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

It discloses that computation happens 'via HuggingFace Inference API and VLM BYOK calls', adding context about external dependencies and authentication beyond the readOnly/idempotent annotations. It also clarifies that guide is for reference only. No contradiction with annotations.

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, key information front-loaded, and every clause adds value. It communicates the unified nature, action modes, reference vs. compute distinction, and external API dependencies with no redundancy.

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 high schema coverage, output schema, and detailed annotation context, the description is sufficient at a high level. It could have elaborated on pipeline behavior or external API prerequisites, but those details are covered by the schema and 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 coverage is 100% with each parameter tagged by action mode, so the description adds no detailed parameter semantics beyond the schema. It only restates action modes that the schema already enumerates.

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 opens with 'Unified tool for multimodal AI evaluation', names specific metrics (CLIP, FID, VQA), and explicitly lists actions 'guide', 'clip_score', 'fid_score', 'vqa_accuracy', 'pipeline'. This clearly identifies the tool's function and distinguishes it from generic utility siblings.

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 provides explicit internal guidance: set action=guide for reference thresholds, or set action to metric actions for computation, and states 'One tool for both reference and computation.' However, it does not mention sibling alternatives or state when not to use it, so explicit exclusions are missing.

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