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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond the annotations: the specific underlying model, the fact that it calls the HuggingFace Inference API, and that output length is controllable via max_length/min_length. It does not mention rate limits or error behavior, which is a minor gap.

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 compact and well-structured: it states the core purpose first, then the model's training suitability, then length controls, then custom model options, and finally use cases. Every sentence earns its place with no fluff or repetition.

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 presence of an output schema and safety annotations, the description covers the essential behavioral and selection context well: model choice, parameter controls, suitability, and use cases. It does not explicitly cover limitations like input character overflow behavior, but the schema already documents the ~10,000 character maximum, so this is a minor gap.

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 baseline is 3. The description adds meaningful semantics by explicitly explaining max_length as a 'token cap' and min_length as a 'token floor,' and by giving a concrete custom model example ('google/pegasus-xsum for extreme single-sentence summaries'). This goes beyond the schema's parameter descriptions and helps the agent make informed parameter choices.

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: 'Summarize a long text into a shorter, coherent paragraph using the facebook/bart-large-cnn model via HuggingFace Inference API.' It clearly distinguishes itself from sibling NLP tools like sentiment, NER, translation, and zero-shot classification, which all have different purposes. The title and description align perfectly.

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 gives concrete use cases: 'article digests, executive summaries, and reducing LLM context window usage.' It also notes the model is 'trained on CNN/DailyMail news articles; works well for factual prose,' which helps an agent infer when it is appropriate. It does not explicitly state when not to use it or name alternative tools, but the context is clear enough for selection.

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