Sorsa

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

No annotations are provided, so the description carries full burden. It describes the outcome (new products appear) and the parameter format, but does not disclose side effects (e.g., if already monitoring, rate limits, reversibility). With no annotations, more behavioral detail would be beneficial.

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 no waste. The first sentence states the purpose and effect, the second explains the parameter and gives usage guidance. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, single-parameter monitoring tool with no output schema, the description covers all essential aspects: what it does, what the parameter means, and when to invoke it. No gaps are evident.

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 only one parameter with a generic title. The description adds valuable semantics: it explains what storefront_id is (Amazon seller ID), provides an example format, and tells where to find it ('in the seller page URL'). This goes well 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 action ('Start monitoring') and the resource ('another Amazon seller's storefront'), with a specific outcome ('new products will appear in store results'). It effectively distinguishes from sibling tools like get_storefront_monitors which lists existing monitors.

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 trigger phrase ('Call when the user says 'track this seller''), giving clear context for use. Does not mention alternatives or when not to use, but the guidance is helpful and specific enough.

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 explains the action and clarifies one parameter but lacks depth on side effects, permissions, or return behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no wasted words, front-loads the primary purpose and usage trigger.

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?

Adequate for a simple add-to-list tool, but lacks details on return values, success/failure behavior, and how the list is affected. Could be more 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?

Schema coverage is 0%; description only mentions 'sourced_from' and implies data comes from analysis. For 11 parameters, this is insufficient to fully understand each field's purpose.

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 action (add), the resource (product to sourcing list), and provides typical usage scenario after analysis. Distinguishes from sibling tools by being a write operation.

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 when user says 'add this to my buy list' and typically after analysis. No exclusion or alternative tools mentioned, 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 full burden. It discloses credit cost, caching behavior, domain scope, and the nature of the operation (quick analysis). Does not mention side effects (likely none), but adequately covers key behavioral aspects.

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: first lists outputs, second gives usage guidance, third covers cost and domain. No unnecessary words, front-loaded with essential 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?

Given no output schema, description adequately lists return fields. Covers cost, caching, domain. Missing error conditions or permission requirements, but overall complete for a quick analysis 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%, so description must compensate. It explains the 'domain' parameter values (GB, US, DE, FR, ES, IT) and mentions ASIN. While ASIN format is not detailed, the description adds meaningful context 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?

Description clearly states the tool performs a quick analysis of an Amazon product by ASIN, listing specific outputs (title, brand, pricing, sales rank, etc.) and explicitly says 'Call this first whenever the user asks about a specific ASIN,' distinguishing it from the full analysis sibling.

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?

Advises to call this tool first for any ASIN query, implying it is the initial step before more detailed tools like analyse_product_full. However, it does not explicitly state when not to use it or list 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?

With no annotations, the description carries full responsibility. It discloses that the tool is 'slower and heavier,' implies read-only analysis, and mentions cost and caching (free if analysed last 24 hours). It does not hide any destructive behavior. Slight room for improvement: could explicitly state it does not modify data.

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 four sentences, each providing essential information: what the tool does, what it includes, performance comparison, usage guidance, and cost/caching details. It is front-loaded with the core purpose and contains 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 only 2 parameters, no output schema, and no annotations, the description covers the tool's purpose, inputs, alternatives, cost, and caching. It does not describe the return format, but that is acceptable without an output schema. Minor gap: could mention that it returns a comprehensive report or data structure.

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 description mentions 'ASIN' as the input, which covers the required parameter. However, the optional 'domain' parameter is not described, and the schema has no parameter descriptions (0% coverage). While agents may infer domain from context, the description could add value by explaining that domain is the Amazon marketplace (e.g., GB, US).

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 performs a 'full analysis of an Amazon product by ASIN' and specifies it includes 'seller/stock breakdown, buy-box history and variations.' It distinguishes itself from sibling 'analyse_product' by explicitly saying it includes everything in that tool plus more.

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 tells when to use this tool: 'use when the user wants seller-level or historical detail in one shot.' It also contrasts with 'analyse_product' by noting it is 'slower and heavier,' providing clear alternatives. Additionally, it mentions the 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?

No annotations are provided, so the description must disclose behavioral traits. It mentions using the full fee engine with tiered referral fees, digital services tax, and VAT, and that prices are in marketplace currency. However, it lacks details on side effects, authentication needs, or output format, so transparency is moderate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, consisting of two sentences that front-load the key action and provide a clear example. Every sentence adds value with no waste.

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 6 parameters, no output schema, and no parameter descriptions, the description is incomplete. It does not explain optional parameters or the return format, which is critical for a calculation tool. The example is helpful but insufficient for full 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?

The schema has 0% description coverage. The description adds meaning for 'sell_price' and 'cost_price' (prices in marketplace currency) but fails to explain the other four parameters (domain, category, referral_fee_pct, pick_and_pack_fee), leaving significant gaps.

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 for given sell/cost prices using a full fee engine. It uses specific verbs and resources, and is distinct from sibling tools like 'get_dashboard_summary' or 'get_product_performance'.

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 when-to-use guidance with a what-if scenario example ('what would I make selling at £24.99 if I buy at £12?'), but does not 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?

With no annotations, the description discloses caching behavior (uncached ASINs queued, re-call needed), return structure (state, gated, apply_link), and prerequisite (connected Amazon account). It doesn't mention side effects or rate limits, but the tool is read-only by nature.

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 four sentences, each adding value: purpose, usage example, behavior/return, and prerequisite. No fluff, 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?

Without an output schema, the description covers the return shape and async behavior well. However, it omits details about the 'condition' parameter and its possible values, which is a gap for a complete understanding.

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 explains 'asins' (array of ASINs to check) but does not mention the 'condition' parameter (with default 'new_new') at all, leaving a significant 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 tool checks if a user is ungated for ASINs, provides example queries, and mentions return fields. It distinguishes itself from sibling tools which are mostly get/dashboard functions, none of which check eligibility.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives specific example use cases ('am I ungated?', 'can I sell these ASINs?') and notes the caching behavior requiring re-call for pending ASINs. It doesn't explicitly state when not to use, but the context is clear and no alternative tool exists among siblings.

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 the description carries the full burden. It discloses that the tool creates a notification task and that the user gets notified on matching deals. It does not mention persistence, limits, or whether it is a write operation, but the core behavior is 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, first stating the purpose and second giving a usage example. No wasted words, front-loaded, and easy to parse.

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 schema descriptions, no output schema, and no annotations, the description is insufficient. It lacks explanations for most parameters, does not mention return values, error cases, or operational constraints. The example provides some context but is not 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?

The input schema has 8 parameters with 0% description coverage. The description only hints at min_profit, min_roi, and category via the example, leaving source, keepa_drops, max_asin_rank, and min_monthly_sales unexplained. The description does not compensate adequately for the lack of 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 it creates an A2A deal-monitor task that notifies the user on matching filters. It distinguishes from siblings like get_deal_tasks and get_deal_results by specifying the creation action. The example invocation reinforces the purpose.

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 concrete usage scenario with an example user request. It explicitly says 'Call when the user says...'. However, it does not cover when not to use or compare to alternatives like get_deal_tasks for listing existing tasks.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It mentions alerting and the source parameter, but does not clarify persistence (e.g., does it create a recurring monitor?), side effects, or prerequisites beyond the example phrases. Partial 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?

Two efficient sentences: first states core purpose, second gives usage triggers and parameter clarification. No fluff, front-loaded, every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description lacks return value info, prerequisites (valid ASIN?), and any rate limits or authentication needs. It covers the essentials but leaves gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% coverage, so the description must add meaning. It explains the 'source' parameter fully by listing marketplaces, and the example implies 'target_price' is a number and 'asin' is a product identifier. This adds value beyond the schema's bare property names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Watch... and alert') and resource ('ASIN'), and clearly differentiates from sibling tools like 'add_storefront_monitor' or 'get_price_monitors' by focusing on creation with a price target.

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 examples ('watch this', 'alert me if it goes below £20'), giving clear context for when to call the tool. However, it lacks explicit statements of 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 exist. Description says it lists recent jobs and status, but doesn't detail ordering, limits, or side effects. Adequate for simple list tool but lacks depth.

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-loaded with main function and follow-up purpose. 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 parameters or output schema, description covers purpose and usage adequately. Could mention more about what is returned (e.g., list with job_ids and statuses), but 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?

No parameters in schema; baseline 4. No need for additional parameter 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?

Description clearly states it lists recent A2A Finder scan jobs and their status, and specifies the purpose: to find a job_id. Distinguishes from siblings like start_a2a_finder_scan and get_a2a_finder_status.

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?

Description implies usage: call to get a job_id for fetching results, but does not name the next tool explicitly or provide when-not-to-use 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 carries full burden. It indicates a read operation (status/retrieval) and describes return value, but lacks explicit statement that it is non-destructive or any authentication requirements. Adequate for a simple status 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 sentences, front-loaded with purpose. No extraneous words; every sentence adds value. Efficient and clear.

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 (1 parameter, no output schema, no nested objects), the description fully covers what an agent needs: it returns progress and results, and is tied to a specific scan process. Complete for its complexity.

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 parameter descriptions), but description adds context by explaining that job_id is from start_a2a_finder_scan. This provides functional meaning beyond the schema's type-only definition, partly compensating 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?

Description clearly states the tool returns 'Status and results of an A2A Finder scan', specifying it provides progress percentage and, upon completion, profitable cross-marketplace flips per ASIN and per country. It distinctly differentiates from sibling tools like start_a2a_finder_scan (which initiates the scan) and get_a2a_finder_jobs (which lists jobs).

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 links to start_a2a_finder_scan, indicating it should be used after initiating a scan. However, does not mention when not to use or provide alternative tools for similar status checking (e.g., get_a2a_finder_jobs).

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 requirement for connected Amazon account, describes output structure (buckets, capital, risk), and implies read-only nature. Lacks detail on pagination or rate limits, but acceptable for simple 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?

Extremely concise: two sentences with key details front-loaded. No filler, every part 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 and no output schema, description covers output, purpose, and prerequisite. Complete for understanding what the tool returns and when to 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?

No parameters in schema; description correctly adds no param info. Baseline 4 for 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?

Clearly states it provides age profile of on-hand FBA stock with specific buckets (0-30, 31-90, 91-180, 181-365, 365+) and includes capital tied up and aged 90+ days risk. Distinguishes from siblings by focusing on age profile, not general 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?

Explicitly gives example use cases: 'what stock is old or at risk?' and 'how much capital is stuck in aged inventory?'. No explicit exclusions or alternatives, but context makes usage 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 full burden. It discloses the return data (SKUs, quantities, conditions) and the prerequisite. However, it lacks details on pagination behavior, filtering logic (e.g., how search works), and rate limits. 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?

Three concise sentences: first states purpose, second gives usage guidance, third mentions prerequisite. No redundancy, well 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?

Given four parameters and no output schema or annotations, the description provides basic purpose and usage but omits parameter explanations and output structure. Adequate for a simple tool but leaves gaps for effective agent 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 description coverage is 0%, and the description does not explain any parameter (page, limit, search, min_quantity). The parameter names are self-explanatory, but the description fails to add meaning beyond them, leaving agents to infer 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 it returns live FBA inventory with SKUs, quantities, and conditions. It specifies when to call (user asks about stock or specific SKU), distinguishing it from sibling tools like get_amazon_orders or get_aged_stock.

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-call guidance ('Call when the user asks what stock they have or about a specific SKU/product they sell') and a prerequisite ('Requires their Amazon account to be connected'). No explicit exclusions or alternative tools mentioned, 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 provided, so the description carries full burden. It discloses the output includes fees and profit, and that a connected account is required. However, it does not mention pagination behavior, rate limits, or what happens if no results. It also fails to describe how the 'search' parameter works beyond implying SKU filtering.

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 with no wasted words. It front-loads the purpose, then provides example queries, then details parameters and requirements. Each sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters, no output schema, and many siblings, the description is incomplete. It fails to describe pagination, status filtering, and return structure. It provides enough context for basic use but leaves agents guessing on important details.

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 description must compensate. It adds meaning for timeframe and date_from/date_to (format YYYY-MM-DD) and suggests 'search' is for SKU. But it does not explain 'page', 'limit', or 'status' parameters, leaving significant gaps.

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 retrieves the user's Amazon orders with fees and COGS-based profit. It provides example use cases like 'how are sales today?' and 'show orders for this SKU', clearly differentiating it from siblings like dashboard summary or 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?

It specifies when to call the tool (questions about sales, orders for a SKU) and gives context about timeframes (today, yesterday, week, month) or date range. It also mentions a prerequisite (connected Amazon account). However, it does not explicitly state when not to use it or mention 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 carries full burden. It correctly implies a read operation but does not explicitly state it is read-only, safe, or requires authentication. Adding such context would improve 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?

Two sentences, front-loaded with purpose and fields, then usage guidance. Every word 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?

For a simple retrieval tool with one optional parameter and no output schema, the description provides all necessary information: what it returns, when to use, and a parameter constraint. No gaps noted.

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 only parameter 'limit' has no schema description (0% coverage), but the description adds 'max 50', which clarifies constraints beyond the default of 20. This compensates well.

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 the user's recent product analyses and lists the fields included (ASIN, title, when). It distinguishes from sibling tools by focusing on analysis history, not other metrics.

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 when the user asks about recent analyses or to re-find a product. Constraint 'limit max 50' provides clear usage boundary.

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 fully disclose behavioral traits. It states the requirement of a connected Amazon account and that data is derived from the user's sales, implying read-only. However, it does not discuss data freshness, error handling (e.g., no sales), caching, or side effects. While the constraints are helpful, the lack of deeper behavioral details 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?

The description is highly concise with two sentences. The first sentence immediately states the core purpose. The second sentence packs usage guidance and parameter limits efficiently. No redundant information is present, and key details are 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?

The tool has two optional parameters and no output schema or annotations. The description covers purpose, usage, parameter limits, and a prerequisite. However, it does not describe the output format (e.g., whether it returns a list, what fields are included), which is needed for an agent to use the tool effectively. Given these gaps, the description is adequate but not fully 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?

With 0% schema description coverage, the description must compensate. It explicitly explains both parameters: months as the number of recent months (up to 24) and top_n as the number of top sellers (up to 50). This goes beyond the schema's names and types, providing meaningful context. However, it does not describe the exact format or relationship between parameters (e.g., whether months and top_n are independent) which prevents a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the user's own top-selling ASINs by net profit over recent months. It is specific about the resource (ASINs), the metric (net profit), and the scope (user's own sales). The usage phrases 'what are my best sellers?' and 'top products this year' further clarify purpose, and the tool is distinct from sibling tools like get_dashboard_summary or get_product_performance.

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 use cases ('what are my best sellers?', 'top products this year') and states constraints (months up to 24, top_n up to 50) and a prerequisite (connected Amazon account). However, it does not mention when not to use this tool or explicitly point to alternative sibling tools, preventing a score of 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 must cover behavioral traits. It does not explicitly state that this is a read-only operation with no side effects, nor does it mention authentication or rate limits.

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-loaded with purpose and usage guidance. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless tool with no output schema, the description adequately covers what data is returned and when to use it.

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 description cannot add meaning beyond schema. Baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns remaining monthly credits and reset date. It is distinct from sibling tools which focus on other data (e.g., inventory, orders). However, phrasing is slightly awkward ('analyse and AI credits').

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 when user asks about usage limits or after a credit-limit error, providing clear context and triggering conditions.

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 (e.g., read-only, destructive, auth needs). The tool likely is safe and read-only, but this is not explicitly stated, leaving room for ambiguity.

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 wasted words. Efficient and clear.

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 or output schema, the description provides enough context: what it does and when to use it. Could mention the type of data returned (e.g., counts), but not essential 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?

There are no parameters, and schema coverage is 100% (empty). The description adds meaning by explaining the output is a summary of headline stats, which compensates for the lack of parameter details. Baseline for 0 params 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 it provides a 'dashboard summary' with 'headline stats across monitors and results', which is a specific verb+resource. It distinguishes itself from sibling tools (e.g., get_store_results, get_best_sellers) by being the high-level overview.

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 for a general 'how's it looking' overview before drilling into specific feeds', giving usage context. However, it does not mention when not to use or name specific alternatives, though the context implies using specific tools afterwards.

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; description states it is a 'search' (implying read-only) and 'free to call', but lacks details on rate limits, pagination, or auth requirements.

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, no fluff. Purpose is first, then usage guidance, then a key parameter detail. Well-structured 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 12 parameters, no output schema, and no annotations, the description covers the core idea and one parameter's options. Missing return format and several filter parameters, so moderately 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?

12 parameters with 0% schema coverage. Description adds meaning to 'sort_field' by listing valid values and hints at 'min_profit' and 'min_roi' via example, but many parameters like 'asin', 'limit', 'offset' remain undocumented.

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 searches Sorsa's A2A deal database with profit, ROI, sales rank, and pricing. It distinguishes from siblings like get_oa_deals and get_store_results by specifying A2A 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 'Call when the user asks for current deals' with a concrete example. Does not explicitly exclude alternatives, but the example is helpful.

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 convey behavioral traits. It implies a read-only list operation but does not explicitly state read-only behavior, destructive nature, or any side effects. Adequate for a simple list, but lacks full 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?

Two succinct sentences with no wasted words. The purpose and usage are front-loaded, making it efficient for an AI agent to parse.

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, the description covers the tool's core functionality and usage context. No missing information apparent for this simple list operation.

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 (schema coverage 100%), so the description does not need to add parameter details. Baseline score of 4 for zero-parameter tools.

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 A2A deal-monitor tasks, specifying the verb (List) and resource (saved filters generating notifications). It distinguishes from siblings like create_deal_task and other get_* tools by focusing on deal-monitor 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?

Explicitly advises calling before creating a deal task or when the user asks about monitors. This provides clear usage context, though it does not explicitly exclude other scenarios.

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 mentions requiring a connected Amazon account (closed beta) but lacks any behavioral traits like read-only, destructive actions, pagination, or rate limits.

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 very concise sentences, no wasted words, immediately front-loads what the tool does and when to use it.

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 no output schema, no annotations, and 0% schema coverage, the description is too sparse. It fails to explain what data is returned, pagination behavior, or search semantics, which are important for a tool with 3 parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds no meaning beyond parameter names. The 'search' parameter is not explained (e.g., what fields it filters on), leaving the agent without guidance on its behavior.

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 refunds issued to the user's customers, distinguishing it from sibling tools like get_fba_returns and get_fba_reimbursements by focusing on refunds specifically.

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 the agent to call when the user asks about refunds or money returned to buyers, providing clear context. However, it does not mention when not to use it or provide alternative tools, which would improve it further.

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 must fully convey behavioral traits. It correctly indicates the tool is a read operation for past reimbursements and mentions the closed beta requirement. However, it lacks details on pagination behavior, data recency, or any side effects, leaving gaps for the agent.

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-loading the purpose and then providing usage context and a requirement. Every sentence serves a clear purpose with no redundancy or 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 4 parameters with 0% schema coverage and no output schema, the description lacks essential details. It does not explain how to use parameters or what the return data looks like, making it incomplete for effective use despite clear overall purpose.

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 should compensate by explaining parameters. It does not mention page, limit, reason, or search at all. The agent must infer their meaning from names alone, which is insufficient 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 tool returns reimbursements paid to the user for lost/damaged stock, which is a specific and distinct purpose. However, it does not explicitly differentiate from sibling tools like get_fba_refunds or get_fba_returns, missing an opportunity to clarify when to use this tool versus alternatives.

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 usage cue: 'Call when the user asks what Amazon has paid them back' and notes the prerequisite of a connected Amazon account. However, it does not specify when NOT to use this tool or offer alternatives, such as using get_fba_refunds for refunds instead.

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. Description mentions a prerequisite (connected Amazon account) but does not disclose behavioral traits like read-only nature, data freshness, or pagination behavior. Leaves the agent uninformed about side effects or limitations.

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, reasonably concise. However, it sacrifices essential information for brevity; could include parameter hints without being verbose.

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, 4 undocumented parameters, and no behavioral details. Description omits return format, pagination mechanics, and how the disposition filter works, making it incomplete for reliable 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 has 4 parameters (page, limit, search, disposition) with zero description coverage. The description only mentions dispositions in general but does not explain any parameter, such as the disposition filter or search input. Fails 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?

Description clearly states the tool retrieves FBA customer returns with dispositions, specifying categories like sellable and damaged. The verb is implied from the name, and the resource is distinct from sibling tools like get_fba_refunds or 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?

Only says 'Call when the user asks about returns.' No guidance on when not to use it, such as for refunds or reimbursements, nor does it mention alternatives like get_fba_refunds. Minimal 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 are provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or error handling. It only hints at the return content (expected vs received) but lacks transparency beyond purpose.

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, front-loading the purpose and usage. No extraneous words, making it efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter and no output schema, the description provides sufficient context: what it returns (expected vs received quantities) and sequencing (after get_fba_shipments). It lacks explicit output fields but is reasonably 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?

With 0% schema description coverage, the description partially compensates by mentioning the shipment_id comes from get_fba_shipments. However, it does not explain the parameter format or any constraints, leaving some ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves line items inside an FBA shipment, specifically expected vs received quantities. It distinguishes from the sibling tool get_fba_shipments by specifying it is called after that to get item-level details.

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 to call this after get_fba_shipments when the user asks what was in a specific shipment or whether everything was received, providing clear context and usage guidance.

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

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

With no annotations, the description carries the full burden. It discloses the account requirement and implies aggregated data via 'summary stats,' but does not detail pagination, rate limits, or error handling.

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: purpose, usage context, prerequisite. No unnecessary words, 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?

Given the four optional parameters and no output schema or annotations, the description is too minimal. It lacks details on output format, pagination, error scenarios, and parameter usage, which are needed for complete understanding.

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 explain any of the four parameters (page, limit, search, status). It offers no guidance on their meaning or usage, leaving the agent to rely solely on 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 returns inbound FBA shipments with summary stats, and specifies when to call: when the user asks about shipments sent to Amazon. This distinguishes it from siblings like get_fba_shipment_items, which focus on individual shipment items.

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 explicitly says to call when the user asks about shipments sent to Amazon, and notes the prerequisite of a connected Amazon account in closed beta. However, it does not mention when not to use it or explicitly 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?

Discloses a cost of 1 analyse credit with a 24-hour cache, which is useful behavioral context. However, with no annotations provided, the description carries the full burden; it omits other behavioral traits like error handling or rate limits for non-cached cases.

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 no redundancy. First sentence states the purpose succinctly. Second sentence provides usage guidance and cost information. Every word 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?

The description is incomplete for a 2-parameter tool with no output schema. It does not explain the parameters, the return format, or edge cases. While it gives usage context, the missing parameter details and output description leave 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?

Schema description coverage is 0%, yet the description adds no information about the two parameters ('asin' and 'domain'). It does not explain that 'asin' is required or that 'domain' defaults to 'GB'. The description fails to compensate for the lack of 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 it retrieves prices for an ASIN across international Amazon marketplaces. It uses a specific verb ('get') and resource ('prices'), and the context of cross-market price differences distinguishes it from other get_* tools, though sibling differentiation is not explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to call: 'when the user asks about cross-market price differences or EU arbitrage opportunities'. Does not provide negative usage or alternatives, but the provided context is sufficient for typical AI agent decision-making.

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 states the tool returns purchased items with listing status but does not disclose read-only nature, authentication needs, rate limits, or data freshness.

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 describes what is returned, second specifies when to use. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no output schema, the description covers purpose and usage context adequately. It mentions output includes SKU and listing errors, though a full output specification is missing.

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 the description does not need to add parameter details. The baseline for no parameters is 4, and the description does not detract.

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 items purchased from the user's sourcing list, including listing status. This distinguishes it from siblings like get_items_sourced, providing a specific verb-resource combination.

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 to call when the user asks what they've bought or about listing progress, providing clear usage context. However, it does not 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 describes the output content but does not explicitly state read-only behavior or lack of side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the tool's purpose, followed by 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?

For a simple no-parameter retrieval tool, the description adequately covers what it returns (sourcing list with projections) and when to use it.

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 adds no parameter details, but this is acceptable given 0 parameters and baseline 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 it retrieves the user's sourcing list with cost/profit projections and status, distinguishing it from sibling tools like 'add_to_sourcing_list' and '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?

The description explicitly says to call when the user asks about their buy list or sourcing pipeline, providing 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.

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

With no annotations, the description carries full burden. It mentions the requirement of a connected Amazon account and lists data returned. However, it does not disclose potential limitations like caching, rate limits, or whether all SKUs are included.

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 focused sentences plus a format note. Informative without redundancy, though the format note could be integrated more smoothly.

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?

Adequate for a simple tool with two parameters. Covers main purpose and usage, but missing details on behavioral aspects and the second parameter. No output schema increases need for completeness.

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 compensate. It specifies the month format 'YYYY-MM' but does not mention the vat_registered parameter at all, leaving its purpose and usage unclear.

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 per-SKU profit & loss for a calendar month, listing specific metrics like revenue, fees, COGS, etc. It includes example queries and distinguishes from siblings by focusing on monthly financial reporting.

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 triggers ('what was my profit in <month>?') and format for the month parameter. Lacks explicit alternatives or when-not-to-use guidance, but the context and sibling list make the use case 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 must convey behavioral traits. It implies read-only retrieval but does not explicitly state no side effects, required permissions, rate limits, or behavior when no notifications exist. The description is insufficient to fully understand tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with the purpose front-loaded. The second sentence could be more structured but is efficient and contains no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple query tool with one non-required parameter and no output schema, the description explains what is returned (notifications grouped by task) and the filter option. However, it lacks details on response format or limits, leaving minor 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?

The single parameter show_checked is fully explained in the description with its allowed values and default, adding meaning beyond the schema's title and default value. Despite 0% schema coverage, the description compensates well.

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 price-alert notifications from A2A monitor tasks, grouped by task. This specific verb+resource combination distinguishes it from sibling tools like get_price_monitors and mark_notification.

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 what alerts or notifications they have,' providing clear usage context. However, it does not mention when not to use it or compare to alternative notification-related tools among siblings.

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 disclose behavior. It fails to mention pagination (page, per_page), result limits, read-only nature, or any side effects. The description is too brief for a tool with 9 parameters and no output schema.

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 short sentences plus a list of sort options. It is front-loaded, begins with the core purpose, and contains no redundant text. Every sentence 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 the tool's complexity (9 parameters, no output schema, 0% schema description coverage), the description is inadequate. It does not cover filtering, pagination, or response structure, leaving significant gaps for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description lists possible values for the 'sort' parameter, which adds value beyond the schema. However, with 0% schema description coverage and 8 other parameters (page, search, min_roi, etc.) completely unmentioned, the description does not compensate sufficiently. Most parameters remain unexplained.

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 profitable OA deals, retail products matched to Amazon listings with profit and ROI. It specifies the context ('OA or retail sourcing opportunities') and lists sort options, effectively distinguishing it from siblings like get_oa_retailers.

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: 'Call when the user asks about OA or retail sourcing opportunities.' This provides clear when-to-use context. However, it does not mention when not to use or suggest alternatives, which would improve the score.

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

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

No annotations exist. Description correctly implies a read-only list operation without side effects. Adequate for a simple 0-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, front-loaded with the action, 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?

For a simple 0-parameter tool with no output schema, the description is fully complete: states what it does and why it's useful.

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 value by explaining the tool's purpose in relation to get_oa_deals, 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?

Description clearly states it lists retailers available in OA deals feed and its purpose as a helper for get_oa_deals, distinguishing it from siblings 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?

Explicitly says to call to know valid filters for get_oa_deals, providing clear context. Does not give negative guidance but enough for the simple use case.

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. The description implies a read operation by using 'list', but does not mention pagination, rate limits, or whether it returns all monitors. It is adequately transparent for a simple listing but lacks explicit behavioral traits.

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 no unnecessary words. The first sentence states purpose concisely, and the second provides usage guidance. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple zero-parameter tool with no output schema, the description is fairly complete: it tells what it does and when to call. However, it does not explicitly describe the return format (e.g., list of monitors with fields like ASIN, target price). The agent can infer the output from context, but a bit more detail would be ideal.

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 zero parameters, and schema coverage is 100%, so the baseline is 4 per instructions. The description adds meaning by explaining what the tool lists (ASIN price monitors) but does not need to describe parameters since none exist.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists the user's ASIN price monitors, specifying both the resource and action. It also provides concrete use cases (avoid duplicates, user query) that distinguish 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 to avoid duplicates and when the user asks what they're watching. This provides clear when-to-use and when-not-to-use guidance, and implicitly distinguishes from other list tools by specifying it's for price monitors.

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 implies a read-only operation (getting performance data) and states a required connection, but does not explicitly confirm read-only status, rate limits, or other behaviors.

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 fluff. The first sentence lists what the tool returns, the second provides usage rationale and constraints. 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?

For a simple tool with one parameter and no output schema, the description covers the purpose, parameter range, and the metrics returned. It could mention pagination or ordering, but the information provided is sufficient for an agent to understand the tool's output.

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 single parameter 'days' has a default of 30 in the schema, but the description adds the valid range (7-365) and explains it as the time period for the data. This adds significant meaning beyond the schema, which has 0% coverage.

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 provides per-SKU profitability metrics over a time period, listing specific fields (units, revenue, profit, ROI, return rate, price vs break-even, selling-at-a-loss flag). This distinguishes it from siblings like calculate_profit or get_best_sellers.

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 tells when to use the tool ('which products make or lose me money?' or 'what's selling below break-even?') and notes the prerequisite of a connected Amazon account. It does not provide explicit when-not guidance, 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?

Discloses credit cost and caching condition (24-hour credit exemption), but no annotations exist. Does not mention rate limits, authentication, or error handling, which are important for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and usage, no superfluous words. Ideal conciseness.

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?

Adequate for a simple read tool, covering purpose, usage, and cost. However, without parameter descriptions or output schema, the agent may not fully understand return format or parameter constraints.

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 description only mentions 'Amazon ASIN' but fails to explain the 'domain' parameter, which determines the marketplace. Agent may not know to specify a non-default domain.

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 current sellers and stock levels for an ASIN. It is specific but does not explicitly differentiate from siblings like get_product_performance, though the function is distinct.

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 lists three use cases (who is selling, number of sellers, competitor stock) and mentions credit cost with condition. Lacks explicit 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, so description is the sole source. It implies read-only but lacks details on authentication, rate limits, or data retrieval behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no fluff, purpose and timeframe are front-loaded. Efficient for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so agent lacks info on return format. With many sibling tools, missing details on pagination or ordering, making it incomplete for a list operation.

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 no descriptions (0% coverage). Description adds the valid timeframe values (all, day, week, month, year) but does not explain behavior differences between them.

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 purchase history and mentions timeframe options. It is distinct from siblings like get_purchase_stats but does not explicitly differentiate.

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?

Tells when to call (user asking about recent purchases) but does not provide when-not-to-use or alternatives among siblings.

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

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

With no annotations provided, the description bears full responsibility for disclosing behavior. It only states what statistics are returned, but omits critical details such as data scope (e.g., time range, user context) and whether the operation is read-only. This lack of transparency is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single sentence that front-loads the key information: the resource type and available breakdowns. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains the return values fairly well (fields and breakdowns). However, it could be more complete by specifying the default time period or user scope. Still, it covers the main components needed for an agent to use the tool 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?

The input schema has no parameters, so there is no parameter semantics to add. According to the guidelines, 0 parameters yields a baseline of 4. The description appropriately focuses on the output instead.

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 provides purchase statistics including total spent, projected profit, items bought, and ROI, with breakdowns by time period and category/source. It effectively conveys the resource and its scope, but does not explicitly differentiate from siblings like get_purchases or get_monthly_pnl.

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 includes an example usage scenario ('how much have I spent this month?'), providing clear context for when to call the tool. However, it does not mention when not to use it or suggest 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?

With no annotations provided, the description must fully convey behavioral traits. It discloses that the tool is a read operation (listing claims) and mentions the prerequisite. However, it does not describe pagination behavior (despite page/limit parameters), output structure, rate limits, or data freshness, leaving some 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?

The description is concise, consisting of three sentences that front-load the core purpose, then provide usage ordering and parameter hints. No extraneous information is included; 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 the tool has 4 parameters, no output schema, and no annotations, the description covers the essential purpose, when to call, and a prerequisite. However, it lacks details on claim_type values, pagination behavior, output format, and error handling, which would be beneficial for a complete understanding.

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 0% coverage (no parameter descriptions). The description adds meaning for the 'status' parameter by listing possible values: 'open, dismissed, filed or all.' However, it does not explain the 'claim_type' parameter or provide details for 'page' and 'limit' beyond their schema defaults. Thus, it partially compensates but not fully.

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 'Individual reclaim opportunities with evidence — what to claim from Amazon and why.' It uses a specific verb ('get') and resource ('reconciliation claims'), and distinguishes itself from the sibling tool 'get_reconciliation_summary' by indicating it lists actual claims rather than a 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?

The description explicitly advises calling this tool 'after get_reconciliation_summary to list the actual claims,' providing clear sequencing guidance. It also mentions the prerequisite 'Requires a connected Amazon account (closed beta)' and lists valid status values, though it does not explicitly state when not to use it or compare to other similar 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, so the description must fully convey behavioral traits. It states it returns totals and requires a connected account, but does not disclose that it is read-only, non-destructive, or any other safety-related behavior. The implied read-only nature is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences, front-loaded with the primary purpose, followed by a use-case and a requirement. 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 and no annotations, the description explains the return content (totals by claim type) and context, but lacks explicit output structure, differentiation from sibling tools like get_reconciliation_claims, and details about data freshness or limitations.

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, and the description adds context by explaining the output and usage. Per rubric, 0 parameters baseline is 4, and the description provides additional meaning 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?

The description clearly states that the tool returns totals of reclaimable money broken down by claim type, with a specific use-case phrase 'how much does Amazon owe me?'. It distinguishes from sibling tools like get_reconciliation_claims by being a 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?

The description provides a clear use-case ('Call for 'how much does Amazon owe me?'') and a prerequisite ('Requires a connected Amazon account (closed beta)'). However, it does not explicitly mention when not to use this tool or suggest alternatives like get_reconciliation_claims for individual claims.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool computes per-SKU sales velocity against stock, calculates days of cover, and outputs a suggested reorder quantity and potential profit. It also explains the parameters lead_time_days and cover_days. The behavior is clearly presented as a read-only analysis, with no contradictions.

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 at 4 sentences, with the core purpose stated first. Each sentence adds essential information: purpose, output, usage context, parameter definitions, and prerequisite. There is no redundancy or extraneous text.

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 only 2 optional parameters and no output schema, the description covers the input semantics, output (suggested quantity, profit, cover days), and a prerequisite (Amazon account). It does not specify the exact return format (e.g., JSON object vs. list), but the level of detail is sufficient for an agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage (no descriptions for parameters), but the description adds complete semantic meaning: 'lead_time_days = how long a resupply takes to land; cover_days = days of stock the reorder should provide.' This fully compensates for the schema gaps and goes beyond the default values provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to suggest what to reorder now based on sales velocity and stock on hand. It specifies the resource (restock suggestions) and directly addresses common user queries like 'what should I restock?' or 'what's running low?', distinguishing it from sibling tools like 'get_aged_stock' or 'get_amazon_inventory' which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool (e.g., 'what should I restock?') and includes a prerequisite ('Requires a connected Amazon account'). However, it does not explicitly mention when not to use it or compare it to alternative tools, missing an opportunity for further clarity.

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 of behavioral disclosure. It describes the tool as listing recent jobs and status, but does not mention any side effects, authentication requirements, rate limits, or what 'recent' means. For a safe read-only tool, this 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 concise, consisting of one clear sentence and a short fragment. It is front-loaded with the main verb 'List' and the object. Every part adds value, though it could be slightly more 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's simplicity (no parameters, no output schema), the description adequately covers what the tool does and why to use it. It lacks details like ordering or limits, but the completeness is sufficient for a basic 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 has no parameters (0 parameters, 100% coverage). With zero parameters, the description adds meaning by specifying that the list is 'recent' and includes 'status', and clarifies the purpose. Baseline 4 is appropriate as the description compensates for the lack of 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 it lists recent Reverse Source scan jobs with status, and explicitly mentions the purpose is to find a job_id for fetching results. This distinguishes it from siblings like get_reverse_source_status (which likely gets status of a specific job) and start_reverse_source_scan (which starts a scan).

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 use case ('Call to find a job_id whose results to fetch'), implying when to use this tool. However, it does not explicitly state when not to use it or mention alternatives, but the context is sufficient for a simple list 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?

Discloses that it returns progress percentage during the scan and final results upon completion. No annotations provided, so description bears full burden; it could mention error handling or polling behavior but is adequate for a status check 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 sentences, front-loaded with purpose, no redundant information. 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 single parameter, no output schema, and no annotations, the description sufficiently covers the tool's purpose, input, and output. Nothing critical missing for its complexity.

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 parameter (job_id). Schema description coverage is 0%. The description implies job_id identifies a Reverse Source scan but does not explicitly define its origin or format. Baseline 3 due to single param, but minor improvement needed.

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 status and results of a Reverse Source scan, specifying both progress percentage and final cheapest source per ASIN. It clearly distinguishes from start_reverse_source_scan which initiates the scan.

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 links to start_reverse_source_scan as the prerequisite, implying this tool is used after starting a scan. Does not explicitly state when not to use, 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, but the description accurately reflects a read-only list operation. It does not contradict any annotations and is transparent about its purpose, though it could mention that no data is modified.

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 short sentences, front-loaded with key information ('List the Amazon storefronts'), no extraneous 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?

The description is sufficient for a simple list tool with no parameters and no output schema. It could be slightly more explicit about the output being a list, but it is generally 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?

The input schema has zero parameters, so the description does not need to explain them. The baseline for no parameters is 4, and the description adds no redundant information.

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 'List', the resource 'Amazon storefronts (competitor sellers)', and the scope 'the user is monitoring'. This distinguishes it from sibling tools like add_storefront_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 this tool 'before adding one, or when the user asks which storefronts they track', providing clear context and 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 must disclose behavioral traits. It states the tool returns summary stats, implying a read-only operation, but does not explicitly mention that it is non-destructive, requires authentication, or any rate limits. It is adequate but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two sentences. It front-loads the purpose and adds value with the sibling differentiation. 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 the tool has no parameters and no output schema, the description explains its overview purpose and relationship to a sibling. It mentions the type of data returned (stores tracked, result counts), which provides sufficient context for a simple stats tool. Could be slightly more detailed but is largely 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?

The tool has zero parameters, so the description does not need to explain parameter semantics. Baseline is 4 per the scoring guide.

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 summary stats for storefront monitors (stores tracked, result counts). It also explicitly distinguishes itself from the sibling tool 'get_store_results' by suggesting to call this for an overview before drilling into results.

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 guidance: 'Call for an overview before drilling into get_store_results.' This tells the agent when to use this tool and when to use the alternative.

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 for behavioral disclosure. It does not mention permissions, data volume, pagination, or what happens with missing results (e.g., no monitors or no products). For a read tool, this lack of detail reduces 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 with no wasted words. The first sentence states the tool's function, the second provides usage guidance and parameter info. 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 the tool is simple (one optional parameter, no output schema), the description covers the core purpose, usage, and parameter role. It could be more complete by hinting at return structure (e.g., product ASINs, names) but is adequate for its complexity.

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 0% description coverage, so the description must add meaning. It states the parameter is an optional filter by store_id, which provides basic semantics but does not explain format, default behavior (e.g., returning all results when null), or constraints like required format of store_id.

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 products found by storefront monitors, distinguishing it from sibling tools like get_storefront_monitors (which returns monitors themselves). It specifies the verb (get) and resource (products from monitors), and mentions optional filtering by store_id.

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 their storefront monitors have found', providing clear usage guidance. It doesn't explicitly exclude when not to use or name alternatives, but the context is strong enough to infer when this tool is appropriate versus siblings.

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 tool lists recent jobs and status, but does not specify limits, what 'recent' means, or authentication needs. The behavior is simple, so a 3 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?

The description is very concise with two short sentences, front-loaded with the main action. 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 no annotations, the description explains the purpose and usage adequately. It lacks details on response format or pagination, but for a simple list 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 (0 params, schema coverage 100%). The description adds no param details but the baseline for no params is 4, and no additional information is needed.

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 'List the user's recent Wholesale Scanner jobs and their status' and explains the purpose is to find a job_id. It distinguishes from siblings like get_wholesale_status and start_wholesale_scan.

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 usage guidance: 'Call to find a job_id whose results to fetch.' It implies a sequential workflow but does not explicitly state when not to use it 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?

With no annotations, the description carries the full burden. It discloses the two-phase output (progress and final results) but does not mention prerequisites, error handling (e.g., invalid job_id), or authentication needs. 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?

The description is a single sentence, very concise and front-loaded. However, it could benefit from a structured format (e.g., bullet points) for clarity, but it avoids 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 the tool's simplicity (one parameter, no output schema), the description covers the main functionality. Missing details like error states or lifecycle (e.g., only valid after starting a scan) reduce completeness.

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 'job_id' with no description (0% coverage). The description does not explain what job_id represents or how to obtain it, failing to compensate for the schema 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 tool returns status and results of a Wholesale Scanner run, specifying progress percentage and final product details. It distinguishes from sibling tools like 'get_wholesale_jobs' (list jobs) and 'start_wholesale_scan' (initiate scan).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage after starting a scan but does not explicitly state when to use or not use it, nor provide alternatives. The context suggests it's for checking progress, but no explicit 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 are provided, and the description does not disclose behavioral traits such as read-only nature, rate limits, or side effects. With no annotations, the description carries full burden, and it fails to clarify that the tool performs a read 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?

The description is two sentences, perfectly concise, with key information front-loaded. Every sentence adds value, and there is 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?

For a simple lookup tool with two parameters and no output schema, the description covers purpose and usage well. However, it lacks details about the 'domain' parameter and whether the return is a single ASIN or list, slightly reducing completeness.

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 description only specifies that the barcode should be 8-14 digits for the 'code' parameter, but does not mention or explain the 'domain' parameter. Since schema coverage is 0%, the description should compensate, but it provides minimal parameter guidance.

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 resolves a barcode to an Amazon ASIN, specifying the input format (EAN/UPC, 8-14 digits). It distinguishes from siblings like analyse_product and search_by_title by its unique function.

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 tells when to use the tool ('Call when the user gives a barcode instead of an ASIN') and what to do with the output ('feed the returned ASIN into analyse_product'), providing clear context and a next step.

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

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

With no annotations, the description carries the burden. It discloses the two states (checked/unchecked) but does not mention idempotency, error handling (e.g., missing notification), or any authentication requirements. Basic but not fully transparent.

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 zero wasted words. Front-loaded with the action and immediate context. 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 simple two-parameter tool with no output schema, the description covers the core behavior adequately. It could mention return value or side effects, but for a toggle action it is sufficient. Slightly incomplete on error conditions.

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% meaning no parameter descriptions in schema. The description only indirectly explains 'checked' by mentioning 'checked or unchecked', but 'notification_id' is not explained. The description adds minimal meaning beyond what the schema provides.

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 notifications as checked or unchecked, with a specific verb ('mark') and resource ('notifications'). It distinguishes from sibling tools like 'get_notifications' by indicating it modifies state.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says to call after user deals with an alert or to un-mark, providing clear context. However, it does not explicitly mention when not to use or any alternatives, though the context is sufficient.

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 the description carries full responsibility. It discloses the tool uses Keepa, returns top matches with thumbnails, and limits results to 1-10. It does not cover rate limits, error handling, or data freshness, but adequately describes the core behavior for a search 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?

The description is three well-structured sentences. The first states the core function, the second gives usage guidance, and the third adds parameter detail. Every sentence is valuable 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?

Given the tool's simplicity (3 parameters, no output schema), the description covers the usage scenario, parameter constraints, and result format (ASINs with thumbnails). It is complete enough for an agent to use correctly, though it lacks explicit mention of domain valid values or error cases.

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 meaning by stating 'limit is 1-10' which clarifies the range for the limit parameter. However, it does not explain the domain parameter (likely Amazon marketplace country code) or add detail about the title parameter beyond the obvious. This provides partial but incomplete parameter 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?

The description clearly states the tool searches Amazon by product title via Keepa and returns top matching ASINs with thumbnails. This distinguishes it from siblings like lookup_by_ean which searches by barcode, and analyse_product which analyzes existing ASINs.

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: 'Call when the user names a product without an ASIN or barcode; present the candidates and let them pick before analysing.' This tells the agent when to use the tool and outlines the workflow, implying alternatives exist when ASIN or barcode is known.

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?

Discloses key behaviors: uses shared Keepa scan key, counts against daily scan credits, returns a job_id for async polling, and is a premium feature. No annotations provided, so description carries full burden.

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?

Six sentences, no fluff, front-loaded with purpose. Could be slightly more structured but 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?

Covers all key aspects: purpose, parameters, limits, credit usage, async nature, polling. Lacks details on output format but no output schema exists.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond schema: explains asins are limited to 100, countries are optional with examples and default. Schema has 0% description coverage, so this is valuable.

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 starts an A2A Finder scan for profitable Amazon-to-Amazon flips, with specific details on ASINs, marketplaces, and optional country filter. It distinguishes from siblings like start_reverse_source_scan.

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 use (checking a list of ASINs for flips) and mentions credit usage and polling. Does not explicitly exclude alternatives like analyse_product for single ASIN 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?

No annotations provided, so the description must cover behavioral traits. It discloses that it uses a shared key, consumes credits, returns a job_id asynchronously, and is a premium feature. It does not mention error handling or concurrency limits, but covers the key behaviors.

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 (3 sentences), front-loaded with the main purpose, and includes all essential details without extraneous information. 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?

No output schema, but the description specifies the return value (job_id) and how to obtain results via polling. It also notes the limit and premium feature. While it omits some details like concurrency or error recovery, it is fairly complete for an async scan 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?

There is one parameter (asins) with 0% schema description coverage. The description adds the constraint 'Up to 250 ASINs', which provides important context beyond the schema, but does not specify format or validation rules. This is adequate but not comprehensive.

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 specifies a clear verb (start) and resource (Reverse Source scan), and explains the outcome: searching Google Shopping for cheaper sources to flip. It distinguishes itself from sibling start_* tools by describing the unique nature of the scan.

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: uses shared Keepa key, counts against daily credits, returns a job_id for polling, and limits input to 250 ASINs. It does not explicitly state when not to use or mention alternatives, but the polling method is indicated.

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 process (matches to Amazon), return value (job_id), limit (500 items), and premium nature. It does not cover error handling or rate limits, but core behavior is well-described.

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 five sentences, no fluff, and key information is front-loaded (purpose, then details, then next step).

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 output schema and one parameter, the description explains the parameter structure, behavior, return value, and next step. It could add more about error handling or prerequisites, but is reasonably 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?

The schema has no parameter description (coverage 0%), but the description fully explains the expected structure of each product dict (title, price, ean) and the maximum number of items. This is essential for correct 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 starts a Wholesale Scanner run against a supplier price list, matches products to Amazon, returns profit/ROI, and returns a job_id. This distinguishes it from sibling tools like get_wholesale_status which polls results.

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 explains when to use this tool (to start a scan), includes the format and limit of products, and mentions polling with get_wholesale_status. It could explicitly state 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.