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 embeddingsRESEARCH_DB_PATH- Base path for storing research databases- A
pocket_chroma_dbsdirectory 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)
- A
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 savetopic(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 querytopic(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