Memory Qdrant MCP

An MCP (Model Context Protocol) server that provides memory management capabilities using Qdrant vector database for storing and retrieving project context, decisions, progress, and patterns.

Features

- Memory Management: Log and query project memories across different categories
- Vector Search: Semantic search through memory entries using embeddings
- Multiple Providers: Support for Gemini, Ollama, and FastEmbed embedding providers
- MCP Integration: Full MCP stdio server implementation
- REST API: Additional HTTP endpoints for direct access
- Workspace Management: Automatic project detection and memory bank initialization
- Import/Export: Markdown-based memory bank import and export functionality
- Conversation Analysis: Automatic logging of relevant information from conversations
- Performance Optimization: Connection pooling, LRU caching, and cache invalidation for improved performance
- End-to-End Testing: Comprehensive test suite with MCP Inspector for protocol compliance

Performance Features

- Connection Pooling: Configurable Qdrant client connection pool for better concurrent performance
- LRU Caching: Least Recently Used cache implementation for:
- Embeddings (reduces API calls for repeated text)
- Query results (faster repeated searches)
- Context and pattern data (improved retrieval speed)
- Cache Invalidation: Automatic cache clearing when data is modified to ensure consistency
- Configurable Cache Sizes: Adjustable TTL and size limits for different cache types
- Cache Statistics: Built-in cache performance monitoring and statistics

Installation

$3

bash

npx memory-qdrant-mcp





This will download and run the server automatically.



$3

bash

npm install -g memory-qdrant-mcp

memory-qdrant-mcp

$3

bash

git clone 

cd memory-qdrant-mcp

npm install

npm run build  # if needed

node server/index.js





Prerequisites



- Qdrant Database: The server requires a running Qdrant instance



  Option 1: Simple Docker run

bash

  docker run -p 6333:6333 qdrant/qdrant





  Option 2: Docker Compose (Recommended for production)

  Create a

docker-compose.yml

 file:

yaml

  services:

    qdrant:

      image: qdrant/qdrant:latest

      container_name: qdrant

      restart: unless-stopped

      ports:

        - "6333:6333"

        - "6334:6334"

      environment:

        QDRANT__SERVICE__CORS: "true"

      volumes:

        - qdrant_data:/qdrant/storage



  volumes:

    qdrant_data:

      driver: local





  Then run:

bash

  docker-compose up -d





- Environment Variables: Copy

.env.example to .env

 and configure:

bash

  cp .env.example .env





  Embedding Provider (choose one):

env

  # Option 1: Google Gemini (fast, recommended - uses Gemini for both embedding and summarization)

  GEMINI_API_KEY=your_gemini_api_key_here



  # Option 2: Ollama (local, free, slower)

  OLLAMA_BASE_URL=http://localhost:11434



  # Option 3: OpenRouter + Gemini/Ollama (OpenRouter for summarization, Gemini/Ollama for embedding)

  OPENROUTER_API_KEY=your_openrouter_api_key_here





  Model Configuration (optional, defaults provided):

env

  EMBEDDING_MODEL=models/gemini-embedding-001  # Gemini embedding model (use nomic-embed-text:v1.5 for Ollama)

  SUMMARIZER_MODEL=openai/gpt-oss-20b:free      # OpenRouter default (auto-switches to Gemini if no OpenRouter key)

  DEFAULT_TOP_K_MEMORY_QUERY=3                  # Search result limit





  Required:

env

  QDRANT_URL=http://localhost:6333





MCP Configuration



$3



Create or update the MCP settings file at:

- Windows:

%APPDATA%\Code\User\globalStorage\github.copilot-chat\settings\mcp.json



- macOS:

~/Library/Application Support/Code/User/globalStorage/github.copilot-chat/settings/mcp.json



- Linux:

~/.config/Code/User/globalStorage/github.copilot-chat/settings/mcp.json





Add the following configuration:

json

{

  "mcpServers": {

    "memory-qdrant-mcp": {

      "command": "npx",

      "args": ["memory-qdrant-mcp"],

      "env": {

        "QDRANT_URL": "http://localhost:6333",

        "GEMINI_API_KEY": "your_gemini_api_key_here",

        "OPENROUTER_API_KEY": "your_openrouter_api_key_here",

        "OLLAMA_BASE_URL": "http://localhost:11434",

        "EMBEDDING_MODEL": "models/gemini-embedding-001",

        "DEFAULT_TOP_K_MEMORY_QUERY": "3",

        "SUMMARIZER_MODEL": "openai/gpt-oss-20b:free"

      }

    }

  }

}





$3



Add to your Roo MCP settings:

json

{

  "mcpServers": {

    "memory-qdrant-mcp": {

      "command": "npx",

      "args": ["memory-qdrant-mcp"]

    }

  }

}





Available Tools



$3



#### log_memory

Log a memory entry to the vector database.



Parameters:

-

project_name

 (string): Name of the project

-

memory_type

 (string): Type of memory (productContext, activeContext, systemPatterns, decisionLog, progress)

-

content

 (string): Content to log

-

top_level_id

 (string, optional): Optional top level ID



#### query_memory

Query memory entries from the vector database.



Parameters:

-

project_name

 (string): Name of the project

-

query_text

 (string): Query text for semantic search

-

memory_type

 (string, optional): Optional memory type filter

-

top_k

 (number, optional): Number of results to return (default: 3)



#### log_decision

Log a decision entry.



Parameters:

-

project_name

 (string): Name of the project

-

decision_text

 (string): Decision text

-

top_level_id

 (string, optional): Optional top level ID



#### log_progress

Log a progress entry.



Parameters:

-

project_name

 (string): Name of the project

-

progress_text

 (string): Progress text

-

top_level_id

 (string, optional): Optional top level ID



#### summarize_text

Summarize the given text.



Parameters:

-

text

 (string): Text to summarize



$3



#### initialize_workspace

Initialize a new workspace with automatic project detection and memory bank setup.



Parameters:

-

projectName

 (string): Name of the project

-

workspaceInfo

 (object): Workspace information containing files and directories

  -

files

 (array): List of file paths in the workspace

  -

directories

 (array): List of directory paths in the workspace



#### sync_memory

Synchronize memory bank with current workspace state and log any changes.



Parameters:

-

projectName

 (string): Name of the project

-

workspaceInfo

 (object): Current workspace information

  -

files

 (array): List of current file paths

  -

directories

 (array): List of current directory paths



$3



#### export_memory_to_markdown

Export all memory entries for a project to markdown files.



Parameters:

-

projectName

 (string): Name of the project

-

outputPath

 (string, optional): Output directory path (default: "./memory-bank")



#### import_memory_from_markdown

Import memory entries from markdown files into the vector database.



Parameters:

-

projectName

 (string): Name of the project

-

inputPath

 (string, optional): Input directory path (default: "./memory-bank")



$3



#### analyze_conversation

Analyze a conversation and automatically log relevant information to memory.



Parameters:

-

projectName

 (string): Name of the project

-

conversation

 (string): The conversation text to analyze

-

context

 (string, optional): Additional context about the conversation



Publishing to npm



To publish your own version:



1. Update

package.json

 with your information:

   - Change

name

 to a unique package name

   - Update

author, repository, homepage



   - Ensure version is appropriate



2. Login to npm:

bash

   npm login





3. Publish:

bash

   npm publish





4. Users can then install and run:

bash

   npx your-package-name





Development



$3



memory-qdrant-mcp/

├── server/

│   ├── index.js              # Main MCP server

│   ├── mcp_tools/            # MCP tool implementations

│   │   ├── memoryBankTools.js

│   │   ├── store.js

│   │   └── summarizer.js

│   ├── embeddings/           # Embedding providers

│   │   ├── providerBase.js

│   │   ├── geminiVertex.js

│   │   ├── ollama.js

│   │   └── fastEmbed.js

│   ├── cache.js              # LRU caching implementation

│   └── config.js             # Configuration

├── test/                     # Test files

│   ├── memoryBankTools.test.js

│   └── mcpServer.integration.test.js

├── babel.config.cjs          # Babel configuration for testing

├── jest.config.cjs           # Jest configuration

├── package.json

└── README.md





$3



1. Implement the tool in

server/mcp_tools/



2. Register it in

server/index.js



3. Update this README



Testing



The project includes comprehensive testing using MCP Inspector for protocol compliance:



$3

bash

npx @modelcontextprotocol/inspector node server/index.js





This will start the MCP Inspector at

http://localhost:6274` where you can test all MCP tools interactively.

$3

MCP Inspector tests cover:
- MCP protocol initialization and tool listing
- Memory bank operations (logging, querying)
- Workspace management and synchronization
- Import/export functionality
- Conversation analysis
- Performance optimizations (caching, connection pooling)

License

MIT

Memory Qdrant MCP

An MCP (Model Context Protocol) server that provides memory management capabilities using Qdrant vector database for storing and retrieving project context, decisions, progress, and patterns.

Features

Performance Features

Installation

$3

bash

npx memory-qdrant-mcp





This will download and run the server automatically.



$3

bash

npm install -g memory-qdrant-mcp

memory-qdrant-mcp

$3

bash

git clone 

cd memory-qdrant-mcp

npm install

npm run build  # if needed

node server/index.js





Prerequisites



- Qdrant Database: The server requires a running Qdrant instance



  Option 1: Simple Docker run

bash

  docker run -p 6333:6333 qdrant/qdrant





  Option 2: Docker Compose (Recommended for production)

  Create a

docker-compose.yml

 file:

yaml

  services:

    qdrant:

      image: qdrant/qdrant:latest

      container_name: qdrant

      restart: unless-stopped

      ports:

        - "6333:6333"

        - "6334:6334"

      environment:

        QDRANT__SERVICE__CORS: "true"

      volumes:

        - qdrant_data:/qdrant/storage



  volumes:

    qdrant_data:

      driver: local





  Then run:

bash

  docker-compose up -d





- Environment Variables: Copy

.env.example to .env

 and configure:

bash

  cp .env.example .env





  Embedding Provider (choose one):

env

  # Option 1: Google Gemini (fast, recommended - uses Gemini for both embedding and summarization)

  GEMINI_API_KEY=your_gemini_api_key_here



  # Option 2: Ollama (local, free, slower)

  OLLAMA_BASE_URL=http://localhost:11434



  # Option 3: OpenRouter + Gemini/Ollama (OpenRouter for summarization, Gemini/Ollama for embedding)

  OPENROUTER_API_KEY=your_openrouter_api_key_here





  Model Configuration (optional, defaults provided):

env

  EMBEDDING_MODEL=models/gemini-embedding-001  # Gemini embedding model (use nomic-embed-text:v1.5 for Ollama)

  SUMMARIZER_MODEL=openai/gpt-oss-20b:free      # OpenRouter default (auto-switches to Gemini if no OpenRouter key)

  DEFAULT_TOP_K_MEMORY_QUERY=3                  # Search result limit





  Required:

env

  QDRANT_URL=http://localhost:6333





MCP Configuration



$3



Create or update the MCP settings file at:

- Windows:

%APPDATA%\Code\User\globalStorage\github.copilot-chat\settings\mcp.json



- macOS:

~/Library/Application Support/Code/User/globalStorage/github.copilot-chat/settings/mcp.json



- Linux:

~/.config/Code/User/globalStorage/github.copilot-chat/settings/mcp.json





Add the following configuration:

json

{

  "mcpServers": {

    "memory-qdrant-mcp": {

      "command": "npx",

      "args": ["memory-qdrant-mcp"],

      "env": {

        "QDRANT_URL": "http://localhost:6333",

        "GEMINI_API_KEY": "your_gemini_api_key_here",

        "OPENROUTER_API_KEY": "your_openrouter_api_key_here",

        "OLLAMA_BASE_URL": "http://localhost:11434",

        "EMBEDDING_MODEL": "models/gemini-embedding-001",

        "DEFAULT_TOP_K_MEMORY_QUERY": "3",

        "SUMMARIZER_MODEL": "openai/gpt-oss-20b:free"

      }

    }

  }

}





$3



Add to your Roo MCP settings:

json

{

  "mcpServers": {

    "memory-qdrant-mcp": {

      "command": "npx",

      "args": ["memory-qdrant-mcp"]

    }

  }

}





Available Tools



$3



#### log_memory

Log a memory entry to the vector database.



Parameters:

-

project_name

 (string): Name of the project

-

memory_type

 (string): Type of memory (productContext, activeContext, systemPatterns, decisionLog, progress)

-

content

 (string): Content to log

-

top_level_id

 (string, optional): Optional top level ID



#### query_memory

Query memory entries from the vector database.



Parameters:

-

project_name

 (string): Name of the project

-

query_text

 (string): Query text for semantic search

-

memory_type

 (string, optional): Optional memory type filter

-

top_k

 (number, optional): Number of results to return (default: 3)



#### log_decision

Log a decision entry.



Parameters:

-

project_name

 (string): Name of the project

-

decision_text

 (string): Decision text

-

top_level_id

 (string, optional): Optional top level ID



#### log_progress

Log a progress entry.



Parameters:

-

project_name

 (string): Name of the project

-

progress_text

 (string): Progress text

-

top_level_id

 (string, optional): Optional top level ID



#### summarize_text

Summarize the given text.



Parameters:

-

text

 (string): Text to summarize



$3



#### initialize_workspace

Initialize a new workspace with automatic project detection and memory bank setup.



Parameters:

-

projectName

 (string): Name of the project

-

workspaceInfo

 (object): Workspace information containing files and directories

  -

files

 (array): List of file paths in the workspace

  -

directories

 (array): List of directory paths in the workspace



#### sync_memory

Synchronize memory bank with current workspace state and log any changes.



Parameters:

-

projectName

 (string): Name of the project

-

workspaceInfo

 (object): Current workspace information

  -

files

 (array): List of current file paths

  -

directories

 (array): List of current directory paths



$3



#### export_memory_to_markdown

Export all memory entries for a project to markdown files.



Parameters:

-

projectName

 (string): Name of the project

-

outputPath

 (string, optional): Output directory path (default: "./memory-bank")



#### import_memory_from_markdown

Import memory entries from markdown files into the vector database.



Parameters:

-

projectName

 (string): Name of the project

-

inputPath

 (string, optional): Input directory path (default: "./memory-bank")



$3



#### analyze_conversation

Analyze a conversation and automatically log relevant information to memory.



Parameters:

-

projectName

 (string): Name of the project

-

conversation

 (string): The conversation text to analyze

-

context

 (string, optional): Additional context about the conversation



Publishing to npm



To publish your own version:



1. Update

package.json

 with your information:

   - Change

name

 to a unique package name

   - Update

author, repository, homepage



   - Ensure version is appropriate



2. Login to npm:

bash

   npm login





3. Publish:

bash

   npm publish





4. Users can then install and run:

bash

   npx your-package-name





Development



$3



memory-qdrant-mcp/

├── server/

│   ├── index.js              # Main MCP server

│   ├── mcp_tools/            # MCP tool implementations

│   │   ├── memoryBankTools.js

│   │   ├── store.js

│   │   └── summarizer.js

│   ├── embeddings/           # Embedding providers

│   │   ├── providerBase.js

│   │   ├── geminiVertex.js

│   │   ├── ollama.js

│   │   └── fastEmbed.js

│   ├── cache.js              # LRU caching implementation

│   └── config.js             # Configuration

├── test/                     # Test files

│   ├── memoryBankTools.test.js

│   └── mcpServer.integration.test.js

├── babel.config.cjs          # Babel configuration for testing

├── jest.config.cjs           # Jest configuration

├── package.json

└── README.md





$3



1. Implement the tool in

server/mcp_tools/



2. Register it in

server/index.js



3. Update this README



Testing



The project includes comprehensive testing using MCP Inspector for protocol compliance:



$3

bash

npx @modelcontextprotocol/inspector node server/index.js





This will start the MCP Inspector at

http://localhost:6274` where you can test all MCP tools interactively.

$3

License

MIT