run_opencypher_query

THE RISK

High Risk

Why run_opencypher_query needs a policy

This tool runs arbitrary openCypher queries against a graph database. While openCypher is a query language rather than general-purpose code, execution of arbitrary queries can traverse, analyze, or manipulate graph structures in ways that depend entirely on user input.

From the tool's definition Tool description states it 'Executes the provided openCypher against the graph,' indicating dynamic code execution of graph query language.

Documented attack patterns abuse exactly the kind of access run_opencypher_query gives an agent:

POLICY

How to control run_opencypher_query

PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon SageMaker AI MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for run_opencypher_query:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "run_opencypher_query": {
      "limits": [
        {
          "counter": "run_opencypher_query_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

run_opencypher_query stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

Create a free account and register Amazon SageMaker AI MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

RATE-LIMIT THIS TOOL →

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EXPLORE

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Go deeper

Govern code and CI agents → Branch allowlists, approval on merges, and rate caps on the build and pipeline tools agents trigger.
High-risk tools in Amazon SageMaker AI MCP Server → Every high-risk tool this server exposes, in one view.
All high-risk MCP tools → Every high-risk tool across the catalogue, and the policy pattern that contains them.
The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Agent sandbox → Bounding what an agent can execute and where the boundary should sit.
Least privilege for MCP → Granting agents the minimum tool surface the task needs.
Rate Limiting MCP Tool Calls: A Practical Guide →
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PolicyLayer vs alternatives → How deterministic, per-call MCP policy compares with gateways, identity layers, and authorisation platforms.

FAQ

Questions about run_opencypher_query

What does the run_opencypher_query tool do? +

Executes the provided openCypher against the graph. It is categorised as a Execute tool in the Amazon SageMaker AI MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on run_opencypher_query? +

Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for run_opencypher_query: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Amazon SageMaker AI MCP Server. Nothing to install.

What risk level is run_opencypher_query? +

run_opencypher_query is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit run_opencypher_query? +

Yes. Add a rate_limit block to the run_opencypher_query rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block run_opencypher_query completely? +

Set action: deny in the PolicyLayer policy for run_opencypher_query. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides run_opencypher_query? +

run_opencypher_query is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Amazon SageMaker AI MCP Server tool call.

Start from Amazon SageMaker AI MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

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