M.K. Electronics

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

With no annotations, the description carries the transparency burden. It discloses that the tool lists a fixed set of 16 nationwide showrooms and supports an optional city filter, clarifying that 'near' refers to city filtering rather than geolocation. However, it does not describe the return fields or any side effects.

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, front-loaded with the verb and resource, and every word adds value. '16 superstores nationwide' is a concise additional detail.

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 one-parameter listing tool, the description is mostly sufficient, but the absence of an output schema and lack of return-value details means the description does not fully specify what the agent can expect. The scale mention helps, but the description is minimal.

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 provides complete coverage for the single optional 'city' parameter, including example values and an explanation of omission. The description adds no additional parameter semantics, so the baseline score of 3 applies.

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 (List) and resource (M.K. Electronics showrooms), and notes the optional city filter. This distinguishes it from sibling tools that handle products and categories.

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?

Usage is implied: the tool is for listing showrooms, but there is no explicit statement of when to use it over alternatives or exclusions. The description does not name search_products or other siblings as 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?

With no annotations provided, the description fully carries the behavioral disclosure burden. It clearly indicates a read-only fetch operation and lists the data returned (specs, EMI options, warranty, current price, stock). It does not mention error handling or non-finding behavior, but for this straightforward read operation, the disclosure is adequate and transparent.

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 two sentences, front-loaded with the core purpose, and includes the usage guidance in a compact, high-signal format. Every phrase adds value, with no redundancy or fluff.

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 having no output schema or annotations, the description effectively conveys the complete context needed: it lists the returned data fields, specifies the input source, and sequences usage after search_products. For a simple one-parameter read tool, this is sufficiently complete for reliable 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 input schema already covers the required parameter 'slug' with a description and example, giving 100% schema coverage. The description adds valuable context by explaining that the slug originates from search_products results, which helps the agent understand how to obtain and use the parameter correctly beyond just the schema definition.

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 function with a specific verb ('Fetch') and resource ('full product detail by slug'), and enumerates the included fields (specs, EMI, warranty, price, stock). It also distinguishes itself from siblings by explicitly positioning it as the follow-up to search_products for deeper detail.

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 directs when to use this tool: 'Use after search_products when the user wants to dig into one item.' This provides clear context and implicitly contrasts with the sibling search_products, giving the agent unambiguous guidance on tool selection.

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 states the action ('Get the storefront category tree') but does not disclose return format, pagination, or potential absence of categories. As a simple read-only operation, the risk is low, but additional behavioral context would improve transparency.

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 exceptionally concise, with two sentences: the first states the action, the second provides usage context. No wasted words, and the key information is 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?

For a simple tool with no parameters and no output schema, the description is largely sufficient. It covers purpose and usage, but lacking any note about whether the tree is flattened or nested, or whether an empty tree is possible, leaves a small gap.

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 tool has zero parameters, so the baseline is 4. There are no parameter semantics to clarify, and the description does not need to add anything beyond what the empty schema already communicates.

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 and resource: 'Get the storefront category tree.' This is distinct from sibling tools like search_products or get_product, so the agent can easily differentiate.

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 the tool: 'Use to orient the user when they're browsing without a specific product in mind.' It implies when not to use (when specific products are involved) but does not explicitly name 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, but the description discloses meaningful behavioral traits: 'Returns in-stock matches only' and 'ordered by relevance.' This goes beyond the schema and gives the agent important constraints. It could mention error handling or pagination, but for a search tool this is reasonably transparent.

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 no wasted words. The first sentence states the core function, the second specifies usage context. Both sentences earn their place.

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 tool is simple with only 2 parameters, and the schema covers them fully. The description provides scope, in-stock filter, relevance ordering, and usage guidance. It omits exact return format or error behavior, but given the simplicity and lack of output schema, this is still a complete definition.

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 'query' and 'limit' having detailed descriptions. The description itself adds no extra parameter-level details, so the baseline of 3 applies; the schema fully handles 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 uses a specific verb+resource pair: 'Full-text search across the M.K. Electronics catalog.' It clearly states what the tool does and implicitly distinguishes itself from siblings like get_product (specific lookup) and list_categories (browsing).

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 says 'Use this when the user asks to find / compare / shop for something,' giving clear usage context. However, it does not mention when not to use it or provide explicit alternative tool references, stopping 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.