Pouch.me

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered externally. The description adds only that the payload is a fixed question/answer catalogue, a useful hint that the content is static, but says nothing about filtering, locale, or size of the returned question set.

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 short clause with no filler; the resource and its consumer are front-loaded. The 'Pouch.me:' brand prefix consumes attention without adding selection value, which keeps it just below top marks.

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 the return shape needn't be restated. However, the key operational fact — that the returned questions and allowed answers are the valid inputs for recommend_bag_type — is only half-stated, leaving the agent to work out the handoff between the two tools.

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 nothing for the description to disambiguate; baseline 4 applies. The description correctly implies a parameterless static fetch.

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?

Names a specific resource (the questions and allowed answers) and ties it to the sibling tool recommend_bag_type, so the agent knows this is a lookup of the question set. It lacks an explicit verb ('returns'/'lists'), but the pairing with a named sibling makes the purpose unambiguous and distinguishable from the other siblings.

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 mention of recommend_bag_type implies this is a prerequisite lookup used before calling that tool, but the description never says so directly — no 'call this first' or 'use when you need valid input values'. The agent must infer the ordering and the alternative of just guessing parameter values.

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 readOnly, idempotent, and closed-world behavior, so safety is covered. The description adds that the payload contains options, materials, and minimum — useful content disclosure — but says nothing about error behavior for a bad id or how the record is scoped.

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?

One short sentence with the key information front-loaded; nothing is redundant. The 'Pouch.me:' branding prefix is the only element that doesn't earn its place.

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 one required parameter, an output schema, and rich annotations, the description is nearly sufficient. The gap is that the sole input's semantics are carried entirely by an undocumented string, and 'minimum' is left undefined (presumably minimum order quantity).

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%, so the single format_id parameter is undocumented in the schema. The description partially compensates by indicating the id comes from list_bag_formats, but gives no format, shape, or validation hints beyond that.

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: retrieve details for one format id, and lists the fields returned (options, materials, minimum). It implicitly contrasts with the sibling list_bag_formats by naming it as the source of ids, so an agent can tell the two apart.

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?

'from list_bag_formats' establishes the prerequisite: call the list tool first to obtain a valid format_id. There are no explicit when-not conditions or warnings about unknown ids, but the usage context is clear.

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 establish read-only, idempotent, closed-world behavior. Beyond that, the description discloses the actual content the agent will receive (free items, workflow steps, options, materials, customer requirements), which is the meaningful behavioral signal for an informational tool. It does not say whether the content is static or time-sensitive, which keeps it from a 5.

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?

One front-loaded sentence naming the service, then a compact enumeration of covered topics. The 'quote -> proof -> payment' shorthand is efficient but slightly dense, and the sentence is a fragment without a leading verb, which mildly reduces clarity.

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 description need not explain return shape, and with no parameters and read-only annotations, the burden is light. Listing the covered topic areas is enough for an agent to decide whether to call it; a note on currency of the content or overlap with sibling tools would make it complete.

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. The description correctly does not waste space on inputs.

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 the resource clearly: an overview of Pouch.me's offering, covering what is free, the quote->proof->payment flow, options, materials, and customer requirements. It lacks a verb ('returns'/'answers questions about'), but the scope is unmistakable and distinguishable from the pricing/options/quote siblings.

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 by the content list — an agent should call it for onboarding/overview questions about the service, pricing inclusions, and ordering process. There is no explicit when-to-use/when-not statement and no pointer to alternatives such as inkable_price or inkable_options when the user wants a specific price or option rather than an overview.

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, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds that the response is the full set of quoted formats and includes per-design MOQ, which is mild but real added context; it says nothing about ordering or whether the list is exhaustive/stable.

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?

One front-loaded sentence with no wasted clauses; the only slightly extraneous element is the 'Pouch.me:' brand prefix, which carries little selection value.

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?

A parameterless read-only list tool with a declared output schema and full annotation coverage needs little more — the description tells the agent what the list contains and what each row includes, leaving return shape to the 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?

Zero parameters, so the baseline is 4; the description correctly implies no filtering or input is needed, consistent with the empty 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+resource: listing the bag formats the business quotes, plus what each entry carries (minimum order quantity per design). That is enough to distinguish it from the singular sibling get_bag_format, though the description never names that sibling explicitly.

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 the word 'list' and the 'we quote' framing — an agent can infer this is the catalogue-browsing step before get_bag_format or submit_quote_request, but no when-to-use, prerequisites, or alternative is stated.

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 cover read-only and idempotent behavior, so the description adds genuinely new context: the result is a *starting* recommendation, the team confirms the final size on the written quote, and a sizing_reference thread carries the result into submit_quote_request. That caveat about authority is the key thing an agent could otherwise get wrong.

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 identity and provenance, then bundles the required inputs, the return shape, the authoritative caveat, and the downstream handoff in a compact block. It is dense but every clause carries information; the only mild cost is that inputs, outputs, and caveats are run together without visual separation.

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 the brief mention of returned inches/mm, fit quality, and explanation is sufficient rather than obligatory. Combined with annotations covering safety and the description covering the human-confirmation caveat and the sizing_reference handoff, an agent has what it needs to call this correctly and act on the result.

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 100% and the per-parameter descriptions already explain product, fill_amount, piece_size, format, and bag_size, including the 'ask the customer; don't guess' instruction. The description restates the same field list without adding syntax or edge-case detail, so it sits at the baseline rather than improving on 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 ('recommend a starting bag size') and pins its provenance to the same sizing engine used by pouch.me/bag-sizing and the quote form, which makes it distinct from the sibling recommend_bag_type (which picks a format, not a size). An agent can tell the two apart without opening either 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?

Gives clear invocation context: supply the product, the fill amount, the bag type (explicitly sourced from recommend_bag_type), and piece size for solid pieces. It also names the downstream path ('pass sizing_reference to submit_quote_request'). It stops short of explicit when-not-to-use guidance beyond noting that roll stock isn't sized.

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 readOnly, idempotent, and closed-world, so the safety profile is covered. The description still adds real value by disclosing that output is rule-based and non-authoritative, and that sizing/fit are out of scope and confirmed manually — the key behavioral caveat an agent needs.

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?

Three short sentences: what it is, what it is not, and how to call it. Nothing is padded and the caller-critical input format is stated directly.

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?

Output schema exists, so return values need no explanation, and annotations cover safety. Remaining minor gaps are input-failure behavior and a positive routing rule against the format-lookup siblings; otherwise an agent has what it needs to call this 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 0% and the single parameter is a loosely typed nested object, so the description carries the burden — and it does, specifying the {question_id: choice_id} mapping and pointing to the tool that supplies valid ids. It does not say what happens with unknown or missing ids, so not a 5.

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 (rule-based starting suggestion) and the exact resources it covers (bag format and material), plus an explicit negative scope (no sizing, no fit check). The reference to bag_recommendation_questions differentiates it from the plain lookup siblings, though it does not distinguish itself from get_bag_format or list_bag_formats.

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?

Makes the usage context clear: this is a pre-quote 'starting suggestion', with format/size/material finalized by the team on the written quote, and answers must come from bag_recommendation_questions. No explicit when-not rule versus get_bag_format or inkable_options, so it stops short of full routing guidance.

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?

The description adds substantial behavioral context beyond the annotations: a confirmation email is sent to the user first, the request only reaches the team after the user confirms, the team may reply with a quote or a spec question, and no reply time is given. It also states that the tool returns a reference number and does not create an order, which is consistent with the non-destructive, non-read-only 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?

The description is front-loaded with the core action and then adds only operationally useful details: the confirmation flow, the review process, the lack of a reply-time promise, and the consent prerequisite. Every sentence earns its place and the length is appropriate for the submission complexity.

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?

Given the output schema exists, the description does not need to explain return values in detail, but it helpfully notes that a reference number is returned. Combined with annotations covering safety and open-world behavior, the description gives an agent enough context to understand the submission flow, the consent requirement, and the fact that no order is created.

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?

With schema description coverage reported at 0%, the description should compensate by explaining the request object's important fields, but it only adds that customer_consent=true is required. It does not clarify the other required request fields or the nested QuoteRequest structure, leaving the agent dependent on the input 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?

The description uses a specific verb and resource: 'send a quote request to our team for your user.' It also explicitly states 'Does not create an order,' which prevents confusion with order creation and distinguishes it from the sibling sizing and information tools.

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 description gives clear context: the customer must have asked for the quote, must consent to being emailed, and must personally confirm the confirmation link. It also says the tool does not create an order, but it does not explicitly route agents to alternatives such as recommend_bag_size before submitting.

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