Deadline Desk

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

No annotations are provided, so the description carries the full burden. The word 'public' hints that receipts are non-sensitive/shareable, but the description says nothing about whether authentication is required, whether receipts expire, or what happens on an invalid or missing receipt_id — all material for a fetch tool with zero annotation coverage.

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 tight sentence with the verb and object front-loaded and no filler. It is well-sized for the tool's complexity, though it is arguably under-specified rather than optimally lean.

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 annotations and no output schema, the description should explain what a receipt contains and what happens on lookup failure. Neither is covered, leaving the agent unable to predict return values or error behavior beyond the vague notion of a 'public TRACE receipt'.

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 receipt_id has 0% schema description coverage, so the schema offers no explanation. The description partially compensates by indicating the ID comes from a prior policy lookup, giving provenance, but it says nothing about format, length, or validity of the identifier.

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?

Specific verb ('Fetch') plus a specific resource ('public TRACE receipt') scoped to a prior Deadline Desk policy lookup, which implicitly distinguishes it from the lookup_policy sibling. An agent can tell this is a retrieval of an artifact produced by an earlier lookup, though the 'TRACE' terminology is left unexplained.

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?

The phrase 'from a prior Deadline Desk policy lookup' implies this tool is used after lookup_policy, but it never states that precondition explicitly or names the alternative. There is no guidance on what to do if no prior lookup exists or if the receipt is unknown.

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 burden and does disclose real behavior: responses include quoted source text, a TRACE receipt, provenance badges (live|curated|archive), and an ordering rule where exceptions surface before headline January dates. It stops short of stating error behavior for unknown stores or whether fetching is live or cached.

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-loaded with the purpose in the first sentence, and the seasonal triggers follow in compact, information-dense clauses. Some internal jargon ('price_adjustment_detail honesty', 'TRACE receipt') is cryptic and costs a bit of clarity per word.

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 four-parameter, no-annotation lookup tool with no output schema, the description covers scope (US retailers only), the returned artifacts (quoted source text, receipt, provenance badges), and the ordering of exceptions — enough to call it correctly without guessing.

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 75%, so the schema already explains store, as_of, and anchor_date, and the description adds nothing param-specific. The mentions of 'holiday_extended_return.policy_year' and 'price_adjustment_detail' are output field names, not parameter semantics, so baseline 3 applies.

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 (look up) and a tightly scoped resource: a US retailer's published return, price_adjustment, warranty, or cancellation policy, plus the shape of the answer (quoted source text, TRACE receipt). It never names the sibling get_receipt, and the phrase 'TRACE receipt' could even blur the line with it, so sibling differentiation is left implicit.

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 concrete calling triggers — before Black Friday / Cyber Week purchase decisions, and during the Dec 26–Jan 31 return window for deadline urgency. That is clear when-to-use context, but there is no when-not guidance and no explicit alternative tool to consult instead.

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