A Model Context Protocol (MCP) server that stores and retrieves project context using OpenAI vector stores. Enable your AI editor to remember and recall project knowledge across sessions. For best results, add this in your agent rules file and force it to use it at the start and the very end of each chat session. Otherwise, in your prompt, ask the agent to use the ingestion or retrieval where necessary.
ingest_document- Store project summaries, design docs, and context in a vector storeretrieve_relevant_chunks- Retrieve relevant context using semantic search
pip install -e .Requirements: Python 3.11+ and an OpenAI API key
Create a .env file (or set environment variables):
# Required
OPENAI_API_KEY=sk-your-api-key-here
# Optional - Vector Store (use ID for existing, NAME to create/find)
CONTEXT_DB_VECTOR_STORE_ID=vs_xxxxx
# OR
CONTEXT_DB_VECTOR_STORE_NAME=context-db-mcp
# Optional - Tuning
CONTEXT_DB_DEFAULT_MAX_RESULTS=10
CONTEXT_DB_REQUEST_TIMEOUT_SECONDS=120.0
CONTEXT_DB_LOG_LEVEL=INFOUse env.example as a template.
Add to your project root inside .mcp.json (create with exact name and preceeding dot) and paste the following:
{
"mcpServers": {
"context-db": {
"command": "{PATH TO THE BIN FOLDER IN YOUR CLONED REPO for example: {YOUR PATH.....}/Context_DB_MCP/env/bin/context-db-mcp}",
"env": {
"OPENAI_API_KEY": "sk-your-api-key-here",
"CONTEXT_DB_VECTOR_STORE_NAME": "context-db-mcp"
}
}
}
}Restart Claude Code. Tools will be available as:
mcp__context-db__ingest_documentmcp__context-db__retrieve_relevant_chunks
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"context-db": {
"command": "{PATH TO THE BIN FOLDER IN YOUR CLONED REPO for example: {YOUR PATH.....}/Context_DB_MCP/env/bin/context-db-mcp}",
"env": {
"OPENAI_API_KEY": "sk-your-api-key-here",
"CONTEXT_DB_VECTOR_STORE_NAME": "context-db-mcp"
}
}
}
}Restart Cursor and access the tools from the MCP integration panel.
Run the diagnostic script to verify your setup:
python test_mcp_connection.pyRun the test suite:
pip install -e ".[dev]"
pytest tests/ -v