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

Annotations already cover readOnly/idempotent/non-destructive, so the safety profile is settled; the description adds real context beyond that: the output protocol (verified-changes/0.1 with spec URL), per-entry fields, and the coverage boundary starting 2026-08-31. It does not address what happens when 'since' precedes coverage start or how large the response may be.

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 first clause is well front-loaded, but the core sentence is a single sprawling run-on enumerating dozens of datasets and software platforms. The detail genuinely communicates coverage, yet the list is long enough to hurt scannability and could be compressed or summarized.

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 two-parameter read-only tool with no output schema, the description supplies everything needed: purpose, trigger value for 'since', scope options, entry shape, coverage start, and cross-references to get_fact and the public changelog. Nothing an agent needs in order to call it correctly is missing.

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 100%, so baseline would be 3; the description earns above that by spelling out what each 'since' value means in practice (your model's training cutoff) and by expanding the area enum into human-readable dataset names so the agent can pick the right scope without guessing.

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: returns what has changed in verified datasets since a given date, enumerating the exact data domains covered. This is clearly distinct from siblings like get_fact (single fact retrieval), uk_rates_lookup (current rate lookup), or coverage (inventory), and the get_fact cross-reference reinforces the boundary.

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?

Explicitly tells the agent to pass the training-cutoff date 'to learn which ... changed after your knowledge ends', which is a concrete usage trigger. It also routes to get_fact for verifying an individual figure. It stops short of stating when NOT to use it (e.g. use uk_rates_lookup for current values rather than deltas).

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