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RationalBloks

by io.github.rationalbloks

Create Graph Project

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 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesProject name
schemaYesGraph schema with 'nodes' and optionally 'relationships' keys. Use get_graph_template_schemas to see valid examples.
cluster_idYesREQUIRED — BYOC resource pool ID (from list_clusters) to deploy this graph project onto your own cluster. Owned hosting is retired: a project we operate must run on your own infrastructure. Register a pool via the Resource Pools UI first, then pass its id here.
descriptionNoOptional project description

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are present and consistent, but the description adds significant behavioral context: it reveals asynchronous deployment ('2-5 min'), the returned job_id for monitoring, the requirement for a BYOC cluster_id with owned hosting retired, and that automatic fields are not needed. This goes well beyond the annotation flags and prepares the agent for real-world side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with headings (SCHEMA STRUCTURE, RULES, WORKFLOW) and an example. It is front-loaded with a clear purpose statement. Some redundancy exists (e.g., 'Use get_graph_template_schemas' appears in both Rule 8 and Workflow step 1), but overall each section earns its place given the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is thorough for a complex tool: covers schema format, rules, example, workflow, and deployment monitoring. It implies the return value is a job_id for get_job_status, but does not explicitly state the full response shape or error behavior. Given the absence of an output schema, a slightly more explicit return description would push it to 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description substantially expands parameter understanding. It provides a full graph schema format, field types, cardinality options, hierarchical node examples, and rules that clarify the 'schema' parameter. It also explains the cluster_id requirement and why it's mandatory, adding meaning beyond the schema's brief property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Create a new Neo4j graph database project from a hierarchical JSON schema.' This clearly distinguishes it from sibling tools like create_project (relational) and graph node/relationship creation tools. The extensive schema guide reinforces the unique functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The WORKFLOW section explicitly instructs to use get_graph_template_schemas first, then call, then monitor with get_job_status. This provides clear usage context. It does not explicitly name alternatives like create_project or say when not to use this tool, but the requirements (BYOC cluster_id, graph schema) make the appropriate use case evident.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation4/5

Most tools are clearly differentiated by domain (project vs graph_project) and action (create, get, list, delete). The main ambiguity is get_project vs get_project_info, which both claim to return detailed project information. Otherwise tool boundaries are clear.

Naming Consistency4/5

The server follows a strong verb_noun convention, with parallel naming for graph and non-graph tools (create_project/create_graph_project, deploy_staging/deploy_graph_staging). Minor deviations include bulk_create_graph_nodes and fulltext_search_graph, but patterns remain predictable.

Tool Count2/5

48 tools is a heavy surface, even when accounting for the two parallel product domains (relational and graph). Many tools are near-duplicates across domains, and the count exceeds the 25-tool threshold that feels manageable. It would benefit from consolidation or sub-servers.

Completeness4/5

Both project types have full life-cycle coverage: create, schema management, deployment, rollback, and deletion, plus graph data operations including bulk, search, and traversal. Minor gaps exist, such as no update_graph_relationship and the redundant get_project/get_project_info pair, but agents can accomplish core workflows.

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