Sorsa

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

No annotations are provided, so the description must carry the full burden. It discloses the behavioral effect (new products appear) and the parameter format. However, it omits details like whether monitoring persists, if there are limits, or if it's reversible. This is adequate but not exhaustive.

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 concise: three sentences, front-loaded with the main action, followed by parameter explanation and usage trigger. No superfluous words.

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 one required parameter and no output schema, the description covers the input format and the effect. It lacks mention of authentication or quotas, but these are implied by the tool's nature and the platform. Overall, it is complete enough for effective use.

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%, but the description compensates by explaining that 'storefront_id' is an Amazon seller ID and provides an example and where to find it. This adds significant meaning beyond the schema's field name.

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 verb 'start monitoring' and the resource 'another Amazon seller's storefront', and explains the effect that new products will appear in the user's store results. It distinctly differentiates from sibling tools like 'get_storefront_monitors' and 'get_storefront_stats'.

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 explicitly says 'Call when the user says track this seller', providing a clear usage trigger. While it does not list alternatives or when not to use, the context is sufficient given the tool's simplicity and the distinct purposes of sibling tools.

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, and the description does not disclose behavioral traits such as whether the tool overwrites existing entries, handles duplicates, or has any side effects. For a mutation tool, this is insufficient for the agent to understand the full impact of calling it.

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 short and front-loaded: the first sentence states purpose, the second gives context, the third explains a key parameter. Every sentence provides value, though it could be more structured with parameter 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 tool has 11 parameters and no output schema. The description does not explain return behavior (e.g., success/failure messages), side effects, or data persistence. For a complex input tool, this is incomplete.

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%, meaning the description must fully define parameter semantics. However, it only explains 'sourced_from'. The other 10 parameters (e.g., roi, cost, notes) are not described; they are only hinted at by 'passing through the numbers from the analysis'. This leaves the agent with insufficient information to correctly populate all parameters.

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 adds a product to the user's sourcing list (buy list). It distinguishes from sibling tools by specifying it is called after product analysis, which is unique among the listed sibling 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 description provides a clear trigger ('add this to my buy list') and context (after analysis, passing numbers). It explains the 'sourced_from' parameter's meaning. However, it does not explicitly state when not to use this tool or compare it to alternatives like 'create_deal_task' or 'add_storefront_monitor'.

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 adds behavioral context: costs 1 credit unless cached, and specifies the marketplace domain. It doesn't disclose read-only nature or potential side effects, but for a non-destructive analysis tool, this is adequate.

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 concise sentences: first enumerates outputs, second gives usage priority, third provides cost and domain details. No wasted words; all content is value-added and front-loaded.

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 quick analysis tool with 2 parameters and no output schema, the description covers what data is returned, when to call, cost implications, and domain context. It could mention error handling or response format, but it's sufficient for typical use.

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 add meaning. It clarifies 'asin' is the product identifier used for analysis, and for 'domain' it lists the allowed marketplaces (GB, US, DE, FR, ES, IT), which the schema lacks. This compensates well for the missing schema 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 clearly states the tool analyzes an Amazon product by ASIN, listing specific outputs (title, brand, pricing, sales rank, estimated monthly sales, FBA fees, profit basics). It distinguishes from sibling tools like 'analyse_product_full' by emphasizing it's a 'quick analysis'.

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?

Explicitly says 'Call this first whenever the user asks about a specific ASIN', which is strong guidance on when to use. Domain options are listed, but no explicit when-not-to-use instructions; however, the context of being 'quick' implies alternatives exist for deeper analysis.

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 discloses key behavioral traits: it is slower and heavier than analyse_product, costs 1 credit unless recently analysed. Could add more detail on limits or data freshness but sufficient.

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, each adding essential value: first defines scope, second gives usage guidance and cost. No redundant or vague phrasing.

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 no output schema and 2 simple params, the description covers purpose, usage, cost, and comparison. Lacks explicit mention of what the tool returns and domain parameter behavior, but overall informative.

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%, yet the description only clarifies the ASIN parameter implicitly (Amazon product identifier). The domain parameter is not explained; its default 'GB' and purpose are left to the user to infer.

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 performs a full analysis of an Amazon product by ASIN, listing specific features (seller/stock breakdown, buy-box history, variations) and explicitly contrasts it with the sibling tool analyse_product.

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?

Provides explicit guidance: use when wanting seller-level or historical detail, else prefer analyse_product for speed. Also notes credit cost and caching behavior.

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 fully details the fee engine components (tiered referral fees, digital services tax, VAT) and currency context. It does not discuss mutation or side effects, but for a profit calculator, this is sufficient.

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 three sentences, each adding value: core functionality, example usage, and currency context. No extraneous content.

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 core purpose and fee details but omits output format (e.g., returns profit, ROI?) and role of parameters like domain and category. Given no output schema and low parameter coverage, more detail is needed.

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%, and the description only explains sell_price and cost_price via the example. Parameters like domain, category, referral_fee_pct, and pick_and_pack_fee are not explained, leaving the agent without context for defaults or usage.

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 Amazon FBA profit and ROI from sell/cost prices using a full fee engine. It distinguishes from sibling tools by focusing specifically on profit calculation, not broader analysis.

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 explicitly says 'Call for what-if questions' with an example, guiding the agent to use it for hypothetical profit scenarios. It does not mention when not to use or alternatives, but the guidance is clear.

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?

The description states that the user gets notified on matching deals, implying ongoing monitoring. However, it fails to disclose how notifications are delivered, whether tasks are persistent until deleted, any rate limits, or permissions needed. Since no annotations are provided, the description carries the full burden but only partially meets it.

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: the first defines the purpose, the second gives a usage example. No extraneous information, front-loaded key action. Highly concise and well-structured.

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 8 parameters, no output schema, and no annotations, the description barely covers the core action. It does not explain return values, how to manage created tasks, or any limitations. The usage example provides some context but insufficient for a complex creation 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% and the description does not add any explanation for the 8 parameters. The only hint is in the usage example ('£5 profit and 40% ROI'), which loosely maps to min_profit and min_roi, but no formal parameter definitions or constraints are given. This is a critical gap.

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 verb 'create', the resource 'A2A deal-monitor task', and the outcome 'user gets notified whenever the deal feed finds products matching these filters'. It distinguishes from sibling tools like get_deal_tasks (listing) and create_price_monitor (different monitor type).

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?

Provides an explicit example of when to call the tool: 'Call when the user says e.g. notify me about deals over £5 profit and 40% ROI in Toys'. This gives clear context. However, it does not mention when not to use it or suggest alternatives like add_storefront_monitor for different monitoring needs.

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 provided, so description carries full burden. It discloses the alert behavior and source options, but does not cover side effects (e.g., duplicate monitors, permissions) or response details.

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 sentences, each serving a purpose: purpose, usage trigger, parameter clarification. No wasted words, front-loaded with core function.

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?

Without output schema or annotations, description covers basic usage but lacks details on alert delivery, monitor management, and edge cases. Adequate for a simple creation tool but not thorough.

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 0%, so description must add meaning. It explains 'source' with marketplaces list and implies target_price format, but asin is not elaborated beyond context. Provides partial help.

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 watches an ASIN and alerts on price drop, using specific verbs and resource. It distinguishes from siblings like add_storefront_monitor by focusing on ASIN price monitoring.

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?

Provides explicit when-to-use examples ('watch this', 'alert me if it goes below £20'), but lacks when-not-to-use or alternatives like other monitoring tools.

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 exist, so the description must convey behavioral traits. It states the data is 'live' and mentions account connection, but does not disclose pagination behavior, rate limits, or whether it is read-only. Adds moderate value but leaves 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 sentences, each additive: what it returns, when to use, and a prerequisite. No redundant or extraneous 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 4 parameters and no output schema, the description is incomplete. It omits parameter usage, pagination details, and response structure. It only gives a high-level result overview.

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?

0% schema description coverage and the description does not explain any of the 4 parameters (page, limit, search, min_quantity). The agent cannot infer how to use these parameters from the description alone.

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 live FBA inventory (SKUs, quantities, conditions) and distinguishes it from sibling get_* tools which are for other data like orders, refunds, or analytics.

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?

Explicitly tells when to call ('when the user asks what stock they have or about a specific SKU/product') and mentions a prerequisite (Amazon account connected). This provides clear guidance for the agent.

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 provided. The description adds useful behavioral context (requires connected Amazon account, returns profit data) but does not explicitly state read-only nature or address rate limits, permissions, or failure modes.

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 efficiently communicate purpose and key usage notes. However, a bulleted list or clearer separation of parameter details would improve structure slightly.

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?

Covers high-level purpose and key parameters (timeframe, dates) but omits pagination, search, and status filters. No output schema, so description should mention return structure; it does not.

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 0%, and the description only elaborates on timeframe, date_from, and date_to (with format). It ignores page, limit, search, and status parameters, leaving them undocumented both in schema and description.

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 it returns 'The user's Amazon orders with fees and COGS-based profit' and gives concrete example queries. It distinguishes this from sibling tools like get_amazon_inventory by focusing on orders and profit.

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?

Provides explicit example questions and timeframe options. Mentions the requirement of a connected Amazon account. However, does not specify when to avoid using it or mention alternative tools.

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 mentions the limit (max 50) and implies read-only behavior, but does not discuss what happens if no history exists, pagination, or ordering.

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 concise, with two sentences that front-load purpose and usage. It efficiently conveys necessary information without 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?

Given the tool's simplicity (one parameter, no output schema), the description adequately covers what the user gets (recent analyses) and when to use it. It could mention ordering but is sufficient for typical use.

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 0%, so the description must compensate. It adds the constraint 'limit max 50' beyond the schema's default, providing valuable semantic information for the single parameter.

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 'recent product analyses' with specific fields (ASIN, title, when). It differentiates from sibling tools by specifying it's for history retrieval, not adding or modifying data.

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?

Explicitly states when to call: 'when the user asks what they looked at recently, or to re-find a product they mention having analysed.' While it doesn't mention when not to use, the context is clear given sibling tools.

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 provided, but description implies a read-only operation without side effects. It does not mention any destructive actions or rate limits, but the simplicity of the tool makes the intent clear.

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, one for content and one for usage guidance. No filler, efficiently conveys purpose and context.

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 parameterless tool with no output schema, the description fully covers what the tool returns and when to use it. No 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?

No parameters in schema, so description cannot add parameter-level detail. A score of 4 is baseline given zero parameters.

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?

Description clearly states it returns remaining monthly credits and reset date. It distinguishes from sibling tools by focusing on credit status vs other data like analyses or purchases.

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?

Explicitly states when to call: when user asks about usage limits or after a credit-limit error. Provides clear context and use cases.

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 exist, so the description must convey behavioral traits. It mentions returning 'headline stats' but does not detail read-only nature, authentication needs, or side effects. Given the tool's simple summary purpose, the description provides adequate but not comprehensive transparency.

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 long, front-loaded with the purpose, and contains no extraneous information. Every word serves a clear function.

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 no input parameters and no output schema, the description adequately situates the tool as a top-level dashboard summary among 28 sibling tools. It hints at the type of data (monitors and results) but could be slightly more explicit about what 'headline stats' includes.

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, so the schema coverage is effectively 100%. The description need not add parameter details; a baseline of 4 is appropriate for this case.

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 'the user's Sorsa dashboard summary' with 'headline stats across their monitors and results'. It includes a usage context ('Call for a general how's it looking overview before drilling into specific feeds'), distinguishing it as a top-level overview among many sibling 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 description explicitly recommends calling this tool for a general overview before drilling into specific feeds. While it does not list alternatives or when not to use it, the guidance is clear and sufficient for a simple summary 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?

No annotations provided, so description carries full burden. It only states 'Free to call' and that it searches a database. No mention of rate limits, response format, or other behaviors. Could disclose more about operation.

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 plus a brief sort_field list. No wasted words, front-loaded with purpose. Efficient structure.

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, so return structure is not described. While usage scenario and sort options are clear, many parameters and exact output format are missing. Adequate for a read tool but incomplete given parameter count.

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 0% (no descriptions in schema). Description adds value by listing valid sort_field values. However, 11 other parameters (asin, limit, etc.) remain unexplained. Partial compensation but incomplete.

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 searches a specific deal database (Amazon-to-Amazon) and lists provided attributes (profit, ROI, sales rank, pricing). It includes an example query, distinguishing it from sibling tools like get_oa_deals.

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?

Provides explicit scenario: 'Call when the user asks for current deals' with an example. Also notes 'Free to call.' Lacks explicit when-not-to-use or alternatives, but context is clear.

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 discloses that the tool lists tasks that generate notifications. However, it does not mention the return format or any potential side effects. For a simple list tool, this is acceptable but could be slightly more detailed.

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, no wasted words. The purpose is front-loaded, and every sentence adds value. Efficiently structured.

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 has no parameters and a straightforward purpose, the description is complete. It tells what, when, and why. The lack of output schema is acceptable for a list tool where the return is typically self-explanatory.

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 input schema has no parameters, so schema coverage is 100%. The description adds no parameter info, which is appropriate since there are none. Baseline for zero parameters is 4.

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 action: 'List the user's A2A deal-monitor tasks' and defines them as 'saved filters that generate notifications'. It distinguishes this tool from siblings like 'create_deal_task' by mentioning it should be called before creating one.

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?

Explicit guidance on when to use: 'Call before creating one, or when the user asks what monitors they have set up.' This provides clear context and an alternative, making it easy for an agent to decide when to invoke this 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?

No annotations provided, so description must carry the burden. It states the requirement and broad purpose but lacks details on pagination, side effects, rate limits, or data freshness. Minimal behavioral info beyond basic read-only implication.

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 efficient sentences: first states purpose, second provides usage guidance and a requirement. No fluff, front-loaded, every sentence adds value.

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?

Despite low complexity, the description omits parameter details and output format. An agent cannot infer pagination or search functionality from the description. With no output schema or annotations, the description is insufficient for fully correct invocation.

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 documentation coverage is 0%, with 3 optional parameters (page, limit, search) having no description. The tool description adds no meaning to these parameters, leaving the agent without guidance on their purpose or usage.

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 'Refunds issued to the user's customers' as the resource, and the name includes 'get' as the verb. It distinguishes from sibling tools like get_fba_reimbursements and get_fba_returns by specifying refunds to customers.

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?

Explicitly says 'Call when the user asks about refunds or money returned to buyers' and notes the prerequisite 'Requires a connected Amazon account (closed beta).' It doesn't mention when not to use, but the context is clear.

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, and the description only implies a read operation. It does not disclose any behavioral traits such as pagination behavior, rate limits, or whether it is destructive. More detail is needed.

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 brief and front-loaded with the core purpose. However, it could include parameter descriptions without significant bloat.

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?

Without an output schema, the description should explain what the response contains. It does not. The tool has four parameters and moderate complexity, so more completeness is needed.

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%, and the description offers no explanation of the parameters (page, limit, reason, search). The agent must infer their meaning from the schema alone, which is insufficient.

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 it returns reimbursements for lost/damaged stock, which is specific. However, it does not explicitly differentiate from sibling tools like get_fba_refunds or get_fba_returns, leaving some ambiguity.

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 a usage cue ('Call when the user asks what Amazon has paid them back') and mentions prerequisites (connected Amazon account, closed beta). But it lacks guidance on when not to use it or alternatives.

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 provided, so description must convey behavior. It mentions data type (dispositions) but does not disclose pagination, rate limits, or read-only nature. Adequate but not comprehensive.

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 succinct sentences with front-loaded purpose and usage context. No filler or redundant 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?

With 4 parameters, no annotations, and no output schema, the description lacks details on pagination, search behavior, and return data structure. Insufficient for an agent to use effectively without additional 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%. The description only hints at 'disposition' parameter via the dispositions list. Other parameters (page, limit, search) are left unexplained, failing to add value beyond the 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 retrieves 'FBA customer returns with dispositions', using a specific verb and resource. It distinguishes from siblings like get_fba_refunds and get_fba_reimbursements.

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?

Explicitly instructs to call 'when the user asks about returns' and notes the prerequisite of a connected Amazon account. Lacks explicit when-not scenarios or alternatives, but the context is clear.

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 provided, but description discloses it is a read operation that returns quantity comparisons, and implies dependency on prior shipment retrieval. Minor gap: no mention of result format beyond 'line items'.

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 concise sentences convey purpose, usage, and return value with no unnecessary words.

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?

Covers purpose, usage, and output concept (expected vs received), but lacks explicit return fields or error conditions. Adequate for a simple list 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?

Only one required parameter, shipment_id, with 0% schema coverage. Description adds context that ID comes from get_fba_shipments, partially compensating, but no format or source details.

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?

Description clearly states it retrieves line items of one FBA shipment with expected vs received quantities, distinguishing it from sibling get_fba_shipments which lists shipments.

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?

Explicitly says to call after get_fba_shipments when user asks about specific shipment contents or receipt verification, providing clear usage context.

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 provided, so description must carry full burden. It states 'inbound FBA shipments' and 'summary stats' but does not disclose side effects, rate limits, or whether the operation is read-only.

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 concise, front-loaded sentences. First sentence states purpose and output, second gives usage guidance and prerequisite. No wasted words.

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?

With 4 parameters, no output schema, and no annotations, the description lacks details on what 'summary stats' includes or how parameters filter results. Incomplete for effective agent use.

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% and the description provides no additional meaning for the 4 parameters (page, limit, search, status). No parameter descriptions in schema or description.

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?

Clearly states it retrieves inbound FBA shipments with summary stats. Distinguishes from siblings like get_fba_shipment_items and get_amazon_inventory.

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?

Specifies when to call (user asks about shipments sent to Amazon) and prerequisite (connected Amazon account). Lacks explicit exclusions but context is clear.

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 the credit cost and 24-hour cache, which are key behavioral traits. However, it does not mention rate limits, required permissions, or the nature of the return data (though no output schema exists). This is still valuable for a read-only operation.

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 sentences, front-loaded with the purpose, and no wasted words. Every sentence adds value: purpose, usage context, and cost/caching behavior.

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, usage, and cost, but with no output schema, it lacks details on what data is returned (e.g., prices, currencies, marketplaces). Given the tool's complexity (2 parameters), the description is partially complete but leaves the return format 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 description coverage is 0%, so the description must compensate. It implies the 'domain' parameter identifies the marketplace (default GB), but does not explain allowed values or how it affects results. The 'asin' parameter is self-explanatory. More explicit guidance on the domain parameter would improve clarity.

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 'Prices for the same ASIN across international Amazon marketplaces,' specifying the verb (get), resource (international prices), and scope (cross-market). It distinguishes itself from sibling tools like 'get_product_sellers' and 'analyse_product' by focusing on price comparison across markets.

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?

Explicitly states when to call: 'when the user asks about cross-market price differences or EU arbitrage opportunities for a product.' Also provides cost and caching behavior, aiding the agent in deciding whether to invoke 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?

Describes the result (purchased items with listing status) but no annotations provided; could add more detail about behavior like read-only or auth needs.

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 efficient sentences, front-loaded with purpose and usage, no wasted words.

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, but description includes key fields (SKU, listing errors). Could specify more detail about return structure but adequate for a simple 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?

No parameters, so schema coverage is 100%. Description adds meaning by explaining the return content beyond the empty 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?

Clearly states it returns items purchased from sourcing list with listing status, distinguishing from siblings like get_purchases and get_items_sourced.

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?

Explicitly says when to call: when user asks about purchased items or listing progress. No exclusions or alternatives though.

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?

Without annotations, the description carries the full burden. It reveals the output content (products, cost/profit projections, status) but does not discuss auth requirements, rate limits, or error states. Adequate but minimal.

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 key information, no extra words. Every sentence serves a purpose.

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 no parameters, no output schema, and no annotations, the description covers the main purpose and usage. Could hint at return format or limitations, but is mostly complete.

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?

No parameters exist, so baseline score of 4 applies. The description does not need to add param info.

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 that the tool returns the user's sourcing list, including products found, considering buying, with cost/profit projections and status. The phrase 'sourcing list' distinguishes it from siblings like 'get_items_purchased'.

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?

Explicitly says 'Call when the user asks about their buy list or sourcing pipeline', providing clear context. Does not specify when not to use, but the sibling list implies alternatives.

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 describes the return format (grouped by task) but does not explicitly state it is a read-only operation or disclose any side effects, errors, or rate limits. For a simple retrieval tool, this is adequate but not thorough.

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 long, front-loads the core purpose, and includes parameter details efficiently. Every sentence adds value with no 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 the tool has one parameter and no output schema, the description provides sufficient context: what it returns, when to use it, and parameter options. Missing details like pagination or ordering are minor gaps for a simple list 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?

The input schema only defines 'show_checked' as a string with a default. The description adds valuable meaning by specifying the allowed values ('unchecked', 'checked', 'all') and their default, which is critical for correct invocation.

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 specific verb 'get' and resource 'notifications' (price-alert notifications), and adds context that they are from A2A monitor tasks grouped by task. This distinguishes it from sibling tools like get_price_monitors and get_deal_tasks.

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 explicitly says 'Call when the user asks what alerts or notifications they have.' This provides clear when-to-use guidance, though it does not mention alternatives or when not to use it.

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 provided, so description bears full burden. It mentions return data (products, profit, ROI) and lists sort options, but does not disclose pagination, required permissions, or effects of omitted filters. Adequate but not comprehensive.

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 concise sentences, first defining the tool, second adding usage guidance and sort options. Efficient, though sort list could be formatted more cleanly.

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?

With 9 parameters, no output schema, and no annotations, the description fails to explain how filters work or default behavior. The agent lacks enough context to use parameters effectively.

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%. The description only explains the 'sort' parameter by listing valid values, but ignores others like page, search, min_roi, retailer, min_profit, min_monthly_sold. Insufficient compensation for large parameter set.

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 it returns profitable online-arbitrage deals with profit and ROI, and distinguishes it from siblings by specifying when to call (OA or retail sourcing).

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?

Explicitly says 'Call when the user asks about OA or retail sourcing opportunities', providing clear context, though no explicit when-not-to-use or alternative naming.

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, but the description uses 'list' implying a read-only operation with no side effects. This adequately discloses the behavioral nature of the 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?

Two concise sentences with no wasted words. The key information is front-loaded and every sentence adds value.

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 no parameters, no output schema, and low complexity, the description fully covers what an agent needs to invoke the tool correctly. It explains the output's purpose and relationship to another 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?

The input schema has zero parameters, so schema_description_coverage is 100%. The description does not need to add parameter details; it is sufficient.

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 it lists retailers available in OA deals feed and explicitly connects to its use as a filter for get_oa_deals. This verb-resource pairing is specific and distinct from sibling 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 description explicitly states when to use (to know valid retailer names for get_oa_deals). While it doesn't list alternatives, the purpose is singular and context is clear.

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 provided, but the description implies a safe read operation. Could mention pagination or limits, but not essential for zero-parameter 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?

Two concise sentences with no unnecessary wording; front-loaded with the key action.

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?

Complete for a simple list tool with no parameters and no output schema; describes what is returned.

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?

No parameters exist, so baseline is 4. Description adds value by explaining the output includes ASINs and target prices.

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 it lists ASIN price monitors (watched products with target prices), distinguishing it from sibling tools like create_price_monitor.

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?

Explicitly advises calling before creating a monitor to avoid duplicates, and when the user asks what they are watching.

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 provided, so description carries full burden. Discloses credit cost and 24-hour caching but lacks details on data scope (e.g., all sellers or limited), side effects, or authentication needs.

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 front-loaded purpose and usage. Every sentence adds value; no wasted words.

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 no output schema and 2 parameters, description covers main purpose, usage context, and cost behavior. Lacks domain parameter explanation, which is a minor 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?

Schema has 2 parameters (asin, domain) with 0% coverage. Description only mentions 'Amazon ASIN' implicitly; domain parameter is not explained. Fails to add meaning beyond 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?

Description clearly states it retrieves current sellers and stock levels for an Amazon ASIN, using specific verbs (get, sellers, stock) and resource (ASIN). It also distinguishes from sibling tools by focusing on seller/stock queries.

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?

Explicitly states when to call: when user asks who is selling, number of sellers, or competitor stock. Adds credit cost context but doesn't mention when not to use or alternatives.

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 full burden. It discloses the timeframe parameter but does not mention any other behavioral traits such as read-only nature, sorting, or pagination. For a simple list tool, this is acceptable but could be improved.

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 concise sentences with no redundancy. The essential information is front-loaded and every sentence is meaningful.

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 and parameter but lacks details on return format or behavior, such as ordering or limits. Given the simplicity of the tool (1 param, no output schema), it is minimally sufficient.

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 has one parameter with 0% description coverage. The description adds value by listing the possible timeframe values ('all, day, week, month or year'), clarifying what the parameter accepts beyond the default.

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?

Description explicitly states 'purchase history' and lists timeframe options, clearly indicating the tool retrieves past purchases. However, it does not differentiate from the sibling 'get_items_purchased', which likely has similar functionality.

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?

Directly states when to call ('when the user asks what they've bought recently'), providing clear usage context. No alternatives or exclusions are mentioned.

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?

Describes output (statistics with breakdowns) but does not disclose behavioral traits like read-only nature, rate limits, or prerequisites. Since no annotations exist, the description carries full burden and only partially fulfills it.

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 that are front-loaded with the tool's purpose and include an example. No redundant information; every part 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 tool with no parameters and no output schema, the description adequately conveys the result structure. However, it could mention whether statistics are all-time, if default time periods apply, or if the call is safe to repeat. Missing some completeness for a no-parameter 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?

With 0 parameters and schema coverage at 100%, the baseline is 4. The description adds value by explaining what the tool returns and the breakdowns, which is meaningful context beyond the empty 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?

Description clearly states it returns purchase statistics (total spent, projected profit, items bought, ROI) with breakdowns daily/monthly/yearly and by category/source. This differentiates it from sibling tools like get_purchases (raw data) and get_dashboard_summary (broader summary).

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?

Includes an example question 'how much have I spent this month?' that implies when to use. However, it does not explicitly state when not to use or name alternative tools for different queries.

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 provided, so description must cover behavioral traits. It mentions prerequisites (connected account) and status options, but does not explicitly state it is a read operation or discuss pagination/limitations. However, the context implies a safe read, so it is adequate.

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 concise sentences, front-loading the purpose and immediate usage guidance. No unnecessary verbiage.

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 4 parameters, no output schema, and no annotations, the description is somewhat complete for a listing tool. It distinguishes from a sibling and explains the status filter, but lacks details on pagination parameters and claim_type, leaving gaps for an agent unfamiliar with 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 coverage is 0% with no descriptions in the schema. The description only explains the status parameter (and its possible values), but neglects to describe page, limit, and claim_type. This leaves three of four parameters unexplained, insufficient for effective use.

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?

Description clearly states it lists individual reclaim opportunities with evidence, distinguishing it from sibling get_reconciliation_summary by specifying it lists actual claims.

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?

Explicitly instructs to call after get_reconciliation_summary, lists possible status values (open, dismissed, filed, all), and notes the requirement of a connected Amazon account in closed beta.

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 provided, so description carries full burden. It discloses that money is 'Sorsa has detected' (not raw), implies read-only behavior, and mentions beta status. Could add more about data freshness, but sufficient for a summary 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?

Two concise sentences. First defines output, second gives usage context. No unnecessary words.

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 no output schema, description adequately hints at return format (totals by claim type, examples listed). No annotations needed. Tool requires minimal context; definition is complete.

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?

Input schema has 0 parameters with 100% coverage. Description adds no param info, which is appropriate since none exist. Baseline score of 4 applies.

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?

Description clearly states the tool returns 'totals of reclaimable money' broken down by claim type, specifying the resource (reclaimable money) and action (get totals). It distinguishes from siblings like get_reconciliation_claims (detailed claims) and get_fba_refunds (specific refunds).

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?

Provides explicit when-to-use guidance with 'Call for "how much does Amazon owe me?"' and notes the prerequisite of a connected Amazon account in closed beta. Does not explicitly exclude alternatives, but the sibling list implies context.

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 full burden. It states the tool lists storefronts, which is a read operation, but does not explicitly confirm non-destructiveness or mention any side effects. The description is adequate but could be more explicit about being read-only.

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 long, front-loaded with the purpose, and contains no unnecessary words. It is efficient and well-structured.

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 tool with no parameters and no output schema, the description covers purpose and usage context. It does not describe the return format, but the simplicity reduces the need for more detail. It is mostly complete.

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?

There are no parameters, so the description has no responsibility to add parameter semantics. It still clearly explains the tool's purpose, which is sufficient.

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 lists Amazon storefronts (competitor sellers) the user is monitoring. The verb 'List' and resource 'storefronts' are specific, and it distinguishes from siblings like 'add_storefront_monitor' and 'get_storefront_stats'.

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 usage context: 'Call before adding one, or when the user asks which storefronts they track.' It implies when to use but does not explicitly state when not to use, which would earn a 5.

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 provided, so description carries full burden. Implies read-only via 'summary stats' but does not explicitly state read-only behavior, auth requirements, rate limits, or error handling. Adequate but could be more 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?

Two concise sentences. First sentence front-loads purpose with specific resource and data types. Second sentence provides usage guidance. No wasted words.

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 and no output schema, description sufficiently covers the tool's role as an overview. Mentions key return fields (stores tracked, result counts). Could be more detailed about return structure, but adequate for a simple stats 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?

No parameters exist, so baseline is 4. Description adds no parameter info, but none is needed. Schema coverage is 100% automatically.

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?

Clearly states the tool provides summary stats for storefront monitors, including stores tracked and result counts. Distinguishes from sibling get_store_results by positioning as an overview before drilling deeper.

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?

Explicitly advises to call for an overview before drilling into get_store_results, providing clear context for when to use this tool and when to use its sibling.

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 provided, so description carries full burden. It discloses that the tool returns products found by monitors (read-only behavior). However, it does not mention authentication, rate limits, or response format, which could be relevant.

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 key information, no redundant words.

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?

Tool is simple with one param, but has no output schema. Description does not hint at response structure (e.g., list of products). Lacks completeness for an agent to fully understand what to expect.

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?

Only one optional parameter (store_id) with 0% schema description coverage. Description adds 'optionally filter to one store_id', but does not explain format, allowed values, or behavior 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?

Description clearly states the verb 'get' and resource 'store results' (products found by storefront monitors). Distinguishes from sibling tools like get_storefront_monitors (monitor list) and get_deal_results (deals).

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?

Explicitly says when to call: when user asks what their storefront monitors have found. Mentions optional filtering by store_id, but does not provide when-not-to-use guidance or compare to alternatives.

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 description carries full burden. It implies a read-only operation but does not disclose potential side effects, rate limits, or authentication needs. Minimal behavioral context beyond returning an ASIN.

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: first defines purpose, second gives usage guidance. No unnecessary words; highly efficient and front-loaded with key 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 simple lookup tool, the description covers primary purpose and usage flow. Lacks details on return format or error handling, but given no output schema, the provided information is sufficient for basic agent decision-making.

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 has two parameters with no descriptions (0% coverage). Description explains 'code' as the barcode but does not mention 'domain' at all. The agent cannot infer domain's purpose or values without additional context.

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?

Description clearly states the tool resolves a barcode to an Amazon ASIN, using specific verb 'resolve' and resource 'barcode to ASIN'. It distinguishes from siblings like search_by_title and analyse_product.

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?

Explicitly instructs when to call this tool (when user gives barcode instead of ASIN) and what to do with the result (feed into analyse_product). Provides clear context for usage.

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 must carry the full burden. It only states the action (mark as checked/unchecked) but omits behavioral details like idempotency, error handling, or side effects. The description is minimal.

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 focused sentences with no wasted words. It is front-loaded and 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 no output schema, no annotations, and 2 undocumented parameters, the description lacks completeness. It does not explain return values, prerequisites (e.g., notification must exist), or error conditions. More context is needed for safe usage.

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 0%, so the description must explain parameters. It partially explains the 'checked' parameter by equating it to 'reviewed' or 'unchecked', but does not explain 'notification_id' or how to obtain it. There is no mention of the default value or required nature.

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 marks a notification as checked or unchecked, using a specific verb and resource. It distinguishes from sibling tools like 'get_notifications' which only retrieves 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?

The description provides clear usage context: 'Call after the user says they've dealt with an alert, or to un-mark one.' It does not explicitly exclude other scenarios but gives good 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?

No annotations provided, so description must carry full burden. It mentions returning top matching ASINs with thumbnails and limit range, but does not disclose rate limits, authentication needs, error handling, or behavior when no matches are found.

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, each serving a distinct purpose: function, usage guidance, and constraint. No extraneous words, front-loaded with the main action.

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?

Covers core function, usage trigger, and limit constraint. Lacks details on output format beyond 'ASINs with thumbnails', domain parameter validity, and no output schema. Adequate for basic comprehension but not fully comprehensive.

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 has 0% description coverage. Description explains 'limit is 1-10' but does not describe the 'title' or 'domain' parameters. 'title' is self-explanatory but 'domain' default 'GB' is not explained. Only partial compensation for missing schema 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?

Clearly states the tool searches Amazon by product title via Keepa and returns top matching ASINs with thumbnails. Distinguishes from siblings like lookup_by_ean by specifying it's for when user has no ASIN or barcode.

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?

Explicitly says 'Call when the user names a product without an ASIN or barcode' and instructs to present candidates for selection before analysis. Provides clear context for use.

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