check_condition

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

With no annotations, the description carries the burden and does disclose meaningful edge-case behavior: a conditionless call returns values with no status, an unevaluable quarter is returned as 'not established' with a reason, and tier selects curated vs xbrl when a ticker exists in both. It does not explicitly state that the operation is read-only or describe any auth/rate-limit constraints, so it falls short of full disclosure.

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 critical constraint (evaluate quarters and test against condition) is front-loaded, and the dense middle paragraph efficiently documents the measure vocabulary. The semicolon-heavy prose is information-dense rather than padded, though it borders on hard to scan.

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 6-parameter tool with a nested object, no annotations and no output schema, the description covers the hard parts: measure vocabulary, condition syntax, output status semantics and unevaluable quarters. Missing only the date/start-end and ticker input formats, which the schema also leaves blank, so an agent can call it correctly in most cases.

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 0% and measure is a free-form nested object, so the description must compensate — and it does, enumerating measure names with arguments, defaults and units, and defining condition as a comparator plus a number in the measure's unit (e.g. ">= 28"). It leaves the formats of ticker, start and end unspecified, which is the remaining gap.

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 gives a specific verb and object: evaluate a measure at every quarter from start to end and test it against a condition. It distinguishes itself from list_metrics by pointing there for series ids, but it does not clearly distinguish itself from get_series, which the 'without a condition the values are returned' clause makes it sound like.

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 tells the agent where series ids come from (list_metrics) and what happens with and without a condition, which implies the use case. However, it never states when to use this tool rather than get_series or list_metrics, and gives no prerequisites or exclusions.

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