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

The description discloses that not all chains support per-store stock queries and that unsupported chains return a clear error. It also mentions that storeId takes precedence over near. With no annotations, the description carries the burden, but it omits details about response format, rate limits, or authentication.

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

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

The description is three sentences with no redundant information. The first sentence states the core purpose, followed by filtering options and a note about chains. It is front-loaded and efficient.

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

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

Given the lack of an output schema and annotations, the description adequately covers the main purpose, filtering options, and chain limitations. However, it does not describe the output returned (e.g., list of stores with availability), leaving a gap in completeness.

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

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

Schema coverage is 100%, so the input schema already describes each parameter in detail. The description adds a high-level summary but does not provide new semantic information beyond what is in the schema, resulting in a baseline score 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 uses a specific verb 'check' and resource 'stores of a given chain have a specific product in stock'. It clearly distinguishes from siblings like find_stores (which finds stores) and get_product (which gets product info) by focusing on stock availability.

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

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

The description provides explicit use cases ('is this product available near Zurich HB?', 'which Coop has item X in stock?') and notes that unsupported chains return a clear error. However, it does not explicitly contrast with sibling tools or state when not to use it.

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 explains that the tool returns store details including opening hours where available, indicating a read-only operation. However, it does not disclose potential rate limits, authentication needs, or any side effects, which is acceptable for a simple search tool.

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

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

The description is three sentences: first states purpose, second describes inputs, third summarizes outputs and examples. It is front-loaded with key information, no redundant phrases, and every sentence adds value.

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

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

For a search tool with 3 parameters and no output schema, the description adequately covers inputs, outputs, and example usage. It mentions return fields (name, address, chain, location, hours). It could have explicitly stated the default radius, but that is detailed in the schema. Overall, it is sufficiently complete for an agent to use effectively.

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

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

The input schema has 100% coverage with descriptions for each parameter. The description adds minor context (e.g., prefer zip or lat/lng for speed, geocoding via Nominatim) but mostly restates schema information. The baseline score of 3 is appropriate as the description provides some additional guidance.

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

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

The description clearly states the tool finds grocery stores near a location with filters for chain and radius, using specific verbs and resource. It also provides example use cases that distinguish it from siblings like find_stock and get_product.

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

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

The description gives concrete examples of when to use this tool ('find a Migros near me', 'which Coop branches are in 8001?') and hints at its role before checking stock. However, it does not explicitly state when not to use it or provide alternatives for non-store queries.

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 lists the returned fields (price, brand, size, etc.) and states that it fetches details, but does not disclose potential side effects, authentication needs, or rate limits. The behavior is sufficiently clear for a read-only fetch.

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

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

The description is three sentences, front-loaded with the core purpose, and contains no redundant information. Every sentence contributes value.

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

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

Given the tool has only 2 simple parameters and no output schema, the description fully explains what the tool does, what it returns, and how to get the required inputs. It is complete for its complexity.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds minimal additional meaning: it reiterates the 'chain' parameter as 'the grocery chain' and 'id' as 'chain-specific product identifier', and mentions obtaining via search_products, which is already 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 verb 'Fetch', the resource 'product details', and the specific scope 'for a specific chain + product ID pair'. It distinguishes itself from sibling tools like search_products by explaining what it does differently.

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 instructs to obtain product IDs from search_products and provides example use cases like 'get details for this Migros product'. It implies when to use this tool but does not explicitly mention when not to use it or alternatives beyond search_products.

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

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

With no annotations, the description transparently explains the tool's behavior: it lists promotions, returns specific fields (name, discount, validity dates, stores), and supports filters. It doesn't mention potential limitations like rate limits or authentication, but the read-only nature is clear.

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

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

The description is concise (two sentences) and front-loaded: first sentence states the main function and filters, second lists returns and usage examples. Every sentence is meaningful and directly contributes to 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?

Despite no output schema, the description adequately covers what the tool returns (name, discount, validity dates, applicable stores). It doesn't mention pagination or result limits, but for a promotional listing query, the essential information is 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?

All 4 parameters have schema descriptions (100% coverage). The description adds value by providing concrete examples (e.g., 'Käse', 'wine' for query) and referencing the helper tool find_stores for store IDs, enriching the schema information.

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

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

The description clearly states the tool lists current promotional deals across Swiss grocery chains, with a specific verb and resource. It distinguishes itself from sibling tools like search_products (general product search) and get_product (single product details) by focusing on promotions and filtering options.

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

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

The description provides explicit usage examples (e.g., 'what is on sale this week?', 'any Migros deals on cheese?'), guiding when to use the tool. It does not explicitly state when not to use it, but the context contrasts well with sibling tools.

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

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

No annotations provided, so description carries full burden. It describes the operation as 'probe with a trivial query' and reports status/latency/capability flags, implying a read-only health check. However, it does not explicitly confirm no side effects, auth requirements, or rate limits. The description is adequate but not rich.

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

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

Two sentences: first states functionality, second provides usage guidance. No wasted words, front-loaded with key action.

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

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

For a simple diagnostic tool with 2 optional params and no output schema, the description covers purpose, trigger, and high-level output. Could be slightly more detailed on output format, but sufficient.

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

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

Schema description coverage is 100% with both parameters documented. The description adds no new meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description uses specific verb 'probe' and resources 'chain adapters' and states outputs: status, latency, and capability flags. It also positions it for debugging missing chains, distinguishing it from sibling data tools like search_products or find_stock.

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

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

Explicitly states when to use: 'chain seems missing from results' or 'debugging adapter problems'. Does not mention when not to use or contrast with alternatives, but the context with sibling tools makes the purpose distinct.

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 responsibility. It mentions return of 'primary plan plus alternatives' and strategies, but does not elaborate on side effects, rate limits, or error behavior (e.g., no stores found). It is adequate but not detailed.

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?

Four sentences, front-loaded with purpose, no redundant text. Each sentence contributes context: purpose, item types, usage hint, and strategy options.

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

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

Covers high-level purpose, item flexibility, and strategies. However, it lacks details on the return format (no output schema) and does not describe what happens when constraints fail (e.g., no matching products). Still, it is largely complete for a planning 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 little new information beyond strategies, making it a baseline 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 specifies the tool's function: planning a multi-store shopping trip near a location, picking the best products across Swiss grocery chains. It distinguishes itself from siblings like find_stock and find_stores, which handle single-store queries or product lookups.

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

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

Explicitly states when to use: 'Use when the user gives a list of items and asks "where should I shop?" or "what's cheapest?"' and outlines three strategies. However, it lacks explicit when-not-to-use guidance compared to alternatives.

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

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

No annotations are provided, so the description must cover behavioral details. It mentions results are grouped by chain and include price, unit price, size, and promotion info, but does not explain pagination (limit/offset defaults), rate limits, or auth requirements. The parallel search across chains is also not disclosed.

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

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

Three sentences: one for main action, one for filters, one for output and examples. Highly concise, front-loaded, and every sentence adds value without 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?

The description covers purpose, filters, and output format well, but lacks details on default pagination, result ordering, and treatment of empty results. Given the complexity (nested filters, no output schema), it is mostly complete but has minor gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by listing output fields (normalized price, unit price, size, promotion info) and providing concrete examples that tie parameters to use cases, which helps the agent understand parameter combinations.

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

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

The description clearly states the tool searches for products across specific Swiss grocery chains by keyword, with filters and grouped results. It provides concrete example queries, effectively distinguishing it from siblings like get_product (single product) or find_stock (stock levels).

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

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

The description gives specific use cases (e.g., 'find organic milk under 2 CHF') and implies usage for multi-chain searches. However, it does not explicitly state when to use other tools like get_product or find_stock, leaving some ambiguity for the agent.

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