Context DB MCP
Provides tools for storing and retrieving project context using OpenAI vector stores, enabling semantic search and context recall across sessions.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Context DB MCPfind relevant context about the authentication flow"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Context DB MCP (BETA Mode- can make mistakes)
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.
Features
ingest_document- Store project summaries, design docs, and context in a vector storeretrieve_relevant_chunks- Retrieve relevant context using semantic search
Related MCP server: ai-cortex
Installation
pip install -e .Requirements: Python 3.11+ and an OpenAI API key
Configuration
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.
Usage
Claude Code
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
Cursor
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.
Testing
Run the diagnostic script to verify your setup:
python test_mcp_connection.pyRun the test suite:
pip install -e ".[dev]"
pytest tests/ -vThis server cannot be deployed
Maintenance
Related MCP Connectors
Shared project memory that keeps teammates and AI agents aligned across sessions.
- OneLoreOAuthai.onelore
Shared project context for AI agents and teams: docs, tasks, and messages that stay current.
Project memory, semantic code search, and grounded agent context.
Persistent memory layer that saves and recalls your project context and preferences.
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- FlicenseNot gradedqualityDmaintenanceStores and searches AI-generated knowledge about software projects, enabling Cursor to access project structures, design patterns, best practices, and technical documentation.-
- AlicenseNot gradedqualityAmaintenanceEnables AI coding assistants to store and retrieve project-aware context as plain Markdown files locally, with no cloud or vector database required.2MIT