configurable caching
• 🎛️ Customizable via environment variables
• 🔒 Secure Authentication using your Limitless API key
• 🔄 Seamless Integration with Claude Desktop, Cursor, and other MCP-compatible clients

📋 Prerequisites

• Node.js 18 or higher (required for fetch API used by the client)
• A Limitless account with a paired Pendant
• A Limitless API key (available to Pendant owners)

🚀 Installation

npm install -g limitless-mcp

🔧 Configuration & Setup

Claude Desktop

1. Open your Claude Desktop configuration file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
   • Linux: ~/.config/Claude/claude_desktop_config.json

2. Add the Limitless MCP server to your configuration:

{
  "mcpServers": {
    "limitless-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "limitless-mcp"
      ],
      "env": {
        "LIMITLESS_API_KEY": "your-api-key-here"
      }
    }
  }
}

3. Restart Claude Desktop

Cursor

1. Open your Cursor MCP configuration file:

   • macOS: ~/.cursor/mcp.json
   • Windows: %USERPROFILE%\.cursor\mcp.json
   • Linux: ~/.cursor/mcp.json

2. Add the Limitless MCP server to your configuration:

{
  "mcpServers": {
    "limitless-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "limitless-mcp"
      ],
      "env": {
        "LIMITLESS_API_KEY": "your-api-key-here"
      }
    }
  }
}

3. Restart Cursor

Other MCP-Compatible Applications

Any application that supports the Model Context Protocol can use limitless-mcp with a similar configuration. The essential elements are:

{
  "command": "npx",
  "args": [
    "-y",
    "limitless-mcp"
  ],
  "env": {
    "LIMITLESS_API_KEY": "your-api-key-here"
  }
}

🎮 Usage

Once configured, you can interact with your Limitless data using natural language within Claude or other MCP-enabled AI assistants.

Example Commands

• List recent lifelogs:

  Show me my recent lifelogs.
  

• Search for specific content:

  Search my lifelogs for conversations about artificial intelligence.
  

• Get a daily summary:

  Give me a summary of my day on May 1, 2025.
  

• Retrieve a full conversation:

  Show me the full text of lifelog OFe86CdN11YCe22I9Jv4.
  

• Generate a transcript:

  Create a dialogue transcript from lifelog OFe86CdN11YCe22I9Jv4.
  

• Analyze recording time:

  Show me a time analysis of my recordings from last week.
  

• Filter by speaker:

  Filter lifelog OFe86CdN11YCe22I9Jv4 to only show what Jake said.
  

🧰 Available Tools

Core Tools

list_lifelogs

Lists your lifelogs with filtering options:

• limit: Maximum number of lifelogs to return (default: 10)
• date: Date in YYYY-MM-DD format
• timezone: IANA timezone specifier (e.g., “America/Los_Angeles”)
• start: Start date/time
• end: End date/time
• direction: Sort direction (“asc” or “desc”)
• includeContent: Whether to include markdown content
• fields: Specific fields to include (title, time, id, etc.)

get_paged_lifelogs

Navigates through paginated results:

• cursor: Pagination cursor from previous results
• limit: Maximum number of lifelogs to return
• date, timezone, direction: Same as above
• includeContent: Whether to include markdown content
• fields: Specific fields to include (title, time, id, etc.)

search_lifelogs

Searches your lifelogs with relevance-based scoring:

• query: Text to search for
• limit: Maximum number of results to return
• date, timezone, start, end: Same as above
• searchMode: Search mode (“basic” or “advanced” with scoring)
• includeSnippets: Whether to include matching content snippets

get_lifelog

Retrieves a specific lifelog with selective field retrieval:

• id: The ID of the lifelog to retrieve
• includeContent: Whether to include full content or just metadata
• fields: Specific fields to include (title, time, speakers, etc.)

get_lifelog_metadata

Retrieves only metadata about a lifelog (faster than full content):

• id: The ID of the lifelog to retrieve metadata for

filter_lifelog_contents

Filters lifelog content by various criteria:

• id: The ID of the lifelog to filter
• speakerName: Filter by speaker name
• contentType: Filter by content type (e.g., heading1, blockquote)
• timeStart: Filter content after this time (ISO-8601)
• timeEnd: Filter content before this time (ISO-8601)

generate_transcript

Creates a formatted transcript from a lifelog:

• id: The ID of the lifelog to generate transcript from
• format: Transcript format style (“simple”, “detailed”, or “dialogue”)

get_time_summary

Provides time-based analytics of your recordings:

• date: Date in YYYY-MM-DD format
• timezone: IANA timezone specifier
• start: Start date for range analysis
• end: End date for range analysis
• groupBy: How to group statistics (“hour”, “day”, or “week”)

get_day_summary

Provides a formatted summary of a specific day’s lifelogs:

• date: Date in YYYY-MM-DD format
• timezone: IANA timezone specifier

Advanced Analysis Tools

summarize_lifelog

Creates intelligent summaries at different levels of detail:

• id: The ID of the lifelog to summarize
• level: Level of summarization detail (“brief”, “detailed”, or “comprehensive”)
• focus: Focus of the summary (“general”, “key_points”, “decisions”, “questions”, “action_items”)

summarize_lifelogs

Summarizes multiple lifelogs with optional combined view:

• ids: Array of lifelog IDs to summarize
• level: Level of detail (“brief” or “detailed”)
• combinedView: Whether to provide a combined summary

extract_topics

Identifies key topics and themes across lifelogs:

• ids: Array of lifelog IDs to analyze
• maxTopics: Maximum number of topics to extract
• minOccurrences: Minimum occurrences required for a topic
• mode: Extraction mode (“keywords” or “phrases”)
• excludeCommonWords: Whether to exclude common English words

analyze_sentiment

Analyzes sentiment in lifelog content:

• id: The ID of the lifelog to analyze sentiment for
• bySpeaker: Whether to analyze sentiment by speaker
• includeSentences: Whether to include individual sentences in the analysis

compare_sentiment

Compares sentiment across multiple lifelogs:

• ids: Array of lifelog IDs to compare sentiment
• bySpeaker: Whether to compare sentiment by speaker across lifelogs

System Tools

manage_cache

Manages the caching system:

• action: Action to perform (“stats” or “clear”)

manage_plugins

Manages the plugin system:

• action: Action to perform (“list”, “enable”, “disable”, or “info”)
• name: Plugin name for enable/disable/info actions

Plugin-Provided Tools

Content Processor Plugin

process_content

Processes and transforms lifelog content:

• id: The ID of the lifelog to process
• operations: List of operations to perform (filter, replace, extract, transform)
• format: Output format (markdown, text, or json)

batch_process

Processes multiple lifelogs with the same operations:

• ids: Array of lifelog IDs to process
• operations: List of operations to perform
• mergeResults: Whether to merge results into a single output

Decorator Plugin

apply_template

Applies templates to format lifelog content:

• id: The ID of the lifelog to format
• template: Name of the template to use or custom template string
• variables: Additional variables to use in the template

manage_templates

Manages content templates:

• action: Action to perform (“list”, “get”, “add”, or “delete”)
• name: Template name for get/add/delete actions
• template: Template content for add action

Semantic Search Plugin

create_embeddings

Creates embeddings for a lifelog to enable semantic search:

• id: The ID of the lifelog to create embeddings for
• chunkSize: Size of text chunks for embeddings (in characters)
• chunkOverlap: Overlap between chunks (in characters)
• forceRefresh: Whether to force refresh embeddings

semantic_search

Searches for semantically similar content:

• query: The query to search for semantically similar content
• ids: Optional array of specific lifelog IDs to search within
• topK: Number of top results to return
• threshold: Similarity threshold (0-1)

manage_embeddings

Manages semantic search embeddings:

• action: Action to perform (“list”, “delete”, “clear”, or “info”)
• id: Lifelog ID for delete/info actions

Time Parser Plugin

parse_time_reference

Parses natural language time references:

• timeReference: Natural language time reference (e.g., “yesterday”, “last week”)
• timezone: IANA timezone specifier
• referenceDate: Reference date (defaults to today)

search_with_time

Searches lifelogs with natural language time references:

• query: Search query text
• timeReference: Natural language time reference (e.g., “yesterday”, “last week”)
• timezone: IANA timezone specifier
• limit: Maximum number of results to return
• includeContent: Whether to include content in results

⚙️ Configuration

Limitless MCP can be configured using environment variables:

API Configuration

• LIMITLESS_API_KEY: Your Limitless API key (required)
• LIMITLESS_API_BASE_URL: Limitless API base URL (default: “https://api.limitless.ai/v1”)
• LIMITLESS_API_TIMEOUT_MS: Timeout in milliseconds for API calls (default: 120000)
• LIMITLESS_API_MAX_RETRIES: Maximum retries for failed API calls (default: 3)

Pagination Configuration

• LIMITLESS_MAX_LIFELOG_LIMIT: Maximum number of results per request (default: 100)
• LIMITLESS_DEFAULT_PAGE_SIZE: Default page size for listing results (default: 10)
• LIMITLESS_SEARCH_MULTIPLIER: Multiplier for search results retrieval (default: 3)

Caching Configuration

• LIMITLESS_CACHE_TTL: Cache time-to-live in seconds (default: 300)
• LIMITLESS_CACHE_CHECK_PERIOD: Cache cleanup interval in seconds (default: 600)
• LIMITLESS_CACHE_MAX_KEYS: Maximum number of items in cache (default: 500)
• CACHE_TTL_METADATA: TTL multiplier for metadata (default: 3)
• CACHE_TTL_LISTINGS: TTL multiplier for listings (default: 2)
• CACHE_TTL_SEARCH: TTL multiplier for search results (default: 1.5)
• CACHE_TTL_SUMMARIES: TTL multiplier for summaries (default: 4)

Plugin Configuration

• LIMITLESS_PLUGINS_ENABLED: Set to “false” to disable all plugins
• LIMITLESS_PLUGIN_CONTENT_PROCESSOR: Set to “false” to disable the Content Processor plugin
• LIMITLESS_PLUGIN_DECORATOR: Set to “false” to disable the Decorator plugin
• LIMITLESS_DECORATOR_TEMPLATES: JSON string with custom templates
• LIMITLESS_PLUGIN_SEMANTIC_SEARCH: Set to “false” to disable the Semantic Search plugin
• LIMITLESS_SEMANTIC_SEARCH_TTL: TTL for embeddings cache in seconds (default: 3600)
• LIMITLESS_SEMANTIC_SEARCH_MAX_KEYS: Maximum number of embeddings to cache (default: 1000)
• LIMITLESS_PLUGIN_TIME_PARSER: Set to “false” to disable the Time Parser plugin
• LIMITLESS_DEFAULT_TIMEZONE: Default timezone for time parsing (default: “UTC”)

For more details on plugin configuration, see plugins.md.

🛠️ Development

Local Setup

# Clone the repository
git clone https://github.com/jakerains/limitless-mcp.git
cd limitless-mcp

# Install dependencies
npm install

# Build the project
npm run build

# Run in development mode with auto-reload
LIMITLESS_API_KEY="your-api-key" npm run dev

# Or start the production server
LIMITLESS_API_KEY="your-api-key" npm start

Testing Locally

To test with a locally running instance:

{
  "mcpServers": {
    "limitless-mcp": {
      "command": "node",
      "args": [
        "/path/to/limitless-mcp/dist/main.js"
      ],
      "env": {
        "LIMITLESS_API_KEY": "your-api-key-here"
      }
    }
  }
}

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Limitless AI for their incredible Pendant device and API
• Model Context Protocol team for creating the standard
• All contributors and users of this project

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