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

Goes well beyond the annotations by disclosing that nothing is rebuilt/re-served/republished, id/versions/schedule are preserved, the old name is released for reuse, and recipe revisions must reference the new name. It also documents two error conditions (name_taken, archived table) that annotations do not cover, and is consistent with idempotentHint=true and destructiveHint=false.

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?

Front-loads the core action and then layers side effects, constraints, errors, and the return shape. Dense and mostly waste-free, though the run-on sentence listing side effects is long enough to be slightly harder to parse.

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?

With no output schema, the description supplies the return shape ({table_id, dataset_id, name, previous_name, changed, dashboard_url}), error cases, and mutation side effects. An agent has everything needed to call it and interpret the result correctly.

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?

With 0% schema description coverage, the description compensates by spelling out the name constraint (lowercase letters/digits/underscores, leading letter, max 63 chars, unique within dataset) and giving a JSON example. table_id is only implied as the target uuid, leaving a small gap.

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?

States a specific verb and resource: "Changes one table's name within its dataset." An agent can immediately distinguish this from siblings like archive_table, promote_table, and update_dataset. The scope (single table, within its dataset) is explicit.

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?

Provides an explicit adoption condition: "Only call it when the person you are working for has asked for the new name," which prevents unsolicited renames. It does not name specific alternative tools (e.g., update_dataset) for adjacent needs, so it stops short of full when/when-not/alternatives guidance.

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