get_dataset_history

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

The readOnlyHint annotation already covers safety. The description adds meaningful context by explaining that snapshots are appended on every successful index_local and that n is capped at 50, which helps the agent understand the data freshness and limits. It does not contradict the annotation.

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 two sentences long, front-loaded with the primary action, and every clause adds value. It is efficiently structured with no fluff.

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 tool is a simple read operation with two well-documented parameters and no output schema. The description covers purpose, usage, and behavioral context, which is sufficient given the low complexity. Minor edge cases like empty datasets or missing dataset IDs are not addressed, but the description is still complete for typical use.

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 100%, so the schema already fully documents both parameters. The description's mention of 'n capped at 50' duplicates schema info without adding new meaning, so the baseline of 3 is appropriate.

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 uses a specific verb 'Return' and a specific resource 'the last N profile snapshots for a dataset', clearly stating what the tool does. It also distinguishes the tool by noting snapshots are appended on every successful index_local, which differentiates it from similar tools like get_schema_drift.

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 explicitly says 'use this to detect schema/content drift over multiple ingests of the same dataset', providing a concrete use case. However, it does not mention when not to use it or name alternative tools, so it falls short of a full 5.

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