Pinecone MCP Server vs Qdrant MCP Server

Pinecone Developer MCP Server

The Model Context Protocol (MCP) is a standard that allows coding assistants and other AI tools to interact with platforms like Pinecone. The Pinecone Developer MCP Server allows you to connect these tools with Pinecone projects and documentation.

Once connected, AI tools can:

• Search Pinecone documentation to answer questions accurately.
• Help you configure indexes based on your application's needs.
• Generate code informed by your index configuration and data, as well as Pinecone documentation and examples.
• Upsert and search for data in indexes, allowing you to test queries and evaluate results within your dev environment.

See the docs for more detailed information.

This MCP server is focused on improving the experience of developers working with Pinecone as part of their technology stack. It is intended for use with coding assistants. Pinecone also offers the Assistant MCP, which is designed to provide AI assistants with relevant context sourced from your knowledge base.

Setup

To configure the MCP server to access your Pinecone project, you will need to generate an API key using the console. Without an API key, your AI tool will still be able to search documentation. However, it will not be able to manage or query your indexes.

The MCP server requires Node.js v22 or later. Ensure that node and npx are available in your PATH.

Next, you will need to configure your AI assistant to use the MCP server.

Configure Cursor

To add the Pinecone MCP server to a project, create a .cursor/mcp.json file in the project root (if it doesn't already exist) and add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<your pinecone api key>"
      }
    }
  }
}

You can check the status of the server in Cursor Settings > MCP.

To enable the server globally, add the configuration to the .cursor/mcp.json in your home directory instead.

It is recommended to use rules to instruct Cursor on proper usage of the MCP server. Check out the docs for some suggestions.

Configure Claude desktop

Use Claude desktop to locate the claude_desktop_config.json file by navigating to Settings > Developer > Edit Config. Add the following configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y", "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<your pinecone api key>"
      }
    }
  }
}

Restart Claude desktop. On the new chat screen, you should see a hammer (MCP) icon appear with the new MCP tools available.

Use as a Gemini CLI extension

To install this as a Gemini CLI extension, run the following command:

gemini extensions install https://github.com/pinecone-io/pinecone-mcp

You will need to provide your Pinecone API key in the PINECONE_API_KEY environment variable.

export PINECONE_API_KEY=<your pinecone api key>

When you run gemini and press ctrl+t, pinecone should now be shown in the list of installed MCP servers.

Usage

Once configured, your AI tool will automatically make use of the MCP to interact with Pinecone. You may be prompted for permission before a tool can be used.

Example prompts

Here are some prompts you can try with your AI assistant:

• "Search the Pinecone docs for information about metadata filtering"
• "List all my Pinecone indexes and describe their configurations"
• "Create a new index called 'my-docs' using the multilingual-e5-large model"
• "Upsert these documents into my index: [paste your documents]"
• "Search my index for records related to 'authentication best practices'"
• "What namespaces exist in my index, and how many records are in each?"

Tools

Pinecone Developer MCP Server provides the following tools for AI assistants to use:

• search-docs: Search the official Pinecone documentation.
• list-indexes: Lists all Pinecone indexes.
• describe-index: Describes the configuration of an index.
• describe-index-stats: Provides statistics about the data in the index, including the number of records and available namespaces.
• create-document-index: Creates a document index with searchable string fields, each indexed for full-text search, embedded by a hosted model (integrated embedding), or both.
• upsert-documents: Inserts or updates documents in a document index.
• search-document-index: Searches a document index by meaning (embed, sparse_embed) or by keywords (text, query_string), with optional filters.
• create-index-for-model: Creates a legacy integrated index that uses an integrated inference model to embed text as vectors.
• upsert-records: Inserts or updates records in an index with integrated inference.
• search-records: Searches for records in an index based on a text query, using integrated inference for embedding. Has options for metadata filtering and reranking.
• cascading-search: Searches for records across multiple indexes, deduplicating and reranking the results.
• rerank-documents: Reranks a collection of records or text documents using a specialized reranking model.

Limitations

Document indexes with string fields (full-text search and integrated embedding) and legacy integrated indexes are supported. Assistants, standalone embeddings, and search with your own vectors are not supported.

Troubleshooting

MCP server not appearing in your AI tool

• Ensure Node.js v22 or later is installed: node --version
• Verify npx is available in your PATH: which npx
• Check that your configuration file is in the correct location and has valid JSON syntax
• Restart your AI tool after making configuration changes

"Invalid API key" or authentication errors

• Verify your API key is correct in the Pinecone console
• Check that the PINECONE_API_KEY environment variable is set correctly in your MCP configuration
• Ensure there are no extra spaces or quotes around the API key value

Tools not working as expected

• The MCP server only supports indexes with integrated inference. If you're trying to use a serverless index without integrated inference, you'll need to create a new index with an embedding model
• Check the MCP server logs for error messages. In Cursor, view logs in Cursor Settings > MCP

Connection issues

• If using a corporate network, ensure your firewall allows connections to api.pinecone.io
• Try running the server manually to see detailed error output: PINECONE_API_KEY=<your-key> npx @pinecone-database/mcp

Contributing

We welcome your collaboration in improving the developer MCP experience. Please submit issues in the GitHub issue tracker. Information about contributing can be found in CONTRIBUTING.md.

mcp-server-qdrant: A Qdrant MCP server

The Model Context Protocol (MCP) is an open protocol that enables
seamless integration between LLM applications and external data sources and tools. Whether you're building an
AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to
connect LLMs with the context they need.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Overview

An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine.
It acts as a semantic memory layer on top of the Qdrant database.

Components

Tools

1. qdrant-store
   • Store some information in the Qdrant database
   • Input:
     • information (string): Information to store
     • metadata (JSON): Optional metadata to store
     • collection_name (string): Name of the collection to store the information in. This field is required if there are no default collection name.
       If there is a default collection name, this field is not enabled.
   • Returns: Confirmation message
2. qdrant-find
   • Retrieve relevant information from the Qdrant database
   • Input:
     • query (string): Query to use for searching
     • collection_name (string): Name of the collection to store the information in. This field is required if there are no default collection name.
       If there is a default collection name, this field is not enabled.
   • Returns: Information stored in the Qdrant database as separate messages

Environment Variables

Configuration is done via environment variables. The only command-line argument is --transport, used to select the transport protocol.

[!NOTE]
You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.

FastMCP Environment Variables

Since mcp-server-qdrant is based on FastMCP, it also supports all the FastMCP environment variables. The most
important ones are listed below:

[!NOTE]
Before version 0.9.0, these settings used the FASTMCP_SERVER_ prefix, for example FASTMCP_SERVER_HOST.
The old FASTMCP_SERVER_HOST, FASTMCP_SERVER_PORT, and FASTMCP_SERVER_DEBUG names still work but are deprecated
and will be removed in a future release. If both names are set, the new one wins.

Installation

Using uvx

When using uvx no specific installation is needed to directly run mcp-server-qdrant.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant

Transport Protocols

The server supports different transport protocols that can be specified using the --transport flag:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
uvx mcp-server-qdrant --transport sse

Supported transport protocols:

• stdio (default): Standard input/output transport, might only be used by local MCP clients
• sse: Server-Sent Events transport, perfect for remote clients
• streamable-http: Streamable HTTP transport, perfect for remote clients, more recent than SSE

The default transport is stdio if not specified.

When SSE transport is used, the server will listen on the specified port and wait for incoming connections. The default
port is 8000, however it can be changed using the FASTMCP_PORT environment variable.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
FASTMCP_PORT=1234 \
uvx mcp-server-qdrant --transport sse

Using Docker

A Dockerfile is available for building and running the MCP server:

## Build the container
docker build -t mcp-server-qdrant .

## Run the container
docker run -p 8000:8000 \
  -e FASTMCP_HOST="0.0.0.0" \
  -e QDRANT_URL="http://your-qdrant-server:6333" \
  -e QDRANT_API_KEY="your-api-key" \
  -e COLLECTION_NAME="your-collection" \
  mcp-server-qdrant

[!TIP]
Please note that we set FASTMCP_HOST="0.0.0.0" to make the server listen on all network interfaces. This is
necessary when running the server in a Docker container.

Installing via Smithery

To install Qdrant MCP Server for Claude Desktop automatically via Smithery:

npx @smithery/cli install mcp-server-qdrant --client claude

Manual configuration of Claude Desktop

To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your
claude_desktop_config.json:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
      "QDRANT_API_KEY": "your_api_key",
      "COLLECTION_NAME": "your-collection-name",
      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
    }
  }
}

For local Qdrant mode:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
      "COLLECTION_NAME": "your-collection-name",
      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
    }
  }
}

This MCP server will automatically create a collection with the specified name if it doesn't exist.

By default, the server will use the sentence-transformers/all-MiniLM-L6-v2 embedding model to encode memories.
For the time being, only FastEmbed models are supported.

Support for other tools

This MCP server can be used with any MCP-compatible client. For example, you can use it with
Cursor and VS Code, which provide built-in support for the Model Context
Protocol.

Using with Cursor/Windsurf

You can configure this MCP server to work as a code search tool for Cursor or Windsurf by customizing the tool
descriptions:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="code-snippets" \
TOOL_STORE_DESCRIPTION="Store reusable code snippets for later retrieval. \
The 'information' parameter should contain a natural language description of what the code does, \
while the actual code should be included in the 'metadata' parameter as a 'code' property. \
The value of 'metadata' is a Python dictionary with strings as keys. \
Use this whenever you generate some code snippet." \
TOOL_FIND_DESCRIPTION="Search for relevant code snippets based on natural language descriptions. \
The 'query' parameter should describe what you're looking for, \
and the tool will return the most relevant code snippets. \
Use this when you need to find existing code snippets for reuse or reference." \
uvx mcp-server-qdrant --transport sse # Enable SSE transport

In Cursor/Windsurf, you can then configure the MCP server in your settings by pointing to this running server using
SSE transport protocol. The description on how to add an MCP server to Cursor can be found in the Cursor
documentation. If you are
running Cursor/Windsurf locally, you can use the following URL:

http://localhost:8000/sse

[!TIP]
We suggest SSE transport as a preferred way to connect Cursor/Windsurf to the MCP server, as it can support remote
connections. That makes it easy to share the server with your team or use it in a cloud environment.

This configuration transforms the Qdrant MCP server into a specialized code search tool that can:

1. Store code snippets, documentation, and implementation details
2. Retrieve relevant code examples based on semantic search
3. Help developers find specific implementations or usage patterns

You can populate the database by storing natural language descriptions of code snippets (in the information parameter)
along with the actual code (in the metadata.code property), and then search for them using natural language queries
that describe what you're looking for.

[!NOTE]
The tool descriptions provided above are examples and may need to be customized for your specific use case. Consider
adjusting the descriptions to better match your team's workflow and the specific types of code snippets you want to
store and retrieve.

If you have successfully installed the mcp-server-qdrant, but still can't get it to work with Cursor, please
consider creating the Cursor rules so the MCP tools are always used when
the agent produces a new code snippet. You can restrict the rules to only work for certain file types, to avoid using
the MCP server for the documentation or other types of content.

Using with Claude Code

You can enhance Claude Code's capabilities by connecting it to this MCP server, enabling semantic search over your
existing codebase.

Setting up mcp-server-qdrant

1. Add the MCP server to Claude Code:

   # Add mcp-server-qdrant configured for code search
   claude mcp add code-search \
   -e QDRANT_URL="http://localhost:6333" \
   -e COLLECTION_NAME="code-repository" \
   -e EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
   -e TOOL_STORE_DESCRIPTION="Store code snippets with descriptions. The 'information' parameter should contain a natural language description of what the code does, while the actual code should be included in the 'metadata' parameter as a 'code' property." \
   -e TOOL_FIND_DESCRIPTION="Search for relevant code snippets using natural language. The 'query' parameter should describe the functionality you're looking for." \
   -- uvx mcp-server-qdrant
   

   Copy

2. Verify the server was added:

   claude mcp list
   

   Copy

Using Semantic Code Search in Claude Code

Tool descriptions, specified in TOOL_STORE_DESCRIPTION and TOOL_FIND_DESCRIPTION, guide Claude Code on how to use
the MCP server. The ones provided above are examples and may need to be customized for your specific use case. However,
Claude Code should be already able to:

1. Use the qdrant-store tool to store code snippets with descriptions.
2. Use the qdrant-find tool to search for relevant code snippets using natural language.

Run MCP server in Development Mode

The MCP server can be run in development mode using the mcp dev command. This will start the server and open the MCP
inspector in your browser.

COLLECTION_NAME=mcp-dev fastmcp dev src/mcp_server_qdrant/server.py

Using with VS Code

For one-click installation, click one of the install buttons below:

Manual Installation

Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "qdrantUrl",
        "description": "Qdrant URL"
      },
      {
        "type": "promptString",
        "id": "qdrantApiKey",
        "description": "Qdrant API Key",
        "password": true
      },
      {
        "type": "promptString",
        "id": "collectionName",
        "description": "Collection Name"
      }
    ],
    "servers": {
      "qdrant": {
        "command": "uvx",
        "args": ["mcp-server-qdrant"],
        "env": {
          "QDRANT_URL": "${input:qdrantUrl}",
          "QDRANT_API_KEY": "${input:qdrantApiKey}",
          "COLLECTION_NAME": "${input:collectionName}"
        }
      }
    }
  }
}

Or if you prefer using Docker, add this configuration instead:

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "qdrantUrl",
        "description": "Qdrant URL"
      },
      {
        "type": "promptString",
        "id": "qdrantApiKey",
        "description": "Qdrant API Key",
        "password": true
      },
      {
        "type": "promptString",
        "id": "collectionName",
        "description": "Collection Name"
      }
    ],
    "servers": {
      "qdrant": {
        "command": "docker",
        "args": [
          "run",
          "-p", "8000:8000",
          "-i",
          "--rm",
          "-e", "QDRANT_URL",
          "-e", "QDRANT_API_KEY",
          "-e", "COLLECTION_NAME",
          "mcp-server-qdrant"
        ],
        "env": {
          "QDRANT_URL": "${input:qdrantUrl}",
          "QDRANT_API_KEY": "${input:qdrantApiKey}",
          "COLLECTION_NAME": "${input:collectionName}"
        }
      }
    }
  }
}

Alternatively, you can create a .vscode/mcp.json file in your workspace with the following content:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "qdrantUrl",
      "description": "Qdrant URL"
    },
    {
      "type": "promptString",
      "id": "qdrantApiKey",
      "description": "Qdrant API Key",
      "password": true
    },
    {
      "type": "promptString",
      "id": "collectionName",
      "description": "Collection Name"
    }
  ],
  "servers": {
    "qdrant": {
      "command": "uvx",
      "args": ["mcp-server-qdrant"],
      "env": {
        "QDRANT_URL": "${input:qdrantUrl}",
        "QDRANT_API_KEY": "${input:qdrantApiKey}",
        "COLLECTION_NAME": "${input:collectionName}"
      }
    }
  }
}

For workspace configuration with Docker, use this in .vscode/mcp.json:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "qdrantUrl",
      "description": "Qdrant URL"
    },
    {
      "type": "promptString",
      "id": "qdrantApiKey",
      "description": "Qdrant API Key",
      "password": true
    },
    {
      "type": "promptString",
      "id": "collectionName",
      "description": "Collection Name"
    }
  ],
  "servers": {
    "qdrant": {
      "command": "docker",
      "args": [
        "run",
        "-p", "8000:8000",
        "-i",
        "--rm",
        "-e", "QDRANT_URL",
        "-e", "QDRANT_API_KEY",
        "-e", "COLLECTION_NAME",
        "mcp-server-qdrant"
      ],
      "env": {
        "QDRANT_URL": "${input:qdrantUrl}",
        "QDRANT_API_KEY": "${input:qdrantApiKey}",
        "COLLECTION_NAME": "${input:collectionName}"
      }
    }
  }
}

Contributing

If you have suggestions for how mcp-server-qdrant could be improved, or want to report a bug, open an issue!
We'd love all and any contributions.

Testing mcp-server-qdrant locally

The MCP inspector is a developer tool for testing and debugging MCP
servers. It runs both a client UI (default port 5173) and an MCP proxy server (default port 3000). Open the client UI in
your browser to use the inspector.

QDRANT_URL=":memory:" COLLECTION_NAME="test" \
fastmcp dev src/mcp_server_qdrant/server.py

Once started, open your browser to http://localhost:5173 to access the inspector interface.

License

This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the
software, subject to the terms and conditions of the Apache License 2.0. For more details, please see the LICENSE file
in the project repository.

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