Medium Risk

create_graph_project

Create a new Neo4j graph database project from a hierarchical JSON schema. ⚠️ GRAPH SCHEMA FORMAT — READ BEFORE CREATING: Graph schemas define nodes (entities) and relationships, NOT flat database tables. Each field is a dict with "type" and optional "required": true (defaults to false). SCHEMA S...

Part of the RationalBloks server.

create_graph_project can modify RationalBloks data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use create_graph_project to create or modify resources in RationalBloks. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call create_graph_project repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach RationalBloks.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "create_graph_project": {
      "limits": [
        {
          "counter": "create_graph_project_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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These attack patterns abuse exactly the kind of access create_graph_project gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so create_graph_project only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the create_graph_project tool do? +

Create a new Neo4j graph database project from a hierarchical JSON schema. ⚠️ GRAPH SCHEMA FORMAT — READ BEFORE CREATING: Graph schemas define nodes (entities) and relationships, NOT flat database tables. Each field is a dict with "type" and optional "required": true (defaults to false). SCHEMA STRUCTURE: { "nodes": { "EntityName": { "description": "What this entity represents", "flat_labels": ["AdditionalLabel"], "schema": { "field_name": {"type": "string", "required": true}, "other_field": {"type": "integer"} } } }, "relationships": { "RELATIONSHIP_TYPE": { "from": "EntityName", "to": "OtherEntity", "cardinality": "MANY_TO_MANY", "data_schema": { "field_name": {"type": "date"} } } } } FIELD TYPES: string, integer, float, boolean, date, json CARDINALITY OPTIONS: ONE_TO_ONE, ONE_TO_MANY, MANY_TO_ONE, MANY_TO_MANY HIERARCHICAL NODES: Nest entities inside parent entities to create type hierarchies. Child entities inherit parent labels automatically. Example: { "nodes": { "Animal": { "description": "Base animal entity", "flat_labels": ["LivingThing"], "schema": { "name": {"type": "string", "required": true}, "habitat": {"type": "string"} }, "Dog": { "description": "A dog (inherits Animal labels)", "flat_labels": ["Pet"], "schema": { "breed": {"type": "string", "required": true}, "trained": {"type": "boolean"} } } } }, "relationships": { "OWNS": { "from": "Person", "to": "Animal", "cardinality": "ONE_TO_MANY" } } } RULES: 1. "nodes" key is REQUIRED — must contain at least one entity 2. Each entity needs "description" and "schema" with field definitions 3. Each field is {"type": "...", "required": true/false} — required defaults to false 4. Relationship "from"/"to" must reference defined node names 5. Relationship types should be UPPER_SNAKE_CASE 6. Entity names should be PascalCase 7. Automatic fields (id, created_at, updated_at) are NOT needed 8. Use get_graph_template_schemas FIRST to see valid examples WORKFLOW: 1. Use get_graph_template_schemas to see valid examples 2. Create schema following the rules above 3. Call this tool 4. Monitor with get_job_status (2-5 min deployment) After creation, use get_job_status with returned job_id to monitor deployment.. It is categorised as a Write tool in the RationalBloks MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on create_graph_project? +

Register the RationalBloks MCP server in PolicyLayer and add a rule for create_graph_project: 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 RationalBloks. Nothing to install.

What risk level is create_graph_project? +

create_graph_project is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit create_graph_project? +

Yes. Add a rate_limit block to the create_graph_project 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 create_graph_project completely? +

Set action: deny in the PolicyLayer policy for create_graph_project. 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 create_graph_project? +

create_graph_project is provided by the RationalBloks MCP server (pypi:rationalbloks-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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