PricePilot — Free CPG Pricing Intelligence

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

The description adds valuable behavioral context beyond the annotations. While annotations indicate read-only, non-destructive, and closed-world operation, the description reveals that this tool performs benchmarking against Amazon category data, returns percentile ranks and market positions, and replaces manual research methods. This provides important context about what the tool actually does operationally.

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 clear sections: purpose statement, usage guidelines, return values, and parameter documentation. Every sentence earns its place by providing essential information. The front-loaded purpose statement immediately communicates the tool's function, followed by progressively detailed 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 complexity of a benchmarking tool with no output schema, the description provides strong coverage of purpose, usage, parameters, and return values. It explains what metrics are returned (percentile rank, market position, distance from median) but doesn't detail the exact output structure. For a tool with no output schema, this is good but could benefit from more explicit output format 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?

With 0% schema description coverage, the description fully compensates by providing complete parameter documentation. It clearly explains that 'products' requires a list with name and price fields, specifies the price format (float in dollars), and enumerates the four valid 'category' values (Grocery, Health & Beauty, Household, Pet Supplies). This adds substantial meaning beyond the bare schema.

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

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

The description clearly states the tool's purpose: comparing CPG product prices against Amazon category benchmarks. It uses specific verbs ('compare', 'returns') and identifies the resource (products with price data). It distinguishes from siblings by focusing on multi-product benchmarking rather than overviews, trends, or single-product positioning.

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: when a brand manager asks specific comparison questions about product pricing and market position. It also mentions what it replaces (manual store walks and spreadsheet comparisons), providing clear context for its application. While it doesn't name specific sibling alternatives, it clearly defines its 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?

Annotations already indicate read-only, non-destructive, and closed-world behavior. The description adds valuable context beyond this: it explains what data is returned (price tier breakdowns, product count, median price, category trend) and mentions it's for 'without expensive syndicated data subscriptions,' suggesting accessibility. However, it doesn't detail potential limitations like data freshness 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 well-structured and appropriately sized. It starts with the core purpose, then provides usage guidelines, followed by return values and parameter details. Every sentence adds 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 tool's moderate complexity (single parameter, no output schema), the description is mostly complete. It covers purpose, usage, returns, and parameters. However, without an output schema, it could benefit from more detail on return format (e.g., structure of 'price tier breakdowns'), though the annotations provide safety 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?

With 0% schema description coverage for the single parameter 'category,' the description fully compensates by providing essential semantics: it lists the valid category names ('Grocery, Health & Beauty, Household, or Pet Supplies') and explains the parameter's purpose ('Category name'). This adds crucial meaning not present in 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's purpose with specific verbs ('Get a pricing landscape overview') and resource ('for an Amazon CPG category'), distinguishing it from siblings like 'get_category_trend' (which focuses on trends) or 'compare_products' (which compares specific products). It explicitly answers what the tool does: provides pricing overviews including price range, tiers, and median price.

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 this tool ('Use when a brand manager asks...') and lists specific use cases ('pricing strategy, market entry analysis, or competitive benchmarking'). It also implicitly distinguishes from siblings by focusing on overviews rather than trends or comparisons, though it doesn't name alternatives directly.

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

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

The description adds valuable behavioral context beyond annotations by specifying the analysis methodology ('Based on 30-day price trend analysis across 100+ tracked products'). While annotations already indicate read-only and non-destructive behavior, the description provides useful implementation details about the data scope and timeframe.

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

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

The description is well-structured with purpose statement, usage scenarios, methodology context, and parameter explanation. While slightly longer than minimal, every sentence adds value. The information is front-loaded with the core purpose stated first.

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 single-parameter read-only tool with no output schema, the description provides comprehensive context including purpose, usage scenarios, methodology, and parameter details. The main gap is lack of information about return format, but this is partially mitigated by the clear purpose statement.

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 fully compensates by clearly explaining the single parameter's purpose and providing specific category examples ('Grocery, Health & Beauty, Household, or Pet Supplies'). This adds essential semantic meaning that the schema alone lacks.

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 ('Check whether Amazon prices... are rising, stable, or falling') and identifies the resource ('CPG category'). It distinguishes from siblings like 'get_category_overview' by focusing specifically on price trend analysis rather than general category information.

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 scenarios with concrete examples ('when a brand manager asks: are prices going up in my category? Should I raise my price? Is there a price war?'). It also implicitly distinguishes from siblings by focusing on price trends rather than product comparison or general overviews.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, covering safety and data scope. The description adds valuable context about the data source ('based on 100+ tracked products'), return format details (percentile rank, Price Index, market position), and positioning as a free alternative to premium services, which helps the agent understand the tool's capabilities beyond basic annotations.

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

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

The description is efficiently structured with a clear purpose statement, usage guidelines, return value explanation, and parameter documentation—all in well-organized paragraphs. Every sentence adds value without redundancy, and the information is front-loaded with the core functionality.

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 (2 parameters, no output schema), the description provides strong context: clear purpose, usage triggers, return value details, and parameter semantics. It effectively compensates for the lack of output schema by describing the return format. The only minor gap is the absence of explicit error handling or data recency information.

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 fully compensates by explaining both parameters: 'price' as 'Product price in dollars' with an example, and 'category' as 'Category name' with specific enumerated values (Grocery, Health & Beauty, Household, Pet Supplies). This adds essential meaning beyond the bare schema, though it doesn't cover edge cases or validation rules.

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 ('Check where a CPG product price sits vs Amazon competitors') and distinguishes it from siblings by focusing on price positioning rather than product comparison, category overviews, or trends. It explicitly answers questions about pricing competitiveness and market positioning.

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 scenarios ('Use when a brand manager or DTC founder asks...') with specific questions to trigger this tool, and mentions a free alternative to NielsenIQ/SPINS data. It implicitly distinguishes from siblings by focusing on price analysis rather than broader comparisons or category-level insights.

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

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

The description adds valuable behavioral context beyond what annotations provide. Annotations indicate read-only, non-destructive, and closed-world operations, but the description adds specific details: coverage scope ('Currently covers Grocery, Health & Beauty, Household, and Pet Supplies'), data volume ('100+ products tracked per category'), and refresh frequency ('refreshed weekly'). No contradiction with annotations exists.

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 structured and concise. The first sentence states the core purpose, the second provides usage guidance, and the third adds important contextual details. Every sentence earns its place with no wasted words, and information is front-loaded appropriately.

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 (0 parameters, no output schema) and good annotation coverage, the description is quite complete. It explains what the tool does, when to use it, what data it covers, and refresh frequency. The only minor gap is that without an output schema, the exact format of 'pricing stats and trends' isn't specified, but for this type of listing tool, the description provides sufficient 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?

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, and it adds context about what data will be returned (categories with pricing stats/trends, specific coverage areas). This compensates well for the lack of parameter documentation needs.

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 ('List available CPG product categories') and resources ('with pricing stats and trends'). It distinguishes from siblings by focusing on listing categories rather than comparing products, getting overviews/trends for specific categories, or checking price positions. The description goes beyond just restating the name/title.

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: 'Use to see which Amazon categories have pricing data available.' This provides clear context for usage. While it doesn't explicitly name alternatives, the sibling tools (compare_products, get_category_overview, get_category_trend, get_price_position) have clearly different purposes, making the distinction implicit but strong.

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

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

Annotations already indicate read-only, non-destructive, and closed-world operation, which the description aligns with by using 'Check' (implying safe read). The description adds valuable context about checking both 'server health' (likely uptime/performance) and 'data freshness' (recency of pricing intelligence data), which are behavioral traits not covered by annotations.

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

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

The description is a single, front-loaded sentence that efficiently conveys the tool's purpose with zero wasted words. Every element ('Check', 'PricePilot pricing intelligence server', 'health and data freshness') earns its place by providing 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 the tool's simplicity (0 parameters, no output schema) and rich annotations, the description is mostly complete. It covers the dual purposes of health and freshness checks, but could benefit from clarifying the output format (e.g., what metrics are returned) since there is no output schema provided.

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

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

With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately notes there are no required inputs by not mentioning any parameters, and it adds semantic context about what is being checked (health and freshness), which is relevant for understanding the tool's operation without inputs.

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') and target ('PricePilot pricing intelligence server health and data freshness'), distinguishing it from sibling tools focused on product comparison, category analysis, and price positioning. It explicitly identifies both health monitoring and data freshness assessment as dual 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 implies usage for monitoring server status and data quality, but provides no explicit guidance on when to use this tool versus alternatives (like checking specific data endpoints via sibling tools) or any prerequisites. The context is clear but lacks comparative or conditional instructions.

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