Pinecone MCP Server vs Qdrant

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.

Vector Search Engine for the next generation of AI applications

Qdrant (read: quadrant) is a vector similarity search engine and vector database.
It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.
Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.

With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Qdrant is also available as a fully managed Qdrant Cloud ⛅ including a free tier.

Quick Start • Agent Skills • Client Libraries • Demo Projects • Integrations • Contact

Getting Started

Agent Skills

Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.

Client-Server

To experience the full power of Qdrant locally, run the container with this command:

docker run -p 6333:6333 qdrant/qdrant

Note that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to secure your instance.

Now you can connect to the server with any client. For example, using Python:

from qdrant_client import QdrantClient

client = QdrantClient(url="http://localhost:6333")

Before deploying Qdrant to production, be sure to read our installation and security guides.

Clients

Qdrant offers the following client libraries to help you integrate it into your application stack:

• Official:
  • Go client
  • Rust client
  • JavaScript/TypeScript client
  • Python client
  • .NET/C# client
  • Java client
• Community:
  • Kotlin
  • PHP

Qdrant Edge

Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.

To get started with Qdrant Edge from Python or Rust, initialize an instance of EdgeShard, which exposes methods to manage data, query it, and restore snapshots. For example:

from qdrant_edge import Distance, EdgeConfig, EdgeVectorParams, EdgeShard, Point, UpdateOperation

shard = EdgeShard.create("./shard", EdgeConfig(
    vectors={"my-vector": EdgeVectorParams(size=4, distance=Distance.Cosine)}
))
shard.update(UpdateOperation.upsert_points([
    Point(id=1, vector={"my-vector": [0.1, 0.2, 0.3, 0.4]}, payload={"color": "red"})
]))

Where Do I Go from Here?

• Quick Start Guide
• Detailed Documentation
• Take the Qdrant Essentials course
• Follow this tutorial to create a semantic search engine with Qdrant

Demo Projects

Discover Semantic Text Search 🔍

Unlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!

Explore Similar Image Search - Food Discovery 🍕

There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name. Check it out!

Master Extreme Classification - E-Commerce Product Categorization 📺

Enter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. Play with it online!

API

REST

Qdrant provides a REST API with an OpenAPI 3.0 specification, enabling client generation for virtually any framework or programming language.

You can also download the raw OpenAPI definitions.

gRPC

For faster, production-tier searches, Qdrant also provides a gRPC interface.

Features

Dense, Sparse, and Multi Vector Search

Qdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models like ColBERT.

Filtering on Payload

Attach any JSON payload to your vectors and filter on it using a rich set of conditions—keyword matching, full-text, numeric ranges, geo-locations, and more—combined with should, must, and must_not clauses.

Hybrid Search

Combine multiple vectors in a single query to get the best of semantic understanding and keyword precision, with results merged via configurable fusion strategies, such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF).

Vector Quantization and On-Disk Storage

Built-in quantization cuts RAM usage by up to 97% and lets you tune the trade-off between search speed and precision.

Distributed Deployment

Scale horizontally with sharding and replication, and update or resize collections with zero downtime.

Highlighted Features

• Faceting - aggregate search results by payload values.
• Recommendation - use positive and negative examples to find similar points.
• Discovery - constrain search to a specific region of the vector space.
• Search Relevance Tuning - tools for adjusting search results, such as Maximal Marginal Relevance (MMR) and the Relevance Feedback Query.
• Multitenancy - scalable partitioning of data for multi-user environments.
• Observability - comprehensive metrics, telemetry, and audit logging for monitoring and debugging.
• Query Planning and Payload Indexes - leverages stored payload information to optimize query execution strategy.
• SIMD Hardware Acceleration - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.
• GPU Support - for accelerated indexing, with support for NVIDIA and AMD GPUs.
• Async I/O - uses io_uring to maximize disk throughput utilization even on network-attached storage.
• Write-Ahead Logging - ensures data persistence with update confirmation, even during power outages.

Web UI

Web UI provides a visual way to interact with your data and monitor the health of your deployment. It enables you to explore your collections, manage data, interact with the REST API, and more.

Integrations

Qdrant integrates with the tools you're already using across every stage of your AI stack. You can connect to embedding providers, AI application frameworks, and data pipeline tools, as well as observability platforms for monitoring and tracing your vector search in production. No-code and low-code automation platforms are supported too. Refer to the Ecosystem page for the complete list.

Contributing

We are happy to receive your contributions! Before opening a pull request, please read our Contributing Guide.

[!IMPORTANT]
Our development branch is dev, not master. Please fork the repo, branch from dev, and open your pull request against dev. PRs targeting master will be asked to retarget.

Contacts

• Have questions? Join our Discord channel or mention @qdrant_engine on X
• Want to stay in touch with the latest releases? Subscribe to our Newsletters
• Looking for a managed cloud? Check pricing. Need something personalized? We're at info@qdrant.tech

License

Qdrant is licensed under the Apache License, Version 2.0. View a copy of the License file.