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Weaviate

Weaviate

Official

Vector database with first-class hybrid (vector + keyword) and modular embeddings.

Score 79(?)WeaviateBSD-3-Clause161Verified Top MCPs for AI & Machine Learning →
InstallView on GitHub

Quick answer

What it does

Exposes Weaviate's schema, data, and search APIs as MCP tools. Manage classes, ingest objects with vectors, run hybrid queries, and call generative-search workflows.

Best for

  • Hybrid (BM25 + vector) retrieval
  • Generative search at the DB layer
  • Multi-modal indexing
  • Schema-first vector workflows

Not for

  • Zero-ops use cases
  • Pure vector workloads where simplicity wins

Setup recipe

Pick your client, then follow the three steps.

  1. 1

    Install

    claude_desktop_config.json
    {
  "mcpServers": {
    "weaviate": {
      "command": "uvx",
      "args": [
        "mcp-server-weaviate"
      ],
      "env": {
        "WEAVIATE_URL": "${WEAVIATE_URL}",
        "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
      }
    }
  }
}

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

    CLI or .mcp.json
    # export WEAVIATE_URL=https://your-cluster.weaviate.network
# export WEAVIATE_API_KEY=YOUR_API_KEY
claude mcp add weaviate -- uvx mcp-server-weaviate

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

    .cursor/mcp.json
    {
  "mcpServers": {
    "weaviate": {
      "command": "uvx",
      "args": [
        "mcp-server-weaviate"
      ],
      "env": {
        "WEAVIATE_URL": "${WEAVIATE_URL}",
        "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
      }
    }
  }
}

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

    .vscode/mcp.json
    {
  "servers": {
    "weaviate": {
      "command": "uvx",
      "args": [
        "mcp-server-weaviate"
      ],
      "env": {
        "WEAVIATE_URL": "${WEAVIATE_URL}",
        "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
      }
    }
  }
}

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

    ~/.codeium/windsurf/mcp_config.json
    {
  "mcpServers": {
    "weaviate": {
      "command": "uvx",
      "args": [
        "mcp-server-weaviate"
      ],
      "env": {
        "WEAVIATE_URL": "${WEAVIATE_URL}",
        "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
      }
    }
  }
}

    Open via Cascade → hammer icon → Configure.

    cline_mcp_settings.json
    {
  "mcpServers": {
    "weaviate": {
      "command": "uvx",
      "args": [
        "mcp-server-weaviate"
      ],
      "env": {
        "WEAVIATE_URL": "${WEAVIATE_URL}",
        "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
      }
    }
  }
}

    Open via the Cline sidebar → MCP Servers → Edit.

    ~/.continue/config.json
    {
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "uvx",
          "args": [
            "mcp-server-weaviate"
          ],
          "env": {
            "WEAVIATE_URL": "${WEAVIATE_URL}",
            "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
          }
        }
      }
    ]
  }
}

    Continue uses modelContextProtocolServers with a transport block.

    ~/.codex/config.toml
    # ~/.codex/config.toml
[mcp_servers.weaviate]
command = "uvx"
args = [
  "mcp-server-weaviate",
]
env = { WEAVIATE_URL = "${WEAVIATE_URL}", WEAVIATE_API_KEY = "${WEAVIATE_API_KEY}" }

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

    ~/.config/zed/settings.json
    {
  "context_servers": {
    "weaviate": {
      "command": {
        "path": "uvx",
        "args": [
          "mcp-server-weaviate"
        ]
      },
      "env": {
        "WEAVIATE_URL": "${WEAVIATE_URL}",
        "WEAVIATE_API_KEY": "${WEAVIATE_API_KEY}"
      }
    }
  }
}

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

    ChatGPT → Apps directory

    Weaviate 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 WEAVIATE_URL, WEAVIATE_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 cluster-wide access. Use multi-tenancy primitives to isolate data per tenant.
Gotchas
  • Schema changes are forward-only in OSS; plan migrations.
  • Generative modules call external LLMs; their privacy policies apply.

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 Weaviate against a real alternative. Swap the second MCP in [brackets] if you want a different match.
Compare Weaviate MCP vs [Pinecone 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/weaviate
- top-mcps.com listing for Pinecone
Asks the agent to install and verify. Works inside Claude Code, Cursor Agent, Codex CLI.
Install the Weaviate 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/weaviate (fetch https://top-mcps.com/mcp/weaviate.json for the canonical server.json if you can read URLs).

Before finishing:
1. Create the required secrets (WEAVIATE_URL, WEAVIATE_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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Compared with Weaviate

Side-by-side breakdowns for the choices people most often weigh against this MCP.

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