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.

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

With no annotations, the description carries the full load and does so well: it discloses the default window behavior and enumerates point varieties (reported with full provenance, computed with named inputs, gap with state and no value, XBRL with later-filing revisions and a `restated` flag). It omits error behavior for unknown ticker/series_id and any pagination or rate-limit characteristics.

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 core action leads, followed by return-shape detail; every sentence describes an actual output variant rather than padding. It is dense but each clause earns its place, particularly since no output schema exists.

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 no output schema and no annotations, the description usefully compensates by describing return structures in detail. It is still incomplete regarding how to obtain ticker/series_id/tier values and what happens on invalid input.

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 0%, so the description must compensate, and it only partially does: it explains `start`/`end` as fiscal labels (e.g., FY2024Q1) defaulting to the whole window, which is genuine value beyond the schema. However, `ticker`, `series_id`, and especially `tier` are left entirely unexplained, so most parameters remain opaque.

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+resource: it returns one series' quarterly points over a window. It is clear what the tool fetches, but it never distinguishes itself from siblings like list_metrics (which presumably yields series_ids) or check_condition.

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?

There is no explicit when-to-use guidance and no mention of alternatives among list_companies, list_metrics, and check_condition. The agent must infer that this is the tool for reading an already-known series_id over the default or specified window.

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

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

No annotations exist, so the description carries the full behavioral burden. It does disclose the exact fields returned (ticker, name, tier, pub date, fiscal quarter range, series count), which is genuinely useful with no output schema. However, it says nothing about read-only nature, result volume/ordering, or pagination, so it only partially discharges that burden.

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?

A single sentence that front-loads the resource and then lists the returned fields with no filler. The trailing colon-list is slightly clunky but every clause carries information, so nothing is wasted.

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 zero parameters, no annotations, and no output schema, the only thing an agent needs is to know what it gets back, and the description supplies that field inventory. Minor omissions (ordering, total count) do not block correct invocation.

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 tool takes zero parameters, so there is no parameter semantics to explain; baseline 4 applies. Schema description coverage is 100% and the input schema is an empty object, which is consistent with the description's no-argument framing.

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 names the resource ('companies in the dataset') and enumerates the attributes returned, which distinguishes it from list_metrics and get_series. It stops short of an explicit verb ('list/return all companies') or any sibling comparison, so it is clear but not fully differentiated.

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?

Usage is only implied: by describing itself as the inventory of companies in the dataset, it suggests it is the discovery entry point. There is no explicit when-to-use, no condition favoring it over list_metrics, and no stated prerequisites or exclusions.

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

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

With no annotations, the description must carry the full behavioral burden. It does disclose the shape of the result (id, label, unit, kind, lens, latest value) and the case-insensitive substring matching rule, plus the tier values, but says nothing about result volume, pagination, unknown-ticker behavior, or that the call is a non-mutating read.

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?

Compact and front-loaded: the return payload is stated first, then the two optional filters in order of appearance in the schema. The opening fragment 'The series a ticker has' is slightly clipped and the field list mixes names without explanation, but no sentence is wasted.

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?

There is no output schema, no annotations, and no parameter descriptions in the schema, so the description is the only source of information. It adequately covers return fields and the two filters but leaves ambiguous fields like 'lens' and 'kind' undefined and says nothing about limits, ordering, or failure modes.

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 0% and `tier` has no enum, so the description does real work: it defines `search` as case-insensitive substring matching on ids or labels and constrains `tier` to 'curated' or 'xbrl' with the condition under which it matters. Only `ticker` is left unexplained, though it is self-evident.

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 names the resource precisely ('The series a ticker has') and enumerates the returned fields, so an agent understands this lists the metric/series inventory for a ticker. The verb 'list' is only implicit and there is no differentiation from siblings such as get_series, which retrieves a single series.

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?

Usage is implied through the parameter explanations: use `search` to narrow by id/label substring and `tier` to disambiguate when a ticker exists in both curated and xbrl. However, there is no statement of when to call this versus get_series or list_companies, and 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.