model_metadata_read

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

With no annotations, the description fully discloses it is a read-only operation (no modification). Lists all returned data types and clarifies scope (embedded metadata vs lora_catalog). No contradictions.

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?

Single paragraph, front-loaded with purpose, then returns, then usage guidance, then parameter clarification. Every sentence adds value. No fluff.

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 2-param read tool with no output schema, the description fully explains what is returned and the context (embedded vs lora_catalog). No gaps remain.

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 clear descriptions. The description rephrases parameter details (category = ComfyUI folder, name = filename with .safetensors) but adds little new information beyond the schema. Baseline 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?

Clearly states verb 'Read' and resource 'model file's CURRENT embedded metadata'. Specifies it's for curating in Model Explorer and distinguishes from siblings like model_metadata_fetch_civitai and model_metadata_propose by noting it reads embedded metadata first.

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?

Explicitly states 'Call this FIRST when the user wants to improve/curate a model's embedded .safetensors metadata'. Also clarifies it is for embedded metadata, not lora_catalog, implying when not to use (e.g., for separate catalog data).

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