Mall Store Directory

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

With no annotations, the description carries the full burden. It transparently describes the output fields and parameter constraints (country values, max_results range), but does not explicitly state that the operation is read-only or disclose pagination/ordering behavior. The 'list' verb implies non-destructive, yet it could be more explicit about the safety profile and any server-side 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 compact: a purpose sentence, a usage hint, and a concise args list. Every sentence adds value, and the most critical information (purpose and follow-up) is front-loaded. No filler 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 listing tool with no output schema, the description covers the output format, the parameter meanings, and a usage pattern. It omits only minor details like sort order or pagination, which are not critical for a discovery call and are unlikely to impede correct 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?

Schema coverage is 0%, so the description must fully explain the parameters. It does: country is specified with allowed values ('ro, us, gb, hu') and max_results is given a range ('1-100'). This is exactly the kind of compensation needed when schemas are bare, leaving no ambiguity about how to invoke the tool.

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 states a specific verb ('List') and a precise resource ('the malls in the directory') with a clear output format (store counts and specific fields). It distinguishes itself from sibling mall_stores by focusing on listing malls rather than their stores, so an agent can tell them apart.

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?

It explicitly calls out the tool as a 'useful first call for discovery' and instructs to 'feed mall_id to mall_stores()', giving a clear workflow. It does not explicitly mention when not to use it or alternative search tools, but the guidance is sufficient for most discovery 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It discloses that the tool accepts a partial name fragment in addition to an exact slug, and that max_results caps the output at 1-500. However, it does not mention the return shape, error behavior for unknown malls, or whether the list is sorted, leaving some behavioral uncertainty.

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, but it repeats the mall_id explanation twice: first in the introductory paragraph ('Also accepts a mall name fragment') and again in the Args section with the same examples. The 'Args' section is a second formatting of the same information, which adds redundancy rather than new content.

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 two-parameter tool with no output schema, the description covers all inputs, the source of valid ids, and the result limit, so an agent can invoke it correctly. The main gap is the lack of any indication of the returned store objects' structure, but that is not needed to make a correct call. Overall the description is sufficient for its scope.

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 provides only property names and types with no descriptions (coverage 0%), so the description is the only source of parameter meaning. It fully explains mall_id (slug from list_malls(), with concrete examples, plus name-fragment fallback) and max_results (maximum number of stores, 1-500). This is far beyond what the schema alone offers.

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 opens with a specific verb-plus-object: 'List every store in a specific mall by mall_id.' This makes the operation unambiguous and distinguishes it from siblings like stores_by_city, stores_by_category, and search_stores, which cover different query axes. The additional note that mall_id is the slug from list_malls() further anchors what the resource is.

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 clear context: use this tool when you have a mall identifier (either a slug from list_malls() or a name fragment). It names list_malls as the source of valid ids, implying a prerequisite call. It does not explicitly state when not to use it (e.g., searching by city or category), but the sibling tool names make those alternatives evident.

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 present, so the description carries the behavioral burden. It discloses important search behavior: free-text matching, diacritic insensitivity, AND-of-tokens semantics, and a 20-field store record return. It does not explicitly state the operation is read-only, but the 'search' framing makes that a reasonable inference.

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 opens with a precise purpose, continues with concrete behavioral details and examples, and ends with result shape and args. Every sentence adds value, and there is no duplication of schema 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 two-parameter search tool with no output schema, this description covers the search scope, matching behavior, result shape, and parameter semantics. It is sufficient for an agent to invoke correctly; missing pagination or no-results behavior are minor gaps rather than critical omissions.

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 no parameter descriptions (0% coverage), but the description's Args section compensates by explaining query as free-text search terms and max_results as a limit with a 1-100 range. This adds real meaning beyond the schema, though it does not cover edge cases like empty or malformed queries.

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 ('Search'), a clear resource ('mall stores'), and a distinctive scope ('across the whole directory' including store, brand, mall, city, operator, category). The free-text examples make the operation unambiguous and distinguish it from the filtered sibling tools.

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 frames this as the directory-wide free-text search tool, which clearly signals when it applies versus the more targeted siblings like stores_by_category or stores_by_city. It does not explicitly say 'use X when Y', but the 'whole directory' contrast and examples give solid contextual guidance.

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 carries full behavioral burden and reveals non-obvious traits: hierarchical expansion ('Fashion' matches 'Women's Fashions'), diacritic-insensitive matching, and accepted country codes. It does not specify return format or empty-result behavior, but for a read-only listing tool these are minor 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 well-structured: a one-sentence summary, a compact behavior note, and a terse Args list. Every sentence adds information; the Args section is useful precisely because the schema lacks descriptions. The only minor redundancy is repeating default values already in the schema.

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 three simple parameters, no annotations, and no output schema, the description covers purpose, filtering behavior, parameter constraints, and match quirks well. It omits when to choose this over sibling tools and the shape of returned store objects, but the core calling contract is clear.

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 0%, so the description must supply all parameter meaning. It does, with examples for category ('Fashion', 'Food Court'), allowed country values (ro, us, gb, hu), and a max_results range (1-100) that supplements the schema's default. This fully compensates for 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 opening sentence 'List stores by category, optionally filtered by country' uses a specific verb and resource, and the matching semantics are clearly described. It differentiates by the category dimension but never explicitly contrasts with siblings like search_stores or stores_by_city, which could also involve category filtering.

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 conveys useful context about when matching works, such as multilingual, hierarchical, and diacritic-insensitive substring matching, which implies use for fuzzy category lookups. However, there is no explicit guidance on when to prefer this tool over search_stores or stores_by_city, nor any when-not-to-use conditions.

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 behavioral disclosure burden. It does this well by revealing the diacritic-insensitive, substring-tolerant matching behavior and the accepted country code set. It does not mention output format or ordering, but for a simple read-only list tool the disclosed traits are the important ones.

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 compact and well structured: a one-line summary, a key behavioral note, and a clean Args section. Every sentence contributes useful information, with no redundancy or filler.

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 provides enough invocation-critical detail: parameters are explained, matching semantics are specified, and the optional country filter is clarified. It could be slightly more complete by noting how it differs from search_stores or describing the return shape, but nothing needed to construct a valid call is missing.

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 0%, and the description fully compensates by documenting all three parameters with concrete details: city examples, valid country codes/full names, and the max_results range. This adds real meaning beyond the bare schema fields.

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 opens with a specific verb and resource: 'List stores in a city, optionally filtered by country.' This clearly identifies both the action and the scope, and it is distinguishable from sibling tools like stores_by_category or stores_by_operator because the focus is location-based lookup.

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 establishes clear usage context: use this tool when you need stores in a given city, with an optional country refinement. It does not explicitly name alternatives such as search_stores or explain when not to use them, so it stops short of a 5.

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 explicitly discloses a non-obvious behavior: diacritic-insensitive substring matching with concrete examples ('nepi' matches 'NEPI Rockcastle'). It also documents the max_results boundary (1-100), adding value beyond the schema. With no annotations supplied, this meaningfully covers the key behavioral nuance, though side effects are not discussed.

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 definition is compact and front-loaded: the purpose sentence comes first, followed by the key matching behavior, then the arguments. Every sentence earns its place with no 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 two-parameter lookup tool, the description covers purpose, matching rules, and parameter constraints. It omits response shape and pagination details, and there is no output schema to supplement those details, but the core invocation requirements are fully addressed.

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 0%, but the description fully compensates by explaining both parameters: operator may be a name or fragment with examples, and max_results is defined as the maximum number of stores with a numeric range. This is complete 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 opens with 'List stores by mall operator,' a specific verb and resource with a clear scope. The 'by mall operator' phrase differentiates it from siblings like stores_by_category and stores_by_city, though it does not explicitly name 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?

There are no explicit statements about when to use this tool versus siblings such as search_stores or mall_stores. The matching examples imply common use cases, but no exclusions or alternative routing are provided, leaving selection mostly to inference.

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