Pinecone MCP Server vs Weaviate: both are coding tools on Scriptiii AI Tools. Pinecone MCP Server pricing: Free. Free to use. (reported by the maker) Weaviate pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
| Field | Pinecone MCP Server | Weaviate |
|---|---|---|
| Page | Pinecone MCP Server | Weaviate |
| Pricing (maker-reported) | Free. Free to use. | Free. Free to use. |
| Upvotes | 0 | 0 |
| Best for | ai-agents, mcp-server, vector-databases | ai-agents, tool, vector-databases |
| Tagline | Connects Pinecone projects and documentation search to Cursor, Claude and other MCP clients. | Open-source vector database that stores objects and vectors with hybrid search and filtering. |
| Website | Visit Pinecone MCP Server | Visit Weaviate |
Pricing and pricing details come from the maker's submission. They are not independently verified.
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:
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.
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.
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.
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.
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.
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.
Here are some prompts you can try with your AI assistant:
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.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.
node --versionnpx is available in your PATH: which npxPINECONE_API_KEY environment variable is set correctly in your MCP configurationapi.pinecone.ioPINECONE_API_KEY=<your-key> npx @pinecone-database/mcpWe 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.
Weaviate is a cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification.
Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre-computed vector embeddings. Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and .
To get started quickly, have a look at one of these tutorials:
Weaviate offers multiple installation and deployment options:
See the installation docs for more deployment options, such as AWS and GCP.
You can easily start Weaviate and a local vector embedding model with Docker.
Create a docker-compose.yml file:
services:
weaviate:
image: cr.weaviate.io/semitechnologies/weaviate:1.36.0
ports:
- "8080:8080"
- "50051:50051"
environment:
ENABLE_MODULES: text2vec-model2vec
MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080
# A lightweight embedding model that will generate vectors from objects during import
text2vec-model2vec:
image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M
Start Weaviate and the embedding service with:
docker compose up -d
Install the Python client (or use another client library):
pip install -U weaviate-client
The following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:
import weaviate
from weaviate.classes.config import Configure, DataType, Property
## Connect to Weaviate
client = weaviate.connect_to_local()
## Create a collection
client.collections.create(
name="Article",
properties=[Property(name="content", data_type=DataType.TEXT)],
vector_config=Configure.Vectors.text2vec_model2vec(), # Use a vectorizer to generate embeddings during import
# vector_config=Configure.Vectors.self_provided() # If you want to import your own pre-generated embeddings
)
## Insert objects and generate embeddings
articles = client.collections.get("Article")
articles.data.insert_many(
[
{"content": "Vector databases enable semantic search"},
{"content": "Machine learning models generate embeddings"},
{"content": "Weaviate supports hybrid search capabilities"},
]
)
## Perform semantic search
results = articles.query.near_text(query="Search objects by meaning", limit=1)
print(results.objects[0])
client.close()
This example uses the Model2Vec vectorizer, but you can choose any other embedding model provider or bring your own pre-generated vectors.
Weaviate provides client libraries for several programming languages:
There are also additional community-maintained libraries.
Weaviate exposes REST API, gRPC API, and GraphQL API to communicate with the database server.
These features enable you to build AI-powered applications:
⚡ Fast Search Performance: Perform complex semantic searches over billions of vectors in milliseconds. Weaviate's architecture is built in Go for speed and reliability, ensuring your AI applications are highly responsive even under heavy load. See our ANN benchmarks for more info.
🔌 Flexible Vectorization: Seamlessly vectorize data at import time with integrated vectorizers from OpenAI, Cohere, HuggingFace, Google, and more. Or you can import your own vector embeddings.
🔍 Advanced Hybrid & Image Search: Combine the power of semantic search with traditional keyword (BM25) search, image search and advanced filtering to get the best results with a single API call.
🤖 Integrated RAG & Reranking: Go beyond simple retrieval with built-in generative search (RAG) and reranking capabilities. Power sophisticated Q&A systems, chatbots, and summarizers directly from your database without additional tooling.
📈 Production-Ready & Scalable: Weaviate is built for mission-critical applications. Go from rapid prototyping to production at scale with native support for horizontal scaling, multi-tenancy, replication, and fine-grained role-based access control (RBAC).
💰 Cost-Efficient Operations: Radically lower resource consumption and operational costs with built-in vector compression. Vector quantization and multi-vector encoding reduce memory usage with minimal impact on search performance.
⏱️ Object TTL: Automatically expire and remove stale data with configurable time-to-live settings per collection, with full RBAC and multi-tenancy support.
For a complete list of all functionalities, visit the official Weaviate documentation.
Weaviate Agent Skills is a collection of skills for AI coding agents (Claude Code, Cursor, GitHub Copilot, and others) that enable them to work with Weaviate more accurately and efficiently. Skills cover searching, querying, collection management, data import, and full application blueprints (RAG, agentic RAG, chatbots, and more).
Install with:
npx skills add weaviate/agent-skills
These demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub.
We also maintain extensive repositories of Jupyter Notebooks and TypeScript code snippets that cover how to use Weaviate features and integrations:
Weaviate integrates with many external services:
| Category | Description | Integrations |
|---|---|---|
| Cloud Hyperscalers | Large-scale computing and storage | AWS, Google |
| Compute Infrastructure | Run and scale containerized applications | Modal, Replicate, Replicated |
| Data Platforms | Data ingestion and web scraping | Airbyte, Aryn, Boomi, Box, Confluent, Astronomer, Context Data, Databricks, Firecrawl, IBM, Unstructured |
| LLM and Agent Frameworks | Build agents and generative AI applications | Agno, Composio, CrewAI, DSPy, Dynamiq, Haystack, LangChain, LlamaIndex, N8n, Semantic Kernel |
| Operations | Tools for monitoring and analyzing generative AI workflows | AIMon, Arize, Cleanlab, Comet, DeepEval, Langtrace, LangWatch, Nomic, Patronus AI, Ragas, TruLens, Weights & Biases |
We welcome and appreciate contributions! Please see our Contributor guide for the development setup, code style guidelines, testing requirements and the pull request process. If you wish to contribute to our Enterprise Edition, your contributions will be under the BSD license.
Join our Community forum to discuss ideas and get help.
This repository contains code in our Community Edition and our Enterprise Edition. Most of this repository is available under the BSD 3-Clause License. Files in our wl/ folder are only available under a commercial license with a license key issued by us. If you are interested in a commercial license, please contact us at http://weaviate.io/enterprise-edition. Our binaries, available for download here, combine these two elements. Enterprise features require a license key.
Please see our licensing FAQ for more information.
| Delivery | Community Edition | Enterprise Edition |
|---|---|---|
| Source code | Under BSD in our GitHub repo | Available for review in folder wl/ of our GitHub repo, but not licensed there |
| Docker image | Under BSD. Single Docker image combines this with Enterprise Edition | Available under our enterprise license. Requires license key to enable enterprise edition features. |