create_appCreate a new application from a natural language prompt. The AI generates a complete web app with pages, components, styling, and data models. Returns a job_id - poll get_job_status to track progress. When the job is done, the app is ready.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Name for the app (1-100 chars) | |
| prompt | Yes | Detailed description of the app to build | |
| description | No | Optional short description |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses asynchronous behavior (returns a job_id and requires polling) and states the outcome ('app is ready' when done). This goes beyond the annotations, which only indicate non-read-only, non-idempotent, and non-destructive. No contradictions 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no fluff. The main purpose is front-loaded, followed by return value and next steps. Each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, but the description specifies the return value (job_id) and how to track it (poll get_job_status), which is essential for the agent to use it correctly. For a creation tool with only three parameters, this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage for all three parameters, so the baseline is 3. The description adds minor context by calling the 'prompt' a 'natural language prompt' and mentioning generated aspects, but does not substantially enhance parameter meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb and resource: 'Create a new application' and specifies it builds a complete web app with pages, components, styling, and data models. It distinguishes from siblings like create_agent (creates agents) and update_app (modifies an app) by focusing on creation from a natural language prompt.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: use this to create a new app from a prompt, and it tells you to poll get_job_status for progress. However, it does not explicitly list alternatives or when not to use it, so it lacks explicit exclusions or sibling comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes with detailed descriptions, but there are some overlapping pairs like read_app_file/read_app_files and create_entity_records vs seed_entity, which could cause misselection. Singular/plural variants and compatibility tools introduce minor ambiguity, but the majority are well-separated.
Tool names predominantly follow a consistent verb_noun pattern (e.g., create_app, get_entities, delete_secret). There are some variations like 'agency_create_client' and 'seed_entity' that deviate slightly, but the overall convention is predictable and readable.
With 82 tools, the server is far above the typical range and feels overwhelming. Even for a full platform API, the count is extreme and likely increases selection complexity. A more curated set would improve navigability without sacrificing capability.
The tool surface is exceptionally comprehensive, covering app lifecycle, file operations, entity CRUD, versioning, A/B testing, secrets, integrations, domains, agents, scheduling, policies, and member management. No obvious missing operations for the platform's scope; it even includes validation and workflow guidance tools.