summarize_dataset

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

The description discloses the exact behavior: reads profiles from index.json, generates summaries, and writes them back, plus the constraint 'No re-parsing of source files.' This goes beyond the readOnlyHint=false annotation by specifying the mutation target and mechanism. It stops short of stating whether existing summaries are overwritten in place, but 'writes them back' implies that.

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?

Three sentences, each dense with information: purpose, mechanism/requirement, and an alternative invocation path (index_local). No filler or repetition; the key action is front-loaded.

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 single-parameter tool with no output schema, the description provides ample context: what it does, how it works, prerequisites, side effects, and when it's redundant. It's complete enough for an agent to decide whether and how to invoke it.

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?

The schema already fully documents the dataset parameter as a 'Dataset identifier (from list_datasets)' with 100% coverage. The description adds a meaningful constraint: the dataset must already be indexed, which is not in the schema, adding nuance beyond the identifier type. This justifies a score above the baseline of 3.

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 opens with a specific verb and resource: 'Generate natural-language summaries for a dataset and all its columns.' It further clarifies the scope by stating it operates on already-indexed datasets and reads/writes index.json, distinguishing it from raw-file parsing tools and sibling describe tools.

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 clearly states the prerequisite that the dataset must already be indexed ('Works on already-indexed datasets') and notes that summaries are auto-generated during index_local, implying this tool is only needed when summaries were not created there. However, it does not explicitly name alternative tools like describe_dataset for structured metadata, leaving some room for ambiguity.

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