summarise_hk_data

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

With no annotations, the description carries the behavioral burden and does disclose important output behavior: a text table, data rows, a localhost chart URL, and no features in the chat. However, it does not discuss limits such as the top parameter, permissions, or whether the operation is purely read-only.

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 front-loaded with purpose and structured with focused bullets for each grouping/aggregation case. It is efficient overall, though the final return-value sentence could be tighter and partly duplicates what an output schema may cover.

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 description gives enough to perform common aggregations and notes return behavior, and an output schema exists. Still, with five parameters at 0% schema coverage and no annotations, the unexplained top parameter and lack of explicit sibling routing leave meaningful gaps.

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 0%, so the description must compensate. It explains group_by, agg, and value_field with examples, but it leaves top and dataset_id semantics unaddressed, so coverage is only partial.

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 states that the tool answers a question about a dataset in text and with a bar chart, and it explains the summarization behavior without returning raw features. It is distinguishable from map/query siblings by its aggregate text-and-chart output, though it does not explicitly name those alternatives.

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?

It gives clear usage scenarios: empty group_by for an overview, group_by with agg=count for feature counts per group, and sum/avg with value_field for totals or averages. It does not state when not to use this tool or explicitly route to siblings such as query_hk_data.

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