Run Model

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It does not mention side effects (e.g., whether running a model modifies data, requires permissions, or has performance implications). It also fails to describe the return value or error behavior, which is critical for a run command.

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 sentences, zero wasted words. Front-loaded with the core purpose, followed by necessary parameter clarification. Highly efficient.

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?

The description covers identification of the model and parameter mapping but omits crucial context such as return value, success/failure indication, and whether the tool is destructive or safe. Given no output schema and no annotations, this is only partially 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 description coverage is 0%, but the description adds meaningful context: it clarifies that 'model' accepts a registered id or .model3 file path, and 'parameters' maps input names to values with examples (layer ids/paths, numbers). This compensates well for the lack of schema descriptions.

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 it runs a Processing model, specifying two distinct ways to identify the model (by registered id or .model3 file path). This differentiates it from sibling tools like execute_processing (which runs individual algorithms) and list_processing_models (which lists models). The verb 'run' and resource 'Processing model' are specific and unambiguous.

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 for running pre-existing models but does not explicitly state when to use this tool versus alternatives like execute_processing or execute_processing_batch. No when-not or alternative guidance is provided, leaving the agent to infer context from sibling tool names.

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