TomeScout

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds useful context beyond that: it uses two independent public models, returns a range rather than a single value, and flags UK/DE as rough scalings. No contradiction exists between the description and 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?

Two sentences, front-loaded with the main function and output, followed by scope and an optional multiplication behavior. Every clause adds information; there is no repetition of the title or fluff.

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 low-complexity, read-only estimation tool, the description covers inputs, supported marketplaces/formats, output type, and optional royalty scaling. There is no output schema, so the description's statement that it returns an estimated range of units per day and per month is sufficient, though exact response structure is not detailed.

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 carry parameter meaning. It conveys bsr ('Best Sellers Rank'), format ('Kindle Store or print'), marketplace ('Amazon.com ... UK/DE'), and royaltyPerSale ('Optionally multiplies by royalty per sale'). It does not specify currency or exact units for royaltyPerSale, but the core semantics are recoverable.

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 ('Turns ... into an estimated range of units per day and per month') applied to a clear resource: an Amazon Best Sellers Rank. It defines what the rank is, distinguishes from a general sales calculator, and separates it from sibling tools like royalty_calculator by focusing on rank-to-sales estimation rather than royalty computation.

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 usage context: it works for Amazon.com Kindle Store or print, and notes that UK/DE results are rough scalings. It does not explicitly name alternative tools or state when not to use it, but the scope and limitations are clear enough for an agent to select it appropriately.

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 this as read-only, idempotent, and non-destructive, so the description doesn't need to restate safety. It adds meaningful behavioral detail by specifying the categories of issues detected and stating that each issue links to its source KDP help page, which gives the agent a clear picture of output behavior.

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 carry the entire definition with no wasted words. The first sentence front-loads the verb, object, and scope; the second adds output behavior. The dense enumeration earns its place because it is the core value of the tool.

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 read-only tool with no output schema, the description adequately covers the input domains, the checks performed, and the output shape (issues with source links and optimization tips). It doesn't describe pass/fail formatting or severity levels, but those are minor for an agent deciding whether to select and invoke the 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 0%, so the description must compensate. It does add meaning by mapping the operation to all five fields (title, subtitle, description, keyword boxes, categories) and listing the guideline areas, which is more than the bare schema provides. However, it doesn't explain per-field expectations or how values map to specific checks, leaving some ambiguity.

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 a specific verb ('Checks') and names a concrete resource: a book's title, subtitle, description, keyword boxes, and categories. It then enumerates the exact guideline dimensions checked, making the tool's function unmistakable and clearly distinct from the sibling research and calculation 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 context of use is clearly implied: use this when a KDP listing needs compliance checking or optimization tips. However, there is no explicit when-not-to-use guidance or named alternatives among the sibling tools, so the agent must infer the right selection from the described function alone.

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 mark the tool as read-only and idempotent, and the description adds substantial behavioral detail beyond them: priority ordering, title/subtitle word removal, dropping of prohibited/trademarked/author-title phrases, and reporting of what did not fit. There is no contradiction with the 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 two tight sentences with no filler. It front-loads the core action and then packs each subsequent clause with a distinct behavioral rule or output note.

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 the input, the packing rule, filtering behavior, and the outcome of reporting unfitted phrases. Since there is no output schema, a slightly more explicit statement of the returned structure would make it fully complete, but the invoking context and side effects are sufficiently clear.

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 0% schema description coverage, the description carries the burden, and it does compensate: 'phrases' are the packed input, 'title' and 'subtitle' feed the removal logic, and 'skipTitleWords' relates to title-word removal. It does not fully spell out each parameter's edge-case behavior, but the main semantic roles are inferable.

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 a specific verb and resource—'Packs keyword phrases ... into KDP's 7 keyword boxes'—and adds a clear constraint ('50 characters each'). It also differentiates the tool from siblings like keyword_research or ads_keywords by focusing on box-filling rather than discovery or advertising.

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 clearly implies the use case: an agent has a candidate phrase list and needs to produce a KDP-compliant keyword-box fill. It does not explicitly name alternatives or exclusion conditions, but the context is clear and not misleading.

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 signal readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful behavioral context beyond that: it uses current 2026 price bands, accounts for delivery cost, VAT/GST, and KDP Select rules, and is a pure calculation tool with no contradiction against the 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?

Three dense sentences front-load the main action and then neatly separate eBook, paperback, and KU cases. There is no filler, no repetition of schema enums, and every clause contributes useful information.

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 11 parameters and no output schema, the description covers the core royalty paths well but leaves gaps: monthlySales is never explained, paperback printing-cost inputs (ink, trim, pages) are only implied, and the expected return/breakdown shape is not described. An agent could call it correctly after inference but not fully from explicit guidance.

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 only 45% schema description coverage, the description compensates by connecting inputs to calculation logic: fileSizeMb drives delivery cost, kdpSelect and marketplace govern the 70/35 option and store list, and kuPagesReadPerMonth feeds KU earnings. It leaves monthlySales and the ink/trim cost relationship implicit, so it does not earn 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?

The description uses a specific verb ('Calculates') and a precise resource ('KDP royalties per sale'), then expands with eBook vs paperback, store coverage, and KU earnings. This makes it immediately distinguishable from sibling tools like keyword_research and bsr_to_sales, none of which perform royalty math.

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 first sentence makes the core use case explicit: call this whenever a KDP royalty or Kindle Unlimited earnings figure is needed. It does not list exclusion conditions or name alternatives, but the sibling tools are clearly separate functions, so the context is unambiguous.

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