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Pinecone

Pinecone

Official

Managed vector database for semantic search and retrieval in AI agents.

Score 80(?)PineconeApache-2.068Verified Top MCPs for AI & Machine Learning →
InstallView on GitHub

Quick answer

What it does

Provides MCP tools for index management, vector upserts, similarity queries, namespace operations, and metadata filtering against a Pinecone serverless or dedicated cluster.

Best for

  • Semantic search over private corpora
  • Production-grade retrieval
  • Multi-tenant agent memory
  • Hybrid metadata + vector queries

Not for

  • Self-hosted-only requirements
  • Hobby projects (free tier is generous but capped)

Setup recipe

Pick your client, then follow the three steps.

  1. 1

    Install

    claude_desktop_config.json
    {
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "${PINECONE_API_KEY}"
      }
    }
  }
}

    Paste under mcpServers. Fully quit and reopen Claude after editing.

    CLI or .mcp.json
    # export PINECONE_API_KEY=YOUR_API_KEY
claude mcp add pinecone -- npx -y @pinecone-database/mcp

    Run from your repo. Commit .mcp.json to share with your team.

    .cursor/mcp.json
    {
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "${PINECONE_API_KEY}"
      }
    }
  }
}

    Global path: ~/.cursor/mcp.json. Reload window after editing.

    .vscode/mcp.json
    {
  "servers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "${PINECONE_API_KEY}"
      }
    }
  }
}

    VS Code uses the "servers" key (not "mcpServers").

    ~/.codeium/windsurf/mcp_config.json
    {
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "${PINECONE_API_KEY}"
      }
    }
  }
}

    Open via Cascade → hammer icon → Configure.

    cline_mcp_settings.json
    {
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "${PINECONE_API_KEY}"
      }
    }
  }
}

    Open via the Cline sidebar → MCP Servers → Edit.

    ~/.continue/config.json
    {
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "npx",
          "args": [
            "-y",
            "@pinecone-database/mcp"
          ],
          "env": {
            "PINECONE_API_KEY": "${PINECONE_API_KEY}"
          }
        }
      }
    ]
  }
}

    Continue uses modelContextProtocolServers with a transport block.

    ~/.codex/config.toml
    # ~/.codex/config.toml
[mcp_servers.pinecone]
command = "npx"
args = [
  "-y",
  "@pinecone-database/mcp",
]
env = { PINECONE_API_KEY = "${PINECONE_API_KEY}" }

    Codex uses TOML. Each server is a [mcp_servers.<name>] subtable.

    ~/.config/zed/settings.json
    {
  "context_servers": {
    "pinecone": {
      "command": {
        "path": "npx",
        "args": [
          "-y",
          "@pinecone-database/mcp"
        ]
      },
      "env": {
        "PINECONE_API_KEY": "${PINECONE_API_KEY}"
      }
    }
  }
}

    Zed calls them "context_servers". Settings live-reload on save.

    ChatGPT → Apps directory

    Pinecone doesn't ship a hosted HTTPS endpoint today. ChatGPT supports remote MCP servers only — to use this server in ChatGPT you'll need to deploy it to a public HTTPS URL first (e.g. via Cloudflare Workers or Vercel) or wait for an official remote build.

  2. 2

    Set required secrets

    Set PINECONE_API_KEY in your shell environment before launching your MCP client.

  3. 3

    Try a minimum working prompt

    Minimum working prompt pending verification. Try any prompt from the MCP’s README once installed.

Tools & permissions

Tools list pending verification. The server exposes tools over MCP; we haven’t yet parsed its capability manifest into this page. Check the GitHub repo for the authoritative list.

Security & scope

Access scope
Read + write
Sandbox
API key grants full project access. Use separate keys per environment.
Gotchas
  • No row-level security — anyone with the key can read/write any vector in the project.
  • Embeddings cost money on every upsert; idempotent upserts matter.

Agent prompt pack

— copy into Claude, Cursor, or ChatGPT.
Paste into Claude, Cursor, or ChatGPT. Edit the [brackets] before sending.
Recommend the best MCP servers for [task: e.g. ai & machine learning work] in [client: Claude].

Constraints:
- Prefer tools that are [official | open-source | read-only] — pick what matters for my use case.
- Exclude MCPs that require [e.g. a paid plan, OAuth-only flows, remote-only transport].
- Return at most 3 picks, ranked.

For each pick include:
1. One-sentence rationale.
2. The ready-to-paste install snippet for my client.
3. Any required secrets I need to create before installing.

Cross-check the top-mcps.com listing: https://top-mcps.com/top-mcps-for-ai-machine-learning
Compare Pinecone against a real alternative. Swap the second MCP in [brackets] if you want a different match.
Compare Pinecone MCP vs [Qdrant MCP] for the following job: [describe the job, e.g. "let an agent create GitHub issues on bug triage"].

Judge them on:
- Setup time and complexity (what a new user hits first).
- Auth model (none / API key / OAuth 2.1) and credential risk.
- Transport (stdio / Streamable HTTP / SSE) and where the server runs.
- Required secrets and the blast radius if they leak.
- Operational risk in an unattended agent loop.
- Which one is "good enough" for a weekend prototype vs. production.

End with one sentence: which should I pick for my scenario, which is: [my scenario].

References:
- https://top-mcps.com/mcp/pinecone
- top-mcps.com listing for Qdrant
Asks the agent to install and verify. Works inside Claude Code, Cursor Agent, Codex CLI.
Install the Pinecone MCP server for my [client: Claude] at the default config path for that client.

Use the exact install snippet published at https://top-mcps.com/mcp/pinecone (fetch https://top-mcps.com/mcp/pinecone.json for the canonical server.json if you can read URLs).

Before finishing:
1. Create the required secrets (PINECONE_API_KEY) and put them in the appropriate env block — do not hard-code them.
2. Restart or reload the client so it picks up the new server.
3. Verify the server is connected (green / running state) and at least one tool is listed.
4. If anything fails, read the client's MCP logs and report the exact error — do not silently retry.

Confirm when done and list the tools the server now exposes.

Frequently asked questions

What changed

— 2 updates tracked.

  1. Refreshed install snippets and fact sheet; verified for 2026.

  2. Initial directory listing.

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