Get detailed information about a specific AI model, including its inputs, outputs, parameters, and plan requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | The unique identifier of the model (e.g., 'REAL-ESRGAN-x4', 'DeblurDiff'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, so the safety profile is well covered. The description adds useful context about the returned data (inputs, outputs, parameters, plan requirements), which goes beyond annotations. However, it doesn't disclose any additional behavioral traits like authentication requirements or rate limits, but this is not critical given the 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?
The description is a single, front-loaded sentence that directly states the action ('Get detailed information about a specific AI model') and the key contents ('inputs, outputs, parameters, and plan requirements'). It contains zero fluff and earns each word.
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?
For a tool with one simple parameter and no output schema, the description adequately explains what the tool returns, which compensates for the missing output schema. It covers the main purpose and key data fields. However, it doesn't explicitly differentiate from 'get_model_parameters', which is a closely related sibling, leaving a small gap in contextual selection.
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?
The input schema has 100% coverage for the single parameter 'model_id', including a description with examples. The tool description adds no parameter-specific information beyond what the schema provides, so the baseline score of 3 is appropriate—the schema does the heavy lifting for parameter semantics.
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 uses a specific verb-resource pair: 'Get detailed information about a specific AI model.' It clearly distinguishes from siblings like 'list_models' (listing) and 'get_model_parameters' (parameters only) by stating it includes 'inputs, outputs, parameters, and plan requirements.' This leaves no ambiguity about what the tool does.
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?
The description clearly implies when to use the tool: when you need detailed information about a single model, including plan requirements. It doesn't explicitly name alternatives or state exclusions, but the context is strong enough for an agent to select it over list_models or get_model_parameters. No explicit 'when not to use' is provided, so it's not a perfect 5.
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.
Each tool targets a distinct resource and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.
All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.
At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.
The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.