Model Context Protocol (MCP)

Memgraph offers Memgraph the Memgraph MCP Server

• a lightweight server implementation of the Model Context Protocol (MCP) designed to connect Memgraph with LLMs.

Run Memgraph MCP server

1. Install uv
2. Install Claude for Desktop.
3. Add the Memgraph server to Claude config:

Open the config file in your favorite text editor. The location of the config file depends on your operating system:

MacOS/Linux

~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows

$env:AppData\Claude\claude_desktop_config.json

Add the following config:

{
    "mcpServers": {
      "mpc-memgraph": {
        "command": "uv",
        "args": [
            "run",
            "--with",
            "mcp-memgraph",
            "--python",
            "3.13",
            "mcp-memgraph"
        ]
     }
   }
}

Chat with the database

1. Run Memgraph MAGE:

   docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=True

   The --schema-info-enabled configuration setting is set to True to allow LLM to run SHOW SCHEMA INFO query.

2. Open Claude Desktop and see the Memgraph tools and resources listed. Try it out! (You can load dummy data from Memgraph Lab Datasets)

Run Memgraph MCP server with Docker

cd /path/to/ai-toolkit
docker build -f integrations/mcp-memgraph/Dockerfile -t mcp-memgraph:latest .

docker run --rm mcp-memgraph:latest

docker run --rm -i -e MCP_TRANSPORT=stdio mcp-memgraph:latest

docker run --rm \
  -p 8000:8000 \
  -e MEMGRAPH_URL=bolt://memgraph:7687 \
  -e MEMGRAPH_USER=myuser \
  -e MEMGRAPH_PASSWORD=password \
  mcp-memgraph:latest

{
  "servers": {
    "mcp-memgraph-http": {
      "url": "http://localhost:8000/mcp/"
    }
  }
}

{
  "servers": {
    "mcp-memgraph": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "MCP_TRANSPORT=stdio",
        "mcp-memgraph:latest"
      ]
    }
  }
}

{
  "servers": {
    "mcp-memgraph": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "MCP_TRANSPORT=stdio",
        "-e", "MEMGRAPH_URL=bolt://memgraph:7687",
        "-e", "MEMGRAPH_USER=myuser",
        "-e", "MEMGRAPH_PASSWORD=mypassword",
        "mcp-memgraph:latest"
      ]
    }
  }
}

Multi-tenant authentication (OIDC / JWT)

The MCP server can optionally enforce OIDC / JWT authentication on the streamable-HTTP transport and route each authenticated session to a different Memgraph logical database based on JWT claims. This is disabled by default — when off, the server behaves exactly as documented in the sections above.

Enable it when you want to:

• Serve different users different Memgraph databases on the same MCP deployment.
• Place MCP behind an OIDC provider (Keycloak, Auth0, Okta, Entra ID, …).
• Capture per-user audit trails on tool calls.

Auth-only tools

When MCP_AUTH_ENABLED=true, the server exposes two additional tools:

Environment variables

All of these are no-ops when MCP_AUTH_ENABLED=false (the default). When auth is enabled, the server fails fast at startup if any of the three required variables are missing.

How it works

1. Every request to /mcp must carry Authorization: Bearer <JWT>.
2. The middleware validates the JWT signature against the IdP’s JWKS (cached in-process and auto-refreshed when an unknown kid arrives).
3. It verifies iss, aud, exp, and the required scope.
4. It reads the tenants array claim, intersects it with MCP_TENANT_CATALOG, and builds a per-session SessionAuth keyed by Mcp-Session-Id.
5. The session’s current_tenant defaults to the JWT’s default_tenant claim (when present and allowed), otherwise the first allowed tenant.
6. Each tool call routes to the Memgraph database with the same name as current_tenant.

Within a session, users can switch among their allowed databases with the use_database tool, and discover them with list_databases.

Discovery endpoints exposed when auth is enabled

The discovery document fetched from the upstream IdP is cached in-process and re-fetched on the next request if the cache is empty (e.g., the IdP was down on the first attempt).

DCR intercept (workaround for Claude Code)

Some MCP clients — notably current Claude Code (see anthropics/claude-code#26675) — force Dynamic Client Registration even when a pre-registered clientId is configured. Setting MCP_AUTH_STATIC_CLIENT_ID=<your-public-client-id> makes the MCP server return the same pre-registered client ID for every DCR request, sidestepping the bug.

When that variable is set, the PRM document also advertises the MCP server itself as the authorization_server so DCR requests come back to the MCP server instead of going directly to the IdP. All other OAuth flows (authorize, token, JWKS) still happen against the real IdP.

What you need on the IdP side

In any OIDC provider, roughly:

1. A public client with PKCE enabled, with redirect URI patterns matching the IDEs you’ll use (e.g., http://localhost:*, vscode://*, cursor://*, claude://*).
2. A tenants claim mapper that emits a JSON-array claim of the user’s tenant memberships (in Keycloak: a Group Membership mapper; in Auth0/Okta: a custom rule reading group or role attributes).
3. An audience claim mapper baking your MCP_AUTH_AUDIENCE value into issued tokens.
4. A scope (default: mcp:tools) attached to the client.
5. For each tenant in MCP_TENANT_CATALOG, a corresponding Memgraph logical database created via CREATE DATABASE <name>.

A complete Keycloak example (single-pod, dev-mode) is available in the keycloak-k8s reference setup.

Run Memgraph MCP server on Kubernetes

A dedicated memgraph-mcp Helm chart deploys the MCP server on Kubernetes alongside an existing Memgraph backend. See the Memgraph MCP Helm chart section for installation steps and configuration options.

Resources

• Introducing the Memgraph MCP Server: A blog post on how to run Memgraph MCP Server and what are the future plans.

MCP server in Lab

Memgraph Lab has become an MCP Client and now has built-in support for MCP servers, including the Memgraph MCP Server. This means you can build your agentic applications powered by Memgraph MCP, Toolkit and Lab.

More details about the MCP connection in Memgraph Lab can be found in the Memgraph Lab documentation.

MCP server for docs

Memgraph provides a public MCP server for its documentation. Connect it to any MCP-capable AI assistant to ask questions about Memgraph and get answers grounded in the current docs, with sources. There is no API key or sign-in required, just one endpoint.

• Endpoint: https://mcp-docs.memgraph.com
• Tool provided: search_memgraph_knowledge_sources

Claude Code (CLI):

claude mcp add --transport http memgraph-docs https://mcp-docs.memgraph.com

Cursor (~/.cursor/mcp.json or project .cursor/mcp.json):

{
  "mcpServers": {
    "memgraph-docs": {
      "url": "https://mcp-docs.memgraph.com"
    }
  }
}

VS Code (.vscode/mcp.json):

{
  "servers": {
    "memgraph-docs": {
      "type": "http",
      "url": "https://mcp-docs.memgraph.com"
    }
  }
}

Claude Desktop and other stdio-only clients (needs Node.js, via the mcp-remote bridge):

{
  "mcpServers": {
    "memgraph-docs": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote@latest",
        "https://mcp-docs.memgraph.com"
      ]
    }
  }
}

After adding it, restart your client and ask a Memgraph question — the assistant pulls the relevant documentation automatically.

Sample prompts to get started

Once connected, ask your assistant these in plain language. It will call the docs server and answer with sources. Copy one, then adapt it to your own schema and data.

Getting started and core querying

• “How do I install Memgraph with Docker and connect to it from Python?”
• “I’m coming from Neo4j. What Cypher differences should I know about in Memgraph, and can I reuse my existing queries?”
• “Show me how to bulk-load a CSV of nodes and relationships efficiently using LOAD CSV.”
• “How do I define indexes and constraints in Memgraph, and when should I add them?”

Performance and production

• “Memgraph is in-memory. How do I configure durability with snapshots and WAL so I don’t lose data on restart?”
• “My traversal query is slow. How do I read a query plan with EXPLAIN and PROFILE, and what do I optimize first?”
• “What’s the recommended way to run Memgraph in high availability with replication?”
• “How much memory will I need for a graph with 50M nodes and 200M relationships, and how do I control memory limits?”

Graph algorithms and analytics (MAGE)

• “Which MAGE algorithms are available, and how do I run PageRank and community detection (Louvain) on my graph?”
• “How do I find the shortest path between two nodes, and how do weighted vs. unweighted shortest path differ?”
• “How do I write a custom graph algorithm as a Python query module and call it from Cypher?”
• “Set up a dynamic PageRank that updates as new edges stream in, instead of recomputing each time.”

GraphRAG and AI

• “How do I build a GraphRAG pipeline on Memgraph? Walk me through schema, ingestion, and retrieval.”
• “How do I use vector search in Memgraph to store embeddings and run similarity queries alongside graph traversals?”
• “Show me how to combine a vector similarity search with a multi-hop Cypher traversal to ground an LLM answer in my knowledge graph.”
• “What’s the best way to model a knowledge graph for retrieval, and how do I keep embeddings in sync as data changes?”

Art of the possible

• “Build me a real-time fraud detection pattern: detect when a new transaction closes a suspicious cycle between accounts.”
• “Model a recommendation engine over users, products, and purchases, and write the Cypher to surface ‘others also bought’ results.”
• “I have a supply chain with suppliers, parts, and factories. Show me how to trace every downstream product affected by one failed supplier.”
• “Design a service dependency graph and query for the blast radius if one service goes down.”