Run Model

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

No annotations provided, so description carries full burden. It only mentions model identification and parameters, but fails to disclose side effects, whether the model modifies data, or what output/feedback to expect. For a run tool, behavioral details are insufficient.

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?

Two concise sentences, front-loaded with purpose. Every word adds value. No redundancy or filler.

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?

Given the complexity of running a Processing model, the description is too short. It omits what happens after execution, error handling, progress feedback, and does not reference output despite no output schema. Sibling tools like execute_processing have more detailed descriptions.

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 description coverage is 0%, so description compensates well: 'model' explained as id or file path, 'parameters' as a mapping of input names to values with examples (layer ids, numbers). This adds meaning beyond the generic schema. Could be more precise with valid value formats.

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 tool runs a Processing model, specifying two identification methods (registered id or .model3 file path). It distinguishes itself from sibling tools like execute_processing (which runs algorithms) and create_processing_model (which creates models).

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 implies usage contexts (running models), but does not explicitly contrast with similar tools like execute_processing or execute_processing_batch. No guidance on when not to use or prerequisites.

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