MCP100 IndexData & Databases

ShortlistFirst-party MCP

Pinecone MCP

by Pinecone

Updated September 24, 2026

Data & DatabasesEducation & ResearchSoftware & TechnologyAccess · Self-hosted

MCP100 verdict

Manage and query Pinecone indexes and search Pinecone documentation through an MCP-enabled vector-search workflow.

MCP100 Assessment

Editorial verdict

Promising vector-database tooling, but it remains on the Shortlist until write-capable index actions, API-key exposure, supported-index limitations, and usage cost receive deeper independent review.

Reviewed July 24, 2026

Task Fit

Mapped

Working with structured data

Trust

Status warning

conflicting_first_party_tool_surface_makes_tool_contract_unknown

Maintenance

Activity observed

A dated repository or package update is available; editorial maintenance review is still pending.

Popularity

Signals available

Visible usage signals are shown for discovery only, not as proof of safety.

Best for

Manage and query Pinecone indexes and search Pinecone documentation.
Teams already using Pinecone that can test with non-sensitive data and least privilege.

Not ideal for

Users who need an MCP100-recommended recommendation today
Unattended production workflows before permissions and failure behavior are reviewed

Avoid if

The connected data or account cannot be safely exposed to the selected AI client
You cannot verify least-privilege access and review write-capable tool calls

Trade-offs

First-party provenance and a clear task improve confidence, but do not replace an independent permissions and usability review.
Client transport compatibility does not prove that authentication, scopes, failure handling, and every tool behave safely.

Evidence scope

Evidence

Current source snapshot

Popularity

72 MCP repository stars

25.1k package downloads / 30 days

Repository identity: pinecone-io/pinecone-mcp

Package identity: @pinecone-database/mcp · version 0.2.1

Official npm package @pinecone-database/mcp: 25,070 downloads / 30 days.

Stars observed September 24, 2026 · Downloads observed July 29, 2026

Exact reviewed repository metric.

Exact reviewed package distribution.

Maintenance

Publisher activity observed February 5, 2026

Current source snapshot

Trust review

Source relationship: First-party MCP

Reviewed July 24, 2026

Sources

GitHub repository ↗Official documentation ↗Package registry ↗

Supporting evidence (1)

Publisher article ↗

Evidence-backed setup

Install & Agents

Choose your MCP client. A client connects to the MCP server; the language model is selected inside that client. Only stored, source-backed configurations are shown.

Claude Code

Local stdio

MCP100 logic checked

claude mcp add pinecone -e PINECONE_API_KEY="YOUR_API_KEY" -- npx "-y" "@pinecone-database/mcp"

The publisher connection details were combined with this client's official MCP transport configuration; installation was not executed by MCP100.

Verify safely: Restart Claude Code, confirm pinecone tools are listed, and perform one read-only test with non-sensitive data.

Uninstall and revoke

Remove pinecone from Claude Code, restart the client, remove any separately installed package, and revoke dedicated credentials if used.

Evidence & methodology

Publisher-documented setup is not the same as MCP100 execution testing. “MCP100 logic checked” means the stored configuration shape was reviewed, not that third-party MCP software was run.

Evidence checked 2026-07-24

Evidence 1 ↗Evidence 2 ↗

Read additional publisher installation details

From the publisher README

Publisher installation details

Original client setup and configuration retained from the publisher documentation.

View original README ↗

Publisher-documented clients

Claude DesktopCursorGemini CLI

These names appear in the publisher's installation documentation. MCP100-verified configurations are shown separately above when available.

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 v20 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.

Loading optional publisher installation details…

Show source-catalog configuration

npx -y @pinecone-database/mcp@0.2.1

Read publisher documentation

Publisher documentation

Publisher README

Original product, capability, tool, and usage documentation from the MCP publisher. Installation sections appear under Install & Agents.

View original README ↗

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.

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-index-for-model: Creates a new 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

Only indexes with integrated inference are supported. Assistants, indexes without integrated inference, standalone embeddings, and vector search are not supported.

Troubleshooting

MCP server not appearing in your AI tool

Ensure Node.js v20 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.

Loading optional publisher documentation…

FAQ

What can Pinecone MCP do?

Manage and query Pinecone indexes and search Pinecone documentation.

Can Pinecone MCP create and query indexes?

Yes, with an important scope limit. The reviewed server can list and inspect indexes, create an index for an integrated-inference model, upsert records, search records, run cascading search, and rerank documents. Its supported data operations focus on integrated-inference indexes rather than every Pinecone index or product surface.

Does Pinecone MCP work with Codex?

Yes. MCP100 reviewed an evidence-backed setup for Codex, but has not independently verified it with a live runtime test.

Is this an official MCP?

Pinecone is listed as First-party MCP in MCP100’s current evidence.

Current official resources:

Pinecone Developer MCP Server

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.

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-index-for-model: Creates a new 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

Only indexes with integrated inference are supported. Assistants, indexes without integrated inference, standalone embeddings, and vector search are not supported.

Troubleshooting

MCP server not appearing in your AI tool

Ensure Node.js v20 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.

Promising vector-database tooling, but it remains on the Shortlist until write-capable index actions, API-key exposure, supported-index limitations, and usage cost receive deeper independent review.

Best for

Not ideal for

Avoid if

Trade-offs

Evidence

Popularity

Maintenance

Trust review

Sources

Install & Agents

Claude Code

Publisher installation details

Setup

Configure Cursor

Configure Claude desktop

Use as a Gemini CLI extension

What it does

Representative uses

Important limits

Context Footprint

Access & risk

Other options

Google MCP Toolbox for Databases

Redis MCP

Supabase MCP

Publisher README

Pinecone Developer MCP Server

Usage

Example prompts

Tools

Limitations

Troubleshooting

MCP server not appearing in your AI tool

"Invalid API key" or authentication errors

Tools not working as expected

Connection issues

Contributing

FAQ

Publisher installation details

Setup

Configure Cursor

Configure Claude desktop

Use as a Gemini CLI extension

Publisher README

Pinecone Developer MCP Server

Usage

Example prompts

Tools

Limitations

Troubleshooting

MCP server not appearing in your AI tool

"Invalid API key" or authentication errors

Tools not working as expected

Connection issues

Contributing