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

With annotations only covering readOnlyHint/openWorldHint, the description carries real behavioral load: at most 60 points returned, weekly interval default, summary-first payload shape, snapshot start date, and the critical null-vs-flat semantics for frozen price feeds. This is exactly the kind of disclosure annotations cannot provide.

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 resource and the summary-first return contract before the long metric enumeration, and every clause does work. The single dense paragraph with stacked parentheticals is slightly hard to scan but not wasteful.

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 tool with no output schema and sparse schema descriptions, the description supplies the return structure (summary stats then points), point-count and interval defaults, and null handling — enough for an agent to call it and interpret results 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?

Schema coverage is only 25%, so the description must compensate, and it does partially: it explains the metric families, provenance of daily_*/price_llama, and that interval defaults to weekly. It leaves range values (30d/90d/1y/all) and the maxItems=6 cap on metrics unexplained.

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 ('one token's history as chartable series') and names the data source (TCC daily snapshots), which cleanly separates it from point-in-time siblings like tc_get_token. It never names a sibling explicitly, so the differentiation is inferable rather than stated.

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?

Gives clear usage direction: 'answer trend questions from it' via the summary block, and notes the metric provenance split so the agent knows which series are deep vs snapshot-limited. It stops short of stating when NOT to use it (e.g., for a current single value, use tc_get_token).

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