roboflow_inference_run

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

With no annotations, the description carries the full burden for behavioral disclosure. It only repeats the image input format from the schema and says 'run inference', but offers no insight into network calls, timeouts, error handling, return behavior, or side effects. Minimal transparency.

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, front-loaded with the core purpose and a short note on accepted input formats. No filler or redundant elaboration, every word earns its place.

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 straightforward inference tool with no output schema, the description is adequate but not complete. It omits any details about expected response structure, failure modes, or model ID usage beyond what the schema states, though the complexity is low enough that this is still a reasonable baseline.

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 100%, so the schema fully documents all five parameters. The description's note about image input formats duplicates the schema's image property and adds no semantic value beyond what the schema already provides, scoring at the baseline of 3.

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?

Description clearly states the tool runs object detection or segmentation inference on a Roboflow model, which is a specific verb+resource+task combo. It distinguishes from sibling tools like roboflow_inference_classify (classification) and roboflow_workflow_run (workflows).

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 when to use it (for object detection/segmentation inference) and implicitly differentiates from classification, but it does not explicitly name alternatives or state exclusions. It provides clear context but no direct 'use this instead of X' guidance.

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