MCP server
Pinecone MCP server
Pinecone's official developer MCP server lets AI search Pinecone docs, manage indexes, upsert records, search and rerank.
- Official server
- Databases
At a glance
- Maintained by
- Pinecone
- Tools exposed
- 9 listed below
- Reviewed
- September 24, 2026
Why it matters
What it does and why it matters
Pinecone is a managed vector database used behind many AI search and knowledge features. Its developer MCP server helps developers build with it: search the Pinecone documentation, create and describe indexes, upsert records, search them and rerank results.
That shortens the path from idea to working retrieval, because a coding assistant can set up an index, load sample data and test searches in one conversation.
It can create indexes and upsert records, and we found no delete tools listed. Documentation search works without an API key, while index tools need one. Use a key for a development project while building, and keep production keys out of coding assistants.
Once the prototype works, we usually move loading and indexing into a proper pipeline, so the assistant searches but does not change production indexes.
Use cases
Best business use cases
Build retrieval faster
Docs answers
Search testing
Index inspection
RAG prototypes
Tools
Available tools
Tool names as published by the maintainer at review time. Versions change, so check the repository for the current list.
Read and write: Read and write: can create indexes and upsert records; no delete tools listed.
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search-docsSearches the official Pinecone documentation.
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list-indexesLists Pinecone indexes.
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describe-indexDescribes an index's configuration.
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describe-index-statsShows statistics about data in an index.
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create-index-for-modelCreates an index with an integrated inference model.
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upsert-recordsInserts or updates records in an index.
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search-recordsSearches records by text query.
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cascading-searchSearches across several indexes.
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rerank-documentsReranks records or documents.
Workflows
Example workflows
Prototype a knowledge search
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1
Connect with a development API key.
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2
Ask the assistant to create an index with integrated embedding.
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3
Upsert a sample of documents.
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4
Run test searches and rerank results.
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5
Review quality before building the full pipeline.
Debug a search problem
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1
A user reports poor search results.
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2
Ask for the index statistics.
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3
Run the user query through search.
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4
Compare with cascading search and reranking.
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5
Adjust the setup based on the results.
Prompts
Example prompts
“Create an index for our help articles using integrated embedding.”
“Search the index for "how do I reset my password" and rerank the top ten.”
“How do namespaces work in Pinecone?”
Setup
Installation and configuration
Examples for Claude Desktop and Claude Code. Replace the placeholder values with your own keys, and store secrets outside version control.
Claude Desktop: claude_desktop_config.json
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y",
"@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "<your pinecone api key>"
}
}
}
}Claude Code: terminal
claude mcp add pinecone -e PINECONE_API_KEY="YOUR_API_KEY" -- npx -y @pinecone-database/mcp- Local server: stdio. npm: @pinecone-database/mcp.
- Needs: PINECONE_API_KEY (needed for index tools; docs search works without it).
Security
Security considerations
-
Development keys
Use keys for development projects in coding assistants.
-
Separate production
Keep production indexes out of reach of experiments.
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Data in indexes
Avoid loading personal data into test indexes.
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Review changes
Check which indexes the assistant created.
Related servers
Servers that pair well with it
Qdrant
Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
Chroma
Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
Weaviate
An MCP server built into Weaviate that lets AI read collection settings, list tenants, run hybrid search and, when allowed, upsert objects.
Keep exploring
Solutions and services that use it
Related services
View all related services- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- Web applications Custom browser-based software for the work your team does every day, from booking tools to operations systems.
- Dashboards and reporting Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
Solutions
View all solutions- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
Industries
View all industriesCase studies
View all case studiesGuides and articles
View all guides and articles- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- How to automate reporting with AI and MCP Build reports that assemble themselves from your systems through MCP, with a written summary people can trust.
- How to set up automated reporting Replace manual weekly and monthly reports with dashboards and summaries that update themselves.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
AI models
View all ai models- text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
- Cohere Embed v4 Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
- Voyage 4 Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
- Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
- Gemini Embedding 2 Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
Glossary terms
View all glossary terms- RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Backend The backend is the part of an application that runs on servers, storing data, applying business rules and serving the frontend through APIs.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
Yes. It is maintained by Pinecone.
Only for index tools. Documentation search works without one.
We found no delete tools listed at review time.