run-model

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not mention that predictions are typically asynchronous, that running a model may incur costs or take significant time, or that the response will be a prediction object rather than the final result. This is a significant gap given the dynamic nature of Replicate models.

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 two sentences, with the main purpose front-loaded and the workflow compactly stated. No wasted words; every sentence contributes actionable guidance.

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?

This tool has high complexity (dynamic input schema, external dependencies on get-model-info) and no output schema. The description does not explain what the tool returns or how the agent should handle the prediction lifecycle, such as polling with check-prediction. This leaves a critical gap for a multi-step workflow.

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 already provides detailed descriptions for both parameters, including the model identifier format and the instruction to use get-model-info for input parameters. Since schema coverage is 100%, the description adds little beyond reiterating the workflow, which is also embedded in the schema. Baseline of 3 is appropriate.

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 and resource: "Run a prediction on any Replicate model." This clearly states what the tool does and distinguishes it from siblings like search-models, get-model-info, and check-prediction by focusing on the action of running the prediction.

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 explicitly provides a workflow: if the model name is fuzzy, call search-models first, then get-model-info, then this tool. This gives clear when-to-use guidance and names the alternative tools, making it easy for an agent to sequence correctly.

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