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duplicate_model

Create a copy of a stormwater model for testing scenarios, returning the new model name to analyze different hydraulic conditions.

Instructions

Duplicates a model and returns the new model name. Use this for testing scenarios.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_nameYes
new_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns the new model name, which is useful, but fails to describe critical behaviors: whether duplication requires specific permissions, if it's a read-only or destructive operation, what happens to the original model, or any rate limits. For a mutation tool with zero annotation coverage, this is a significant gap.

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

Conciseness5/5

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

The description is extremely concise with two sentences that each serve a clear purpose: the first states the action and return value, the second provides usage context. There is zero wasted language, and it's front-loaded with the core functionality.

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

Completeness3/5

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

Given that there is an output schema (which should document the return value), the description doesn't need to explain return values in detail. However, for a mutation tool with no annotations and 0% schema coverage for parameters, the description is incomplete—it lacks details on permissions, side effects, and parameter meanings. It's minimally adequate but with clear gaps.

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

Parameters2/5

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

Schema description coverage is 0%, meaning the schema provides no descriptions for the two parameters. The description adds no information about what 'model_name' or 'new_name' represent, their formats, constraints, or examples. It doesn't compensate for the lack of schema documentation, leaving parameters semantically unclear.

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

Purpose4/5

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

The description clearly states the verb ('duplicates') and resource ('a model'), specifying what the tool does. It distinguishes from siblings like 'list_models' or 'run_model' by focusing on duplication rather than listing or execution. However, it doesn't explicitly differentiate from 'upload_model' or 'prompt_model_upload' in terms of source, which keeps it from a perfect score.

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 description provides explicit context for when to use this tool: 'for testing scenarios'. This gives clear guidance on its intended purpose. However, it doesn't specify when NOT to use it or mention alternatives like 'upload_model' for non-duplication cases, which prevents a score of 5.

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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