Inkable Label

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the agent knows this is a safe, repeatable, closed-domain read. The description adds the topic scope of the returned content, which is useful, but says nothing further about behavior (e.g., that it returns static documentation with no per-order data). No contradiction with annotations.

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 with the subject front-loaded and topics enumerated in a compact parenthetical. The topic list is dense but each item earns its place by telling the agent what questions this tool answers; nothing is padding.

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?

An output schema exists, so return structure need not be described, and with no parameters the surface area is small. The description covers the informational scope well enough for an agent to decide whether to call it, with only the sibling routing left unstated.

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 the baseline is 4. The description correctly implies a no-argument, topic-browsing call and raises no parameter expectations an agent would have to reconcile.

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 a concrete resource (Inkable Label Co.'s ordering process) and enumerates the topics it covers: pricing flow, proofing, printing, turnaround, shipping, quote forms, and contact info. An agent can tell it is a static informational/FAQ tool. It does not explicitly distinguish itself from siblings inkable_price and inkable_options, which overlap on the pricing and product-option topics mentioned here.

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: read this when a question is about process, turnaround, shipping, or contact rather than a concrete price or option lookup. No sentence states when to prefer inkable_price or inkable_options instead, which matters because the description itself mentions 'website price' and product forms.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description adds that the result is a small closed set of allowed values and that omission returns the full set, but says nothing about return format beyond that (partially mitigated by the output schema).

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 compact sentence with the values and the omission rule front-loaded; nothing is padded. The 'Inkable Label Co.:' prefix is slightly wasted space in an otherwise efficient line.

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 one-optional-parameter, read-only lookup with an output schema, the description covers enough to invoke it correctly. The residual gap is the unlabeled 'product' parameter, which is documented nowhere else, so an agent has to bridge the naming mismatch itself.

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% — the single 'product' parameter has no description at all — so the description must compensate. It does supply the legal values and the 'omit for all' semantics, but it never ties them to the parameter name 'product', leaving an agent to infer that these are the values for that argument.

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 purpose: enumerating the allowed values (roll_labels, sheet_labels, tissue) for the inkable_price tool's product argument. It names the sibling it feeds, so an agent can tell it apart from get_bag_format/list_bag_formats. It stops short of a clean verb+resource phrasing ('Inkable Label Co.: allowed options for...'), but the intent is unambiguous.

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?

Ties the tool to inkable_price and gives one explicit usage rule — omit the value to get all options — which tells the agent both when to call it and how to call it broadly. It never states when not to call it or what alternatives (if any) exist for enumerating option sets.

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?

Goes well beyond the read-only/idempotent annotations by disclosing the downstream purchase flow (customer adds to cart, uploads files, pays on the website first, then gets a proof), the 3-10 day lead time after proof approval, and the fallback behavior for unsupported options. This is exactly the business context an agent needs to relay to a customer.

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 what the tool returns, followed by flow and lead-time context. Four sentences, none obviously wasted, though the fulfillment-flow sentence sits between two pricing sentences and slightly interrupts the core message.

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 an output schema present, the return shape needs no explanation, and annotations cover the safety profile. The description addresses the key gaps: fallback behavior for non-standard options and post-quote lead times. What it omits is guidance on discovering valid option values, which an agent must infer from the enum-laden schema.

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 description names the product families but not individual fields; the real parameter meaning lives in the nested PriceRequest descriptions (proof constraints, width 2-8 whole inches, height 0.1-8, quantity ranges, tissue vs. label fields), which are not visible from the described 0% top-level coverage. The description adds the out-of-range-to-quote rule, which is genuine value, but does not compensate for the top-level coverage gap on its own.

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 and resource: the website's own price for roll labels, sheet labels, or custom tissue, plus a product-page link. An agent can tell this apart from inkable_options (option discovery) and submit_quote_request (quoting) without opening the schema.

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?

Implies the correct usage context (online-priced configurations only) and gives an explicit routing rule for out-of-scope inputs: return quote_required with the quote page. It never names a sibling tool by name, so the alternative paths are implied rather than spelled out.

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?

Annotations already indicate a non-read-only, open-world, non-idempotent submission. The description adds substantial behavioral context beyond annotations: the customer must confirm by email, the agent must not click the confirmation link, the team reviews and replies by email, no reply time is promised, and a reference number is returned.

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 action is front-loaded, and the remaining sentences carry distinct operational facts: confirmation flow, review process, no reply time, price alternative, consent requirement, and the fact that this is not an order. Despite covering a complex process, it avoids redundant explanation.

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 complex, open-world quote submission tool with an output schema, the description covers the full user-visible process, consent prerequisite, sibling alternative, and outcome limits. It does not need to repeat return-value details already covered by the output schema.

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 schema description coverage signal is 0%, so the description should compensate for parameter ambiguity. It only adds meaning for customer_consent=true and loosely for product_type; it does not explain quantities, the request object structure, or the nested labels/tissue/bags detail objects. Most parameter semantics are left to the schema.

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 and resource: send a quote request to Inkable's team for custom labels, tissue paper, or pouches/bags. It also distinguishes the tool from inkable_price by explaining that online roll labels, sheet labels, and tissue paper use the website price instead.

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?

Explicitly names the key alternative, inkable_price, and the condition under which it should be used instead. It also states prerequisites such as customer_consent=true, explains the confirmation-link flow, and clarifies that the tool does not create an order.

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