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

Goes well beyond the annotations: it discloses the key-based match/insert-vs-merge rule, that only supplied columns are overwritten, that views update immediately without edits, and that writes are confined to this workspace with no external calls. These details materially inform a destructive, idempotent mutation that annotations alone could not convey.

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 behavior, then layers matching, idempotency, typing rules, and scope in a tight sequence. Every sentence carries actionable information with no filler.

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 3-param mutation tool with no output schema, the description covers matching, typing, null conventions, and side effects well. It does not indicate what the call returns (e.g. inserted/updated counts) or any size/rate caveats around the 5000-row cap, leaving 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 coverage is already 100%, but the description adds real semantics the schema lacks: how rows are matched on key columns, the required key column inclusion, and strict value typing rules (JSON numbers not "$1,200", ISO dates, null instead of "" or 0). This is substantive value beyond the schema text.

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+resource ("Adds or updates rows in a dataset") and immediately clarifies intent with the aliases "add new data" / "correct a number". This clearly separates it from delete_rows, query_dataset, and create_dataset in the sibling list.

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?

Explains when to reach for it (adding data, correcting values) and that matched keys merge rather than duplicate, which guides invocation. It never names the sibling alternative (e.g. delete_rows for removal) or states when not to use it, so it stops short of full routing guidance.

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