Shopify Change Intelligence

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

With no annotations, the description carries the full behavioral burden. It discloses critical context: paid cost ($0.03 USDC per store on Base via x402), a max of 50 stores, and the shape of results (per-store change counts for new/removed/price/stock). It does not cover error handling, rate limits, or whether the operation is read-only, but the payment and output details are substantial.

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 two sentences and front-loads the most critical operational detail (payment requirement and max) before stating the tool's purpose and use cases. The list of use cases at the end is slightly verbose but each phrase adds a distinct scenario, and there is little wasted wording.

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 batch tool with one fully described parameter, no output schema, and no annotations, the description covers cost, limits, output format, and primary use cases. It does not explain edge cases like exceeding the max store count, but otherwise provides enough context for an agent to invoke it 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 100% for the single 'stores' parameter, and the schema itself provides a clear description and example. The tool description adds no parameter-specific details beyond what the schema already states, so the baseline score of 3 is appropriate.

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 (monitor) and resource (competitor Shopify stores) and emphasizes batch scope ('whole set ... in one call'), which implicitly distinguishes it from single-store siblings. However, it does not explicitly name or contrast with any sibling tool, so differentiation is inferred rather than stated.

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 provides clear use cases: tracking many rivals, category-wide price monitoring, brand/agency portfolio surveillance, market-scanning at scale. It does not include any when-not-to-use guidance or explicit alternatives, but the context for when to choose this tool over a single-store one is evident.

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 burden. It does disclose real behavioral context: the tool is paid ($0.05 USDC on Base via x402), which is essential before calling it, and that output is a diff against the last snapshot. However it omits auth requirements, whether payment is per-call, and what happens if no prior snapshot exists.

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 dense sentence with no filler, and the cost banner is front-loaded so an agent sees the payment requirement first. Slightly compressed, but every clause earns 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?

For a one-parameter, no-output-schema tool the description is nearly sufficient: it covers cost, output shape, and the snapshot dependency. The main gap is the absence of any prerequisite or error behavior when no snapshot exists.

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?

One parameter with 100% schema description coverage, so the schema already documents 'store' with a domain example. The description adds no additional meaning about the parameter, which matches the baseline of 3 when the schema does the work.

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: returns change intelligence versus the last snapshot, and enumerates the diff types (new/removed, price changes, stock changes). This clearly separates it from shopify_snapshot, though it never names a 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 implied by 'vs the last snapshot' (a prior snapshot must exist) but never stated as a condition, and there is no guidance on when to pick this over shopify_intel_report or shopify_landscape. The paid-cost note is disclosed but is not usage 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?

With no annotations, the description carries the burden and does well on the critical trait: it discloses the tool is PAID ($0.50 USDC on Base via x402), which is exactly the kind of gating/cost signal an agent needs before invoking. It also sketches the output scope ('full-catalog'). It stops short of failure/refund behavior or rate limits, but the payment disclosure is the important one.

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, front-loaded with the cost constraint before the value proposition. Every clause earns its place by either pricing the call or enumerating deliverables; no filler.

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-parameter tool with no annotations and no output schema, the description compensates well: it lists what the report contains, which substitutes for a return-value spec, and it flags the payment requirement. An agent has enough to decide and invoke 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 100% and there is a single parameter whose schema description already gives the format ('Shopify domain, e.g. allbirds.com'). The description adds nothing about the store input, so the schema does the heavy lifting; baseline 3 is appropriate.

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: generating a full-catalog competitor intelligence report, and enumerates its contents (price bands, median/range, discounts/hikes, stock signals, takeaways). It hints at being the premium option versus siblings ('highest-value tool') but never names which sibling to use instead, so differentiation is implied rather than explicit.

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?

Implied usage only: 'you can put straight into a briefing' signals a comprehensive report use case, and calling it the 'highest-value tool' nudges toward selection. There is no explicit when-to-use vs. when-to-choose-a-sibling (shopify_snapshot, shopify_landscape) guidance or any 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 burden and does disclose two high-value behavioral facts an agent needs before invoking: it is paid ($5 USDC on Base via x402) and it accepts up to 10 stores. It does not state read-only nature, latency, or rate/failure behavior, but for an analysis tool the cost-and-payment disclosure is the decisive detail.

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 zero filler, and the paid/cost constraint is front-loaded before the analytical scope. Slightly compressed but nothing that should be cut; it could be one clause tighter around the metrics list.

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?

No output schema and no annotations, yet the description approximates the return content (median-price positioning, premium/value flags, price-war signals, stock anomalies), which is what an agent needs to judge the output. The main remaining gap is behavior when 'anchor' is absent and whether the tool is read-only.

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%, so the schema already explains 'anchor' and 'stores' (including the up-to-10 limit). The description restates the 10-store cap and the anchor-vs-peers relationship but adds no syntax, format, or default behavior for the optional 'anchor' parameter when omitted.

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 analysis verb and resource: it positions an anchor store against competitor stores by median price and flags premium/value players, price wars and stock anomalies. That is concrete and distinguishable from generic 'report' or 'snapshot' siblings, though no sibling is named to sharpen the boundary.

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 word 'strategic competitive landscape' implies the use case (multi-competitor positioning) but there is no explicit when-to-use, when-not-to-use, or comparison against shopify_intel_report, shopify_snapshot, or shopify_changes. The up-to-10-store cap hints at scope but is not framed as a selection rule.

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 burden, and it does disclose useful traits: reads only the public /products.json feed (implying no auth and failure on private/password-protected stores), returns live data, and returns a sample rather than the full catalog. It omits rate limits, error behavior, and sample size, which for a scrape-style tool are meaningful gaps.

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, front-loaded with the cost signal and immediately followed by what is returned and where it comes from. No filler and no repetition of structured fields.

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-parameter read tool with no output schema, the description adequately covers what comes back (count, price range, availability, sample) and the data source. Only the sampling/pagination limits and failure modes on non-public stores are left unspecified.

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 100% for the single 'store' parameter (domain example given), so the schema already does the work. The description only adds that the store must be public, which is slightly more than the schema states but not substantial.

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 ('Returns a live snapshot of a public Shopify store') and enumerates the payload (product count, price range, availability, sample of products). It does not explicitly differentiate itself from siblings like shopify_changes or shopify_landscape, though the word 'snapshot' implicitly reads as current-state.

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 or when-not-to-use guidance, and no sibling is named as an alternative. 'FREE' lightly signals a cost consideration relative to paid siblings, but the agent must infer the snapshot-vs-changes distinction on its own.

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