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Create Project

create_project

Create a new RationalBloks project from a JSON schema.

⚠️ CRITICAL RULES - READ BEFORE CREATING SCHEMA:

  1. FLAT FORMAT (REQUIRED): ✅ CORRECT: {users: {email: {type: "string", max_length: 255}}} ❌ WRONG: {users: {fields: {email: {type: "string"}}}} DO NOT nest under 'fields' key!

  2. FIELD TYPE REQUIREMENTS: • string: MUST have "max_length" (e.g., max_length: 255) • decimal: MUST have "precision" and "scale" (e.g., precision: 10, scale: 2) • datetime: Use "datetime" NOT "timestamp" • ALL fields: MUST have "type" property

  3. AUTOMATIC FIELDS (DON'T define): • id (uuid, primary key) • created_at (datetime) • updated_at (datetime)

  4. USER AUTHENTICATION: ❌ NEVER create "users", "customers", "employees" tables with email/password ✅ USE built-in app_users table

    Example: { "employee_profiles": { "user_id": {type: "uuid", foreign_key: "app_users.id", required: true}, "department": {type: "string", max_length: 100} } }

  5. AUTHORIZATION: Add user_id → app_users.id to enable "only see your own data"

    Example: { "orders": { "user_id": {type: "uuid", foreign_key: "app_users.id"}, "total": {type: "decimal", precision: 10, scale: 2} } }

  6. FIELD OPTIONS: • required: true/false • unique: true/false • default: any value • enum: ["val1", "val2"] • foreign_key: "table.id"

AVAILABLE TYPES: string, text, integer, decimal, boolean, uuid, date, datetime, json, uuid_array, integer_array, text_array, float_array

Array types store PostgreSQL native arrays with automatic GIN indexing: • uuid_array: UUID[] — for sets of references (e.g., tensor coordinates) • integer_array: BIGINT[] — for dimension indices, integer sets • text_array: TEXT[] — for tags, categories, label sets • float_array: DOUBLE PRECISION[] — for weight vectors, scores GIN-indexed operators: @> (contains), <@ (contained_by), && (overlaps)

BACKEND ENGINE: • python (default): FastAPI backend — mature, full-featured • rust: Axum backend — faster cold starts, lower memory, high performance

WORKFLOW:

  1. Use get_template_schemas FIRST to see valid examples

  2. Create schema following ALL rules above

  3. Call this tool (optionally choose backend_type: "python" or "rust")

  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
schemaYesJSON schema in FLAT format (table_name → field_name → properties). Every field MUST have a 'type' property. Use get_template_schemas to see valid examples.
cluster_idYesREQUIRED — BYOC resource pool ID (from list_clusters) to deploy this 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
backend_typeNoBackend engine: 'python' (FastAPI, default) or 'rust' (Axum, faster). Default: python

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only indicate non-readonly/open-world. The description adds substantial behavioral detail: mandatory BYOC deployment with cluster_id, schema format constraints, automatic field injection, array type GIN indexing, and expected deployment time (2-5 min). No contradiction with annotations.

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 numbered rules, clear sections, and code examples. It is front-loaded with the core purpose and each rule earns its place given the schema complexity. Slightly verbose but effectively organized.

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

Completeness5/5

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

The description covers schema construction rules, available types, backend options, prerequisites, deployment workflow, and monitoring. It even mentions the returned job_id despite no output schema, so the agent has all necessary context to invoke the tool and follow up.

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?

Even though schema descriptions cover 100% of parameters, the description vastly expands semantics: it details the flat schema format with examples, required properties per field type, automatic fields to avoid, authentication patterns, and clarifies backend_type differences and the meaning of cluster_id in the BYOC context.

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 clear action phrase: 'Create a new RationalBloks project from a JSON schema.' This specifies the verb, resource, and input format, and the lengthy schema rules distinguish it from graph-specific tools like create_graph_project.

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 gives explicit steps: use get_template_schemas first, optionally choose backend_type, then monitor via get_job_status. It also notes the cluster_id prerequisite from list_clusters. However, it does not directly mention alternatives like create_graph_project, leaving the contrast to the name and siblings.

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.