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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (checks authentication status), what it returns (status, URL, error messages), and its purpose in a workflow (verification before other tools). However, it lacks details on potential side effects, rate limits, or specific error handling beyond mentioning 'error messages'.

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 front-loaded with the core purpose in the first sentence, followed by usage guidelines. Both sentences are essential—the first explains what the tool does, and the second explains when to use it—with zero wasted words or redundancy.

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

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

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and output details. However, it could be more complete by specifying the exact format of the return values (e.g., structured data types) or any prerequisites, though this is mitigated by the simple context.

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

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

Schema description coverage is 100%, so the schema fully documents the single parameter 'region' with its enum values. The description adds no additional parameter semantics beyond what the schema provides, such as explaining why region matters for authentication or how it affects the check. This meets the baseline for high schema 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 the specific action ('Check if the browser session is authenticated'), the resource ('Amazon for a specific region'), and the output ('authentication status, current URL, and any error messages'). It distinguishes this tool from its siblings, which are all data export or retrieval tools, by focusing on authentication verification rather than data extraction.

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

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

The description explicitly states when to use this tool ('to verify login status before running other tools') and when not to use it ('or to prompt user to log in if session expired'), providing clear context for its application. It implies alternatives by suggesting it should precede other tools, though it doesn't name specific 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 provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (export to CSV) and lists the CSV columns, which helps understand the output format. However, it doesn't mention authentication requirements, rate limits, whether it's a read-only operation, or potential side effects like file system changes. The description adds some value but leaves important behavioral aspects unspecified.

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 perfectly concise with two sentences that each serve distinct purposes: the first states the core functionality and output format, the second provides usage context. Every word earns its place with no redundancy or unnecessary elaboration.

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 moderate complexity (export operation with 3 parameters), no annotations, and no output schema, the description does a good job covering the essentials: what it does, what the output contains, and when to use it. However, it doesn't address authentication requirements or potential limitations (like pagination behavior implied by max_pages parameter), which would be helpful given the lack of annotations.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, but with complete schema coverage and only 3 parameters, this is acceptable. The baseline would be 3, but the description's clarity about the overall purpose and output format elevates it slightly.

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

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

The description clearly states the specific action ('Export Amazon gift card transaction history to CSV file') and distinguishes it from siblings by focusing on gift cards rather than items, orders, shipments, or general transactions. It explicitly mentions the resource (Amazon gift cards) and output format (CSV).

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

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

The description provides clear context about when to use this tool ('Useful for tracking gift card usage and reconciling balances'), which differentiates it from siblings like get_amazon_gift_card_transactions (which likely returns data without exporting). However, it doesn't explicitly state when NOT to use it or name specific alternatives for different 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?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it visits each order's invoice page, extracts item-level data, provides performance estimate (~2s/order), and lists all CSV columns. It doesn't mention authentication requirements, rate limits, or error handling, but covers the core operation thoroughly.

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 efficiently structured with two sentences: first states the core operation with performance context, second lists CSV columns and use cases. Every element adds value without redundancy, and it's appropriately front-loaded with the most important information.

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

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

For a tool with no annotations and no output schema, the description provides substantial context: operation method, performance characteristics, output format details, and use cases. It could benefit from mentioning authentication requirements (implied by sibling check_amazon_auth_status) and error scenarios, but covers the essential behavioral aspects well.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add meaningful parameter semantics beyond what's in the schema - it mentions CSV columns but doesn't explain how parameters like region or max_orders affect the extraction process. Baseline 3 is appropriate when schema does the heavy lifting.

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 exports Amazon order items to CSV with specific details about data extraction from invoice pages (~2s/order). It distinguishes from sibling tools like export_amazon_orders_csv by focusing on item-level data rather than order-level summaries.

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

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

The description provides clear context for when to use this tool ('ideal for expense tracking, inventory analysis, or accounting exports') and mentions the performance characteristic (~2s/order). However, it doesn't explicitly contrast with alternatives like export_amazon_orders_csv or get_amazon_order_details, which could help users choose between similar 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 carries the full burden and does well by disclosing performance traits ('~0.5s per 10 orders'), output details (CSV columns and default save location), and operational constraints (batching advice for large accounts to avoid timeouts). It doesn't mention authentication requirements or error handling, 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 front-loaded with the core purpose, followed by performance details, output specifics, and usage advice in three efficient sentences. Every sentence adds value without redundancy, making it appropriately sized and well-structured.

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

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

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is largely complete: it covers purpose, performance, output format, defaults, and scaling advice. However, it lacks details on authentication, error cases, or what happens on failure, which would be helpful for a tool with no annotations.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by explaining the default filename pattern and batching recommendation for max_orders, but doesn't provide additional semantic context beyond what's in the schema descriptions.

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

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

The description clearly states the specific action ('Export Amazon orders summary to CSV file') and resource ('Amazon orders'), distinguishing it from siblings like export_amazon_items_csv or export_amazon_transactions_csv by focusing on orders summary rather than items, transactions, or other data types.

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

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

The description provides clear context for when to use this tool ('Fast extraction from order list page') and includes an explicit alternative for large accounts ('use max_orders to batch exports and avoid timeouts'), but it doesn't explicitly state when NOT to use it versus other sibling tools like get_amazon_orders or export_amazon_items_csv.

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 full burden and does well by disclosing key behavioral traits: it reveals the operation visits each order's detail page (~4s/order), mentions the optional tracking number extraction adds ~2s per shipment, and describes the CSV output format. However, it doesn't cover error handling, authentication requirements, 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?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, subsequent sentences add important behavioral context (processing time, CSV columns), and the final sentence provides usage context. Every sentence earns its place with zero 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?

Given the tool's complexity (7 parameters, no annotations, no output schema), the description provides good context about the operation's behavior, timing, and output format. However, it doesn't explain what happens on failure, whether authentication is required, or provide examples of the CSV output. For a tool with no annotations or output schema, it could be more complete about 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 description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions CSV columns which relate to output, not input parameters. Baseline 3 is appropriate when schema does the heavy lifting.

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 with specific verbs ('export', 'extract') and resources ('Amazon shipment tracking data', 'CSV file'), and distinguishes it from siblings by focusing on shipment tracking rather than orders, transactions, or gift cards. It specifies the data source (order detail pages) and processing characteristics.

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

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

The description provides clear context for when to use this tool ('useful for tracking deliveries and reconciling shipments'), but doesn't explicitly state when not to use it or name alternatives among sibling tools. It implies usage for CSV export of shipment data but lacks explicit comparison with other export 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 carries the full burden of behavioral disclosure. It explains the extraction method ('Extracts transaction data from each order's detail page'), which adds useful context beyond the basic 'export' action. However, it lacks details on permissions needed, rate limits, error handling, or whether the operation is idempotent, leaving gaps for a mutation-like tool (exporting files).

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 front-loaded with the core purpose, followed by implementation details and a clear alternative. Both sentences earn their place: the first defines the tool and its output format, the second provides critical usage guidance. No wasted words or redundancy.

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

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

Given the tool's complexity (6 parameters, file export operation) and no output schema, the description does well by specifying the CSV columns and extraction method. However, it could better address behavioral aspects like file overwriting or error scenarios. With no annotations, it's mostly complete but has minor gaps in operational transparency.

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 description coverage is 100%, so all parameters are documented in the schema itself. The description does not add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain the relationship between 'year' and 'start_date/end_date' or clarify 'max_orders' behavior). Baseline 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the specific action ('Export Amazon payment transactions to CSV file') and resource ('transaction data from each order's detail page'), distinguishing it from sibling tools like 'export_amazon_orders_csv' or 'export_amazon_items_csv' by focusing specifically on payment transactions. It provides concrete details about the CSV columns, making the purpose unambiguous.

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

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

The description explicitly provides when to use this tool vs. alternatives by stating 'For faster bulk transaction export, consider get_amazon_transactions which scrapes the dedicated transactions page.' This gives clear guidance on choosing between detailed extraction from order pages vs. faster bulk export, addressing a key decision point for users.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns current balance, timestamp, and paginated transaction history with specific fields, and supports multi-page fetching. However, it does not mention authentication needs, rate limits, or error handling, leaving some gaps for 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 front-loaded with the core purpose in the first sentence, followed by detailed return values and pagination support. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 annotations and no output schema, the description compensates well by detailing return values (balance, timestamp, transaction fields) and pagination behavior. It covers the tool's functionality adequately for a read operation, though it could improve by mentioning authentication or error scenarios for full 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 description coverage is 100%, so the schema fully documents parameters like 'region', 'max_pages', and 'fetch_all_pages'. The description adds no additional parameter semantics beyond what the schema provides, such as explaining region implications or pagination details. Baseline 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the specific action ('Get current Amazon gift card balance and transaction history') and distinguishes it from siblings like 'get_amazon_gift_card_transactions' by emphasizing it returns both balance and paginated history. It explicitly names the resource (Amazon gift card) and verb (get), making the purpose unambiguous.

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

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

The description implies usage for retrieving gift card data but does not explicitly state when to use this tool versus alternatives like 'get_amazon_gift_card_transactions' or 'export_amazon_gift_cards_csv'. It mentions pagination support, which hints at context for large datasets, but lacks clear guidance on prerequisites or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns detailed transaction data (listing specific fields like date, amount, order ID), supports pagination, and implies it fetches historical data. However, it lacks information on authentication requirements, rate limits, error handling, or whether it's a read-only operation, which are important for a tool accessing sensitive transaction 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 front-loaded with the core purpose in the first sentence, followed by specific return details and pagination support. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

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

Given the complexity of a transaction history tool with no annotations and no output schema, the description does well by specifying return values (balance, count, detailed fields) and pagination behavior. However, it lacks details on authentication, error cases, or data freshness, which are important for completeness in this context.

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

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

Schema description coverage is 100%, with both parameters ('region' and 'max_pages') well-documented in the schema (including enum values and default). The description adds no additional parameter semantics beyond what the schema provides, such as explaining region implications or pagination details. Baseline 3 is appropriate since the schema handles the heavy lifting.

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

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

The description clearly states the specific action ('Get Amazon gift card transaction history with full details') and resource ('Amazon gift card'), distinguishing it from siblings like 'get_amazon_gift_card_balance' (which only retrieves balance) and 'export_amazon_transactions_csv' (which exports to CSV). It explicitly mentions the scope ('full details' and 'complete history'), making the purpose unambiguous.

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

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

The description provides clear context by specifying it retrieves 'transaction history with full details' and 'supports pagination for complete history,' which helps differentiate it from tools like 'get_amazon_gift_card_balance' (balance-only) or export tools. However, it does not explicitly state when not to use this tool or name specific alternatives, such as for summary vs. detailed data.

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 return data structure comprehensively but lacks behavioral details like authentication requirements, rate limits, error conditions, or performance implications (e.g., latency for certain parameters). The mention of 'Adds ~2s per shipment' for 'fetch_tracking_numbers' is a positive behavioral hint, but more is needed for a higher score.

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

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

The description is front-loaded with the core purpose, followed by a structured list of returned data. Every sentence adds value: the first states the action, and the second elaborates on the comprehensive return data. No wasted words or redundancy.

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

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

Given no annotations and no output schema, the description does well to detail the return data structure, but it lacks context on authentication, errors, or performance limits. For a tool with 5 parameters and no structured safety hints, more behavioral context would improve 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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain parameter interactions or provide examples). Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get comprehensive details'), resource ('for a specific Amazon order by order ID'), and scope ('Returns full order data including...'). It distinguishes this tool from sibling tools like 'get_amazon_orders' (which likely lists orders) and 'get_amazon_transactions' (which focuses on transactions).

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

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

The description implies usage for retrieving detailed order information when an order ID is known, but does not explicitly state when to use this tool versus alternatives like 'get_amazon_orders' (for listing orders) or 'export_amazon_orders_csv' (for bulk export). No explicit exclusions or prerequisites are mentioned.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it specifies the return data structure (order summaries with listed fields), mentions optional detailed item data and shipment tracking, and notes performance implications ('Adds ~2s per order' for include_items and include_shipments). However, it does not cover aspects like authentication requirements, rate limits, or error handling, which are relevant for a tool fetching sensitive order 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by specifics on returns and usage. Every sentence earns its place by adding value (e.g., listing return fields, explaining optional features, and stating use cases), with no redundant or vague language.

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 (8 parameters, no output schema, no annotations), the description is largely complete: it covers purpose, return data, optional features, and usage context. However, it lacks details on authentication (implied by sibling check_amazon_auth_status but not stated), error scenarios, or pagination/limits beyond max_orders, leaving some gaps for a tool handling sensitive order data.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it mentions 'date range or year' which aligns with start_date/end_date/year parameters, and implies include_items and include_shipments through 'Optionally includes detailed item data... and shipment tracking.' Since the schema does the heavy lifting, the baseline score of 3 is appropriate, with no significant additional semantic insights 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 specific action ('Fetch Amazon order history') and resource ('Amazon order history'), distinguishing it from sibling tools like get_amazon_order_details (likely for single orders) or export_amazon_orders_csv (for CSV export). It explicitly mentions what data is returned, making the purpose distinct and comprehensive.

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

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

The description provides clear context for when to use this tool ('for browsing order history or building reports'), but does not explicitly state when not to use it or name alternatives among sibling tools (e.g., vs. get_amazon_order_details for single orders). The guidance is helpful but lacks explicit exclusions or comparisons.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context beyond the input schema: it describes the scraping method ('infinite-scroll transactions list'), performance ('Faster than per-order extraction'), and return format ('Returns: date, order IDs, amount, payment method, card info (last 4 digits), vendor'). However, it lacks details on potential limitations like rate limits, authentication needs, or error handling, which are important for a scraping 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by efficiency notes, return values, and use cases. Every sentence earns its place by adding value without redundancy, making it easy to scan and understand quickly.

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 complexity (a scraping tool with 4 parameters) and no annotations or output schema, the description is mostly complete: it covers purpose, method, returns, and use cases. However, it lacks details on output structure (e.g., format of returned data) and behavioral aspects like error handling or pagination, which would be helpful for an agent invoking this 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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain region codes or date formats further). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description adds no extra parameter semantics.

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

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

The description clearly states the specific action ('Fetch all Amazon payment transactions') and resource ('from the dedicated transactions page'), distinguishing it from sibling tools like get_amazon_order_details or export_amazon_transactions_csv by focusing on payment transactions rather than orders or CSV exports. It explicitly mentions the method ('scrapes the infinite-scroll transactions list') and purpose ('reconciling payments, tracking spending, or exporting for accounting').

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

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

The description provides clear context for when to use this tool: 'Faster than per-order extraction' implies it's preferred over tools like get_amazon_order_details for bulk transaction data, and 'Useful for reconciling payments, tracking spending, or exporting for accounting' gives practical use cases. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as export_amazon_transactions_csv for CSV output.

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