Pocket Assistant MCP Server

A Model Context Protocol (MCP) server that provides pocket assistance capabilities with ChromaDB vector storage. This server enables AI assistants to save, retrieve, and manage research content efficiently using vector embeddings.

Features

• Vector Storage: Uses ChromaDB for efficient storage and retrieval
• Topic Organization: Organize research content by topics
• Deduplication: Automatic content deduplication using hashing
• Semantic Search: Query research content using natural language
• Multiple Topics: Manage multiple research topics simultaneously
• OpenAI Embeddings: Uses OpenAI's text-embedding-3-small model

Installation

Using uvx (Recommended)

uvx pocket-agent-mcp

Using uv

uv pip install pocket-agent-mcp

Using pip

pip install pocket-agent-mcp

From Source

git clone https://github.com/VikashS/pocket_agent_mcp.git
cd pocket-agent-mcp
uv pip install -e .

Configuration

Environment Variables

Required:

• OPENAI_API_KEY - Your OpenAI API key for embeddings
• RESEARCH_DB_PATH - Base path for storing research databases
  • A pocket_chroma_dbs directory will be created inside this path
  • Example: /path/to/data (will create /path/to/data/pocket_chroma_dbs)
  • Example: ~/.pocket-agent-mcp (will create ~/.pocket-agent-mcp/pocket_chroma_dbs)

Create a .env file with your configuration:

OPENAI_API_KEY=your-api-key-here
RESEARCH_DB_PATH=/path/to/data

Claude Desktop Configuration

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "research-assistant": {
      "command": "uvx",
      "args": ["pocket-agent-mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key-here",
        "POCKET_DB_PATH": "/path/to/data"
      }
    }
  }
}

Note: Both OPENAI_API_KEY and POCKET_DB_PATH are required. The database will be stored in POCKET_DB_PATH/pocket_chroma_dbs/.

Available Tools

1. save_research_data

Save research content to vector database for future retrieval.

Parameters:

• content (List[str]): List of text content to save
• topic (str): Topic name for organizing the data (creates separate DB)

Example:

Save these research findings about AI to the "artificial-intelligence" topic

2. query_research_data

Query saved research content using natural language.

Parameters:

• query (str): Natural language query
• topic (str): Topic to search in (default: "default")
• k (int): Number of results to return (default: 5)

Example:

Query the "artificial-intelligence" topic for information about transformers

3. list_topics

List all available research topics and their document counts.

Example:

List all available research topics

4. delete_topic

Delete a research topic and all its associated data.

Parameters:

• topic (str): Topic name to delete

Example:

Delete the "old-research" topic

5. get_topic_info

Get detailed information about a specific topic.

Parameters:

• topic (str): Topic name

Example:

Get information about the "artificial-intelligence" topic

Usage Examples

Once configured with Claude Desktop or another MCP client, you can:

• "Save this article about machine learning to my 'ml-research' topic"
• "Query my 'ml-research' for information about neural networks"
• "List all my research topics"
• "Get information about the 'quantum-computing' topic"
• "Delete the 'old-notes' topic"

Technical Details

• Protocol: Model Context Protocol (MCP)
• Transport: stdio
• Vector Database: ChromaDB
• Embeddings: OpenAI text-embedding-3-small
• Storage: Local filesystem at POCKET_DB_PATH/pocket_chroma_dbs/

Requirements

• Python 3.11 or higher
• OpenAI API key
• Dependencies: chromadb, langchain, fastmcp, openai

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/VikashS/pocket_agent_mcp.git
cd pocket_agent_mcp

# Install with development dependencies
uv pip install -e .

License

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

Author

Vikash Singh

• Email: vikash.singh@linuxmail.org
• GitHub: https://github.com/vikashs

Acknowledgments

• Built with FastMCP
• Uses ChromaDB for vector storage
• Powered by LangChain
• Implements the Model Context Protocol