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 what data is returned (name, price, aisle location) but doesn't cover important aspects like whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or how results are structured/limited. This leaves significant gaps for an agent to understand the tool's 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 appropriately brief (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first states what the tool does, the second specifies what information is returned. There's no wasted verbiage 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?

For a simple search tool with one parameter and no output schema, the description provides basic but incomplete context. It covers what the tool does and what data it returns, but lacks behavioral details that would be important for proper usage. Without annotations or output schema, the description should ideally provide more operational guidance.

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, with the single 'query' parameter well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline score when schema coverage is high.

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 ('Search for products') and resource ('products available at Publix grocery stores'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'store_finder' or 'weekly_deals' beyond the different resource focus, which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives like 'store_finder' or 'weekly_deals'. It doesn't mention prerequisites, limitations, or specific contexts where this search tool is preferred over other available tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what the tool does (find stores and return specific details) but lacks information on traits like rate limits, error handling, authentication needs, or data freshness. The description is minimal and does not compensate for the absence of 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 front-loaded and concise, consisting of two sentences that efficiently convey the tool's purpose and return values without unnecessary details. Every sentence earns its place by providing essential information, making it easy for an agent to parse 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 low complexity (single parameter, no output schema, no annotations), the description is adequate but has gaps. It covers the basic purpose and return values, but without annotations or output schema, it lacks details on behavioral traits and output structure. The description is complete enough for a simple lookup tool but could be more informative.

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

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

The input schema has 100% description coverage, with the parameter 'location' well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 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 tool's purpose with specific verbs ('Find') and resources ('Publix grocery store locations'), and distinguishes it from siblings by focusing on store locations rather than products or deals. It specifies what information is returned (address, hours, phone number), making the purpose explicit and differentiated.

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 by mentioning 'near a zip code or city', but it does not explicitly state when to use this tool versus the sibling tools (product_search, weekly_deals). There is no guidance on exclusions or alternatives, leaving the agent to infer usage based on the purpose alone.

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 returns (item name, original price, sale price, savings) which is helpful, but doesn't mention important behavioral aspects like whether this requires authentication, rate limits, freshness of data, or pagination for large result sets. The description adds some value but leaves significant 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 perfectly concise with two sentences that each earn their place: the first states the core purpose, and the second explains optional filtering and return values. There's zero wasted language, and information is front-loaded with the primary function stated immediately.

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 optional parameter, no output schema, no annotations), the description is adequate but has clear gaps. It explains what the tool does and what it returns, but doesn't cover behavioral aspects like authentication needs, data freshness, or error conditions. For a tool that fetches current promotional data, more context about data sources and limitations would be helpful.

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% (the single parameter 'category' is fully documented in the schema), so the baseline is 3. The description adds value by explaining the optional nature of filtering ('Optionally filter by category') and providing context about what happens when omitted ('Omit to get deals across all categories'), which enhances understanding beyond the schema's technical specification.

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 a specific verb ('Get'), resource ('weekly deals and specials at Publix'), and scope ('current'). It distinguishes from sibling tools like 'product_search' (which likely searches individual products) and 'store_finder' (which finds store locations) by focusing specifically on promotional 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?

The description implies usage context ('Get the current weekly deals') but doesn't explicitly state when to use this tool versus alternatives. It mentions optional filtering by category but doesn't provide guidance on when filtering is appropriate versus getting all deals. No explicit exclusions or comparisons to sibling tools are provided.

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