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 mentions the tool returns 'full details including prices and specifications', which adds useful context beyond the input schema. However, it lacks information on potential limitations like pagination, error handling, or performance characteristics that would be helpful 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 efficiently structured in two sentences that each serve a distinct purpose: stating the core functionality and providing usage context. There's no wasted language, and the information is front-loaded with the essential purpose statement.

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

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

For a read operation with 3 parameters and no output schema, the description provides adequate but minimal context. It explains what the tool does and when to use it, but doesn't address return format details, error cases, or how results are structured. Given the lack of annotations and output schema, more completeness would be beneficial.

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 100% description coverage, providing detailed documentation for all three parameters. The description doesn't add any meaningful parameter semantics beyond what's already in the schema, so it meets the baseline expectation without adding extra value.

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 the verb 'browse' and resource 'products within a specific category', making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'search_products' or 'get_product_by_model', which might offer overlapping functionality with different approaches.

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 ('when you don't have specific search criteria'), which helps distinguish it from search-based siblings. It doesn't explicitly state when not to use it or name alternatives, but the implied guidance is sufficient for basic 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 full burden. It mentions 'detailed specifications and pricing' and 'availability markers', giving some behavioral context about return content. However, it doesn't disclose error handling for invalid models, rate limits, authentication needs, or response format details, leaving gaps for a tool with no annotation coverage.

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 waste: the first states purpose and input requirement, the second provides usage guidelines. Each sentence earns its place by adding distinct value, and the description is appropriately sized 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 no annotations and no output schema, the description partially compensates by outlining return content (specs, price, availability). However, for a tool with 100% schema coverage but no structured output definition, it should ideally specify response structure or error cases more explicitly to be 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?

Schema description coverage is 100%, so the schema already fully documents the single 'model' parameter with examples and matching rules. The description adds marginal value by reinforcing 'exact model number' but doesn't provide additional syntax or format details beyond what the schema specifies.

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 'Get' and resource 'detailed specifications and pricing for a specific product by exact model number', specifying the exact input requirement. It distinguishes from siblings by emphasizing 'exact model number' retrieval rather than category listing or search operations.

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 'Use when you know the exact product model and need full details including specs, current price, and availability markers', providing clear when-to-use guidance. It implicitly distinguishes from sibling tools (e.g., search_* tools) by requiring exact model matching rather than keyword searches.

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 that the tool returns a 'complete list' with statistics, which implies it's a read-only operation without side effects. However, it lacks details on behavioral traits such as pagination, rate limits, authentication needs, or error handling. The description adds some context but misses key operational 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?

The description is concise and well-structured in two sentences. The first sentence states the purpose and output, and the second provides usage context. Every sentence adds value without waste. It could be slightly more front-loaded by emphasizing the core action first, but overall it's 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's complexity (simple list operation with 0 parameters) and no annotations or output schema, the description is moderately complete. It explains what the tool does and its use cases, but it lacks details on the output format (e.g., structure of statistics) and behavioral constraints. For a tool with no structured data support, more completeness is needed to fully guide an agent.

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 parameters with 100% coverage, so no parameter documentation is needed. The description does not mention any parameters, which is appropriate. It earns a baseline score of 4 because it avoids redundancy and focuses on the tool's purpose and output, adding value without unnecessary details.

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

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

The description clearly states the tool's purpose: 'Get a complete list of all product categories with statistics.' It specifies the verb ('Get') and resource ('product categories'), and lists the data returned (names, counts, markers). However, it does not explicitly distinguish this from sibling tools like 'get_category_products' or 'search_products', which reduces the score from a 5.

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 implied usage guidance: 'Useful for browsing available product types or understanding the catalog structure.' This suggests when to use it (for overview purposes), but it does not explicitly state when not to use it or name alternatives among the sibling tools (e.g., use 'search_products' for specific queries). The guidance is helpful but not comprehensive.

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 for behavioral disclosure. It mentions search functionality and sorting but doesn't describe what the tool returns (list of CPUs? with what fields?), pagination behavior, error conditions, or rate limits. For a search tool with 4 parameters and no output schema, this is inadequate.

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

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

Two concise sentences with zero waste. First sentence states purpose and key parameters, second provides usage context. Well-structured and appropriately sized for the tool's complexity.

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 search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what results look like, what fields are returned, or how to interpret results. The schema covers parameters well, but the overall context for using the tool effectively is lacking.

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 mentions the three main parameters (socket, cores, sort_by) but adds no meaningful semantics beyond what's in the schema descriptions. 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: 'Specialized CPU/processor search tool' with specific search criteria (socket compatibility, core count, price sorting). It distinguishes from generic search tools but doesn't explicitly differentiate from 'search_products' or other hardware-specific search siblings.

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 implied usage context ('Best for building compatible systems or finding CPUs with specific performance characteristics') but doesn't explicitly state when to use this vs. alternatives like 'search_products' or 'get_product_by_model'. No when-not-to-use guidance is provided.

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 the tool as a 'search tool' which implies read-only behavior, but doesn't specify whether it queries a database, API, or local inventory, nor does it mention rate limits, authentication needs, or error handling. The description adds minimal behavioral context beyond the basic search function.

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 and front-loaded, stating the tool's purpose in the first sentence and adding context in the second. Both sentences earn their place by defining specialization and use cases, with 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 search tool with 4 parameters, 100% schema coverage, and no output schema, the description is adequate but has gaps. It covers purpose and use cases but lacks details on behavioral aspects like data source, result format, or error conditions. Without annotations or output schema, more 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?

The input schema has 100% description coverage, providing detailed explanations for all parameters. The description adds value by mentioning the search criteria (chipset, VRAM, price) and use cases, but doesn't provide additional semantic context beyond what's in the schema. Baseline 3 is appropriate as 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 tool's purpose as a 'search tool' for 'graphics cards (VGA)' with specific search criteria (chipset, VRAM, price) and target use cases (gaming, content creation, AI). It distinguishes from siblings like 'search_cpu' or 'search_products' by specializing in GPUs, though it doesn't explicitly contrast with 'search_products' which might also handle GPUs.

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 context ('Perfect for gaming builds, content creation, or AI workloads') but doesn't explicitly state when to use this tool versus alternatives like 'search_products' or 'get_product_by_model'. It provides general scenarios but lacks specific guidance on tool selection 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 carries the full burden of behavioral disclosure. It mentions the tool's critical role in compatibility but does not detail behavioral traits like response format, error handling, or performance characteristics. The description adds some context about the importance of matching parameters but lacks specifics on what the tool returns or how it behaves 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 front-loaded and efficient: two sentences that clearly state the tool's purpose and critical usage context. Every sentence earns its place by conveying essential information without redundancy or unnecessary details, making it easy to 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 complexity (5 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains the tool's purpose and importance but does not cover behavioral aspects like return format or error conditions. With no output schema, more detail on what results to expect would improve completeness, but the description meets minimum viability for a search tool.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by emphasizing the importance of socket and size matching for compatibility. However, it does not provide additional semantic context or usage examples that go beyond the schema's detailed 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 explicitly states the tool's purpose: 'Specialized motherboard (MB/mainboard) search tool. Find motherboards by CPU socket, chipset, and size.' It uses specific verbs ('search', 'find') and resources ('motherboards'), and distinguishes itself from siblings like 'search_cpu' or 'search_products' by focusing on motherboard-specific parameters.

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: 'Critical for system compatibility - must match CPU socket and case size.' It implies usage in PC building scenarios where motherboard compatibility is essential. However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as 'search_products' for broader searches.

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 of behavioral disclosure. The description mentions it's a 'general search' which implies read-only behavior, but doesn't explicitly state whether this requires authentication, has rate limits, or what the return format looks like. It provides some context about scope but lacks details on behavioral traits like pagination, error handling, or performance characteristics.

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 just two sentences that each serve a clear purpose. The first sentence states the tool's purpose, and the second provides explicit usage guidelines. There's no wasted text, and the information is front-loaded with the most important details 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?

Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description provides good contextual completeness. It clearly explains the tool's purpose and when to use it versus alternatives. However, without annotations or output schema, it could benefit from more information about what the search returns (e.g., result format, pagination) and any behavioral 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?

With 100% schema description coverage, the input schema already provides comprehensive documentation for all 5 parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions the tool's general purpose but doesn't explain parameter interactions or provide additional semantic context, so it meets the baseline for good 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 tool's purpose as a 'general search across all computer components' with a specific verb ('search') and resource ('computer components'). It explicitly distinguishes this from sibling tools by mentioning dedicated search tools for specific components (search_cpu, search_gpu, etc.), making it easy to understand when this tool is appropriate 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 explicit guidance on when to use this tool ('for broad searches or when component type is uncertain') and when to use alternatives ('For specific components, use dedicated search tools'). This directly addresses the sibling tools listed, giving clear context for tool selection without any misleading information.

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

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

No annotations are provided, so the description carries the full burden. It mentions the tool is for searching/finding memory, which implies a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, rate limits, error handling, or what the output format looks like (e.g., list of products with details). This leaves significant gaps for an agent to understand how to interact with it effectively.

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 with two sentences: the first states the purpose and parameters, and the second provides usage context. It's front-loaded with key information and avoids unnecessary details, though it could be slightly more structured (e.g., bullet points for parameters).

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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., product listings, availability, links), how results are formatted, or any prerequisites (e.g., authentication). For a search tool with multiple parameters and no output schema, this leaves the agent without crucial context for successful 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?

The description lists the search criteria (DDR generation, capacity, speed, price), which aligns with the input schema parameters. However, with 100% schema description coverage, the schema already provides detailed parameter information (e.g., options, examples, ranges). The description adds minimal value beyond restating the parameters, so it meets the baseline of 3.

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: 'Find desktop or laptop memory by DDR generation, total capacity, speed, and price.' It specifies the resource (memory/RAM) and the search criteria, though it doesn't explicitly differentiate from sibling tools like 'search_products' or 'search_cpu' beyond mentioning it's 'specialized' for RAM.

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 implied usage context: 'Essential for system upgrades or new builds.' This suggests when to use the tool, but it doesn't explicitly state when not to use it or name alternatives among sibling tools (e.g., 'search_products' might be a broader 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 the full burden of behavioral disclosure. It mentions the tool is for 'search' and 'find' operations, implying read-only behavior, but doesn't specify if it's safe, requires authentication, has rate limits, or what the output format looks like (e.g., list of products with details). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 with two sentences: the first states the purpose and key parameters, and the second adds context about drive types and usage. It's front-loaded with essential information and avoids redundancy. However, the second sentence could be slightly more concise by integrating 'for OS and data storage' 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?

Given the tool's complexity (search with 4 parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and basic parameters but lacks details on behavioral traits (e.g., safety, output format) and doesn't fully compensate for the absence of structured data. For a search tool, it's adequate but has clear gaps in providing a holistic 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 schema description coverage is 100%, meaning all parameters are well-documented in the input schema. The description adds minimal value beyond the schema by mentioning 'interface type, capacity, and price' as search criteria, but doesn't provide additional syntax, format details, or examples not already covered. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't significantly enhance parameter understanding.

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: 'Specialized SSD/solid-state drive search tool. Find storage drives by interface type, capacity, and price.' It specifies the resource (SSDs) and key search parameters, distinguishing it from general search tools like 'search_products' by focusing on SSDs. However, it doesn't explicitly differentiate from 'search_cpu' or 'search_gpu' beyond mentioning SSD-specific attributes.

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 context: 'Covers M.2 NVMe, SATA, and PCIe drives for OS and data storage,' suggesting it's for finding storage drives in computing builds. It doesn't provide explicit when-to-use guidance versus alternatives like 'search_products' (general) or 'get_product_by_model' (specific), nor does it mention exclusions or prerequisites. The context is clear but lacks sibling differentiation.

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