VibeCart

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

Beyond annotations (non-readonly, non-destructive), the description reveals server-side price resolution and the fact that charging only occurs upon customer completion of Stripe Checkout. This adds valuable behavioral context without contradiction.

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 cover purpose, usage guidance, and behavioral nuance without any wasted words. Front-loads the core action and legacy/modern distinction.

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 three parameters with oneOf and a clear output schema, the description adequately covers the essential behavioral and usage aspects. Minor omission: no mention of required authorization or catalog setup, but siblings imply the context.

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% so baseline is 3. The description adds value by explaining the legacy vs. new parameter choice and confirming server-side price handling, which goes beyond the schema's field descriptions.

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 explicitly states the tool creates a hosted checkout URL for merchant-catalog products, distinguishing it clearly from sibling tools that handle products or integration instructions.

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 advises new integrations to use 'items[]' over legacy 'productId' input, and clarifies that the tool contacts Stripe but does not charge the customer. Lacks explicit when-not-to-use scenarios, but guidance is solid.

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, destructiveHint=false, aligning with a safe, data‑retrieval operation. The description adds concrete context about the content of the returned guidance (trusted catalog pricing, multi‑item checkout, shared MCP endpoint), which is behaviorally informative beyond 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, front‑loaded sentence (23 words) efficiently conveys the tool's purpose and key deliverables. Every word adds value; there is no filler or redundancy.

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 zero parameters, a comprehensive set of annotations, and an output schema present, the description is nearly complete. It states the type of guidance and its scope (Next.js App Router, VibeCart installation). It could mention prerequisites or error conditions, but the combination of annotations and output schema mitigates this 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 has zero parameters, and schema coverage is 100%. With no parameters to explain, baseline 4 is appropriate. The description does not need to add parameter semantics since none exist.

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 returns 'concise Next.js App Router guidance for installing VibeCart' with specific features (trusted catalog pricing, multi-item checkout, shared MCP endpoint). This distinguishes it from sibling tools like create_checkout, get_product, and list_products, which handle commerce operations rather than integration setup.

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 usage for obtaining installation guidance but provides no explicit when-to-use or when-not-to-use advice. No alternatives are mentioned, and the context (sibling tools) suggests differentiation exists but is not articulated in the description itself.

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 and destructiveHint=false, so the tool is clearly a safe read. The description adds valuable context: that it accesses a 'trusted USD-cent price' from a 'configured merchant catalog' and explicitly rules out Stripe/checkout operations. 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?

The description is extremely concise: two sentences (20 words) that front-load the purpose and add a clarifying exclusion. No wasted words; every sentence 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 simple read-only tool with one parameter and an existing output schema, the description covers all needed aspects: what it does, what it doesn't do, and where to get the parameter value. Additional details (like output structure) are unnecessary given the output schema. Complete enough for an agent.

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% for the sole parameter 'productId', and its schema description clearly states it is 'A stable ID returned by vibecart.list_products.' The tool description adds no further parameter details, but the schema already provides adequate guidance. 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?

The description explicitly states 'looks up one product and its trusted USD-cent price' from the catalog, which is a specific verb and resource. It clearly distinguishes from sibling tools like 'list_products' (returns multiple) and 'create_checkout' (creates a checkout) by stating what it does and doesn't do.

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 tells when to use this tool (to look up a single product) and explicitly states what it does NOT do ('Does not access Stripe or create a checkout'), which helps avoid confusion with 'create_checkout'. However, it does not explicitly mention alternative tools or when not to use it, though that is implicit from sibling names.

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 `destructiveHint=false`, covering the safety and idempotency profile. The description adds that products come from 'the configured trusted merchant catalog', which is a minor detail about the source. No additional behavioral traits (e.g., pagination, rate limits, ordering) are mentioned. With strong annotation coverage, the description's extra value is limited, resulting in a baseline score.

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 consists of exactly two sentences. The first sentence states the core action and scope, and the second provides actionable usage guidance. Every sentence earns its place; there is no fluff. The structure is front-loaded and highly efficient.

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 tool's simplicity (0 parameters, rich annotations, presence of an output schema), the description is complete. It explains what the tool does and how the returned data should be used. The output schema handles return value details. No gaps remain for a list operation with no parameters.

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 has zero parameters, and schema coverage is trivially 100%. The description does not need to add parameter semantics, and it doesn't. Per the guidelines, with 0 parameters the baseline is 4. The description simply omits any parameter discussion, which 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 uses the specific verb 'Lists' and identifies the resource as 'products from the configured trusted merchant catalog'. It also mentions using the returned IDs for 'product lookup or checkout', which distinguishes it from sibling tools like `vibecart.get_product` (single product) and `vibecart.create_checkout` (checkout action). The purpose is clear and 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?

The description provides explicit guidance on how the output should be used ('Use the returned stable IDs for product lookup or checkout'), which implies when to use this tool (to obtain product IDs before further operations). However, it does not explicitly state when not to use it or directly compare with alternatives like `vibecart.get_product`. The context is clear but lacks explicit exclusions.

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