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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety and repeatability profile is covered without description help. The description adds the returned quantities but no extra behavioral context such as cost, caching, or latency for whole-dataset queries, so it stays at the baseline-plus level.

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?

Two sentences, front-loaded with the return payload and then the use cases; nothing is redundant or padded.

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 one optional parameter at full schema coverage, rich read-only annotations, and a separate output schema, nothing an agent needs to invoke this correctly is missing from the definition.

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?

The single parameter is fully documented in the schema ('Ticker to check; omit for the whole dataset.'), giving 100% coverage, so the baseline is 3. The description's phrase 'for the whole dataset or for one ticker' merely echoes that semantics rather than adding format or edge-case detail.

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 states a specific verb and resource: it returns the first and last historical event date plus the event count, and it names the two scopes (whole dataset or one ticker). That is far more specific than a name restatement, though it never names a sibling such as get_meta or get_ticker_info to sharpen 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?

It gives concrete triggering questions ('what period does the data cover?', 'how much history for X?'), which is clear positive guidance for when to call it. It stops short of exclusions or an explicit alternative ('use get_meta instead when...'), so it is not quite a 5.

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