compare_infer

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

With no annotations provided, the description carries the full burden. It discloses core behavior: multi-image comparison and reasoning, per-image annotations, and relation categories, but it does not describe output format, potential limitations, or how mode/alpha parameters influence behavior, leaving notable uncertainty for the agent.

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 a single, dense sentence that front-loads the purpose, then specifies input count, per-image annotations, and reasoning categories. It contains no filler or redundant elaboration.

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 tool has no output schema, optional mode/alpha parameters, and a nested items_per_image object, so the description needs to cover return behavior and parameter effects. It adequately conveys the core purpose and relation types but omits output format and mode/alpha semantics, making it moderately complete but not fully sufficient for all invocation contexts.

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 coverage is high (80%) and the schema already documents items_per_image as an optional per-image mapping, mode defaults, and alpha. The description adds little beyond restating that images can carry independent annotations, so it does not meaningfully expand parameter understanding beyond the schema.

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 identifies the tool as performing multi-image joint reasoning on 2-4 images, with optional per-image annotations and enumerated relation types (differences, causality, timing, overall conclusion). It is specific enough about scope, though it does not explicitly contrast it with the similarly named sibling tool compare_images.

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 multi-image reasoning scenarios and mentions the supported relationship types, but it provides no explicit guidance on when to prefer this tool over alternatives like compare_images or reason_graph, and no exclusions or conditions are stated.

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