Store Starter Tools for Shopify

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

With no annotations, the description carries the full burden. It usefully discloses that unsupplied fees use an EXAMPLE default that must be verified—a key behavioral trait. However, it does not state that the tool is a pure calculation with no side effects, nor does it describe the output format or assumptions beyond the default warning.

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?

Two sentences with zero waste: the first states the core calculation, the second delivers a critical warning about defaults. Front-loaded and appropriately sized.

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 8 parameters, 2 required, no output schema, and 0% schema description coverage, the description is insufficient. It lists the outputs but does not explain parameter meanings, constraints, or the shape/format of results. An agent would need to guess parameter semantics.

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 define the 8 parameters. It only vaguely mentions 'fee value' without mapping to specific parameters like paymentFeeFixed or paymentFeePercent, and completely omits others such as monthlyFixedCosts and adCostPerSale. The description adds minimal meaning beyond parameter names.

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 clearly states the tool calculates specific profit metrics (profit per sale, margin, break-even price, maximum ad cost) for a product. It uses a precise verb+resource and is easily distinguished from the unrelated sibling tools (find_niches, generate_store_names, etc.).

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 implies the tool is used for profitability analysis of a product, but provides no explicit guidance on when to use it versus alternatives or any prerequisites. It lacks exclusions or context about scenarios where the tool is appropriate.

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 a meaningful trait: results come from a curated list and are qualitative, not market data — critical for an agent not to present them as research. It omits other traits such as determinism, ordering, or whether results are ranked, but the core expectation-setting is present.

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?

Two tight sentences with the core purpose front-loaded and the caveat immediately after. No filler or restatement of the tool name.

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 3-parameter, read-only ideation tool with no output schema, the description conveys source, matching inputs, and output nature adequately. The unexplained `limit` parameter and lack of any return-shape hint (list size, ordering) are the only gaps.

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 a moderate 67%; the schema itself documents the budget enum thresholds and the interests list. The description only echoes that interests and budget are used for matching and adds nothing about the undocumented `limit` parameter (1-20).

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 ("Suggest online store niches") plus the source constraint ("hand-curated list"). The clause "Qualitative reasoning only, not market data" implicitly separates it from the quantitative sibling calculate_profit, though no sibling is named outright.

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 "matched to the user's interests and starting budget," telling the agent this is the ideation step. However, it never states when to prefer it over generate_store_names or calculate_profit, nor any 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 zero annotations the description carries the full burden and it does disclose the most consequential trait: results are not validated for domain or trademark availability, and the agent is told to communicate that. It stops short of stating the tool is side-effect-free/read-only and that output is deterministic given the same seed, leaving some behavioral inference to the 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?

Two short sentences, capability first, caveat second — zero filler and nothing structurally misplaced.

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?

Four parameters, no annotations, and no output schema, so the description bears more weight than it carries. It never indicates the shape of the result (list size, default count, whether names are single words or multi-word), nor that the same seed yields the same set, which matters for a 4-param generation tool.

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 75%, so the schema already explains keywords, style, and seed. The description only reinforces the keyword count (1-5) and mentions style; it says nothing about the count parameter, so it adds little beyond the schema baseline of 3.

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 ("Generate brandable online store name ideas") plus the input shape (1-5 keywords and a style), so an agent knows exactly what comes back conceptually. No sibling tool (calculate_profit, find_niches, get_launch_checklist) overlaps with name generation, so differentiation 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?

There is a downstream instruction ("tell the user to check" domain/trademark status), which is useful but is about handling results, not about when to select this tool. No explicit when-to-use or when-not guidance is given, though the sibling set makes the choice obvious by elimination.

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 behavioral burden. It discloses the content shape (six named phases) and implies a safe, parameterless read, but never states that the output is static/generic rather than personalized to a specific store, nor anything about size or format.

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 verb and resource, with the phase list doing real work rather than padding. Nothing extraneous.

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 zero-parameter, non-destructive read with no output schema, the description is nearly sufficient: it says what the checklist covers, which substitutes for return-value documentation. A note on whether the content is static or tailored would close the last gap.

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 parenthetical phase list incidentally signals that no input is needed to scope the checklist.

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 ('Return') plus a precise resource ('step-by-step checklist for launching an online store'), with the six phases enumerated in parentheses so the agent knows exactly what content to expect. It is clearly distinct from the sibling tools (calculate_profit, find_niches, generate_store_names, get_shopify_signup_link) by content, though it never explicitly contrasts itself with them.

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: an agent can infer this is the tool to call when a user needs a launch roadmap, but the description gives no explicit when-to-use trigger, no sequencing relative to siblings (e.g. call after find_niches), and no prerequisites.

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 are provided, so the description carries the full burden. It discloses that the result is a link, which implies a harmless read with no side effects, but it says nothing about whether the link is static, personalized, referral/affiliate-tagged, or whether it expires – meaningful gaps for a zero-annotation tool.

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, front-loaded sentence with no filler. It is appropriately sized, though the phrasing 'the link the user can use' is slightly indirect.

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 simple zero-parameter read tool with no output schema, the description covers the essentials. However, it never clarifies the nature of the link (affiliate/referral, freshness, any signup flow prerequisites), which is the main thing an agent would want to know before presenting it to a user.

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 are no parameter semantics to document; the baseline for a parameterless tool applies. Nothing in the description misleads about 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?

States a specific verb ('Get') and a specific resource ('the link ... to start a Shopify store'), which an agent can easily tell apart from the sibling tools (calculate_profit, find_niches, generate_store_names). It does not explicitly name or contrast the siblings, so it stops short of a 5.

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 'the user can use to start a Shopify store' implies the triggering situation (a user wants to sign up for Shopify), but there is no explicit when-to-use, when-not-to-use, or named alternative among the siblings.

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