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

With no annotations, the description carries the burden of behavioral disclosure. It reveals return field types and the precedence rule, but does not mention authentication, error handling, or side effects. As a fetch operation it implies read-only, but this is not explicit.

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 concise paragraphs with a clear front-loaded purpose and a focused argument list. Every sentence adds value; no fluff or repetition of schema details.

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?

The description covers purpose, params, and precedence, and the presence of an output schema covers return details. It does not specify behavior when neither parameter is provided, which is a minor gap for a tool with zero required 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 schema has 0% description coverage, so the description fully compensates. It explains that product_id is a numeric id from search_products and takes precedence, and that url is a full tokopedia.com URL used as a fallback. This adds meaningful usage guidance beyond the raw 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 clearly states the tool's action ('Fetch full details for one Tokopedia product') and lists the specific data fields returned (price, description, variants, stock, media, shop info). This distinguishes it from sibling tools like get_product_reviews and search_products, which serve different purposes.

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 clear context for when to use the tool, indicating that product_id comes 'from search_products' and that it takes precedence over url. It implies usage after searching for products, though it does not explicitly mention alternatives like get_product_reviews or specify when NOT to use the tool.

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 provided, the description carries the full burden of behavioral disclosure. It transparently explains the return format (a dict with 'reviews' list and 'count') and the effect of max_count (maximum number of reviews, range 1-100, default 20). This adds meaningful context beyond the name and schema, though it does not discuss potential errors, auth, or rate limits, which would elevate it further.

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 optimally concise and well-structured: a one-sentence summary, a clear return value explanation, and a two-line Args section. Every sentence is informative, with no filler or redundant repetition.

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 tool with two parameters and an existing output schema, the description is complete. It covers the return structure, parameter ranges, and source of the key input. The sibling tools are distinct, and nothing critical is missing 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 description coverage is 0%, so the description must fully compensate. It does: product_id is explained as 'the numeric product id (from search_products),' and max_count is given a range and default. This adds semantic meaning far beyond the type-only schema, clarifying how and why each parameter is used.

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 'Fetch customer reviews for a Tokopedia product.' This is a specific verb+resource that immediately distinguishes it from siblings like get_product_details and search_products. The scope (reviews, not details or search) 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?

The description implies usage context by noting that product_id comes 'from search_products', suggesting a typical workflow. However, it does not explicitly state when to prefer this tool over siblings or provide exclusion criteria. The usage guidance is implicit rather than explicit, earning a mid-range score.

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 transparency burden and does well by revealing the exact return structure (products list with name, price in IDR, rating, shop, URL, and count) and the filtering behavior of every parameter. It does not disclose sorting order or edge-case error handling, a minor gap given the otherwise thorough context.

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 well-structured: a single-sentence purpose, a brief return summary, and a clearly formatted Args list covering all 13 parameters. Every line provides necessary information without redundant prose, making it highly scannable for an agent.

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 complexity (13 parameters, no annotations), the description is fully complete: it covers purpose, output format, parameter semantics, and the relationship to get_product_details. The output schema also exists, but the description goes beyond it, ensuring an agent can invoke the tool correctly with no missing 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 description coverage is 0%, so the description must explain all parameters, and it does so comprehensively. Each parameter includes specific meanings, value mappings (e.g., condition 1=new, 2=used; shop_tier 2=Mall, 3=Power Shop), and examples, adding substantial value beyond the bare 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 opens with 'Search Tokopedia products by keyword,' a specific verb+resource statement. It goes beyond a vague purpose by specifying the return format (dict with products list and count), clearly distinguishing it from the sibling tools for product details and reviews.

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 guidance for using search results: 'Each product can be passed to get_product_details for full information.' However, it does not explicitly mention when to prefer this over get_product_reviews or when not to use it, so the guidance is strong but not exhaustive.

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