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

With only sparse annotations (readOnlyHint false, idempotentHint false, destructiveHint false), the description carries most of the behavioral burden. It usefully discloses that the tool creates an isolated Postgres agent job and that results are strictly grounded with exact evidence text and character slice offsets. It does not explain how to monitor or retrieve the job, but the added behavioral context is meaningful.

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: a one-line purpose statement followed by three short clauses covering inputs, job creation, and grounding guarantees. Every sentence earns its place, with no redundant restatement of schema fields.

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 an asynchronous dataset-building tool with an output schema present, the description covers the core inputs, isolation, and grounding guarantee. However, it omits limit semantics, the flexible columns input format, and an explicit pointer to agent_job_status for checking the created job, which an agent would need to use it end-to-end.

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 maps query to 'task/topic query' and columns to 'requested column names', which adds some meaning. However, it says nothing about the limit parameter and does not explain that columns can be a string, array, or null, leaving partial parameter gaps.

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 concrete verb ('Build') and a specific deliverable ('grounded structured dataset grid from web extraction'), then clarifies inputs and job creation. It is clearly distinguishable from generic search or recall tools, though it does not explicitly name or contrast a sibling tool, so it falls short of a 5.

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 communicates a clear use context: turning a task/topic query and requested columns into a web-extracted, grounded dataset. It does not explicitly state when not to use it or name alternatives like research, search_web, or extract_url, but the context is strong enough that an agent can infer the intended scenario.

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