123elec-mcp

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 disclosure. It only states that it checks stock but does not describe what kind of stock information is returned, whether any side effects exist, error behavior, or authentication requirements. This is a significant gap for an operation that might be read-only but has unspecified 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 a single, concise sentence that directly states the tool's purpose without any unnecessary words. It is perfectly front-loaded and has no 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?

Given there is no output schema, the description should explain what the tool returns when checking stock (e.g., quantities, availability flags, low-stock indicators). It also does not mention batch size limits or behavior for invalid SKUs. The tool is simple, but the lack of return value details makes the description incomplete for an agent to know what to expect.

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 covers 100% of parameters (skus as a list), so the baseline is 3. The description adds minimal extra meaning by confirming the tool works with multiple products, which is already implied by the array type. It does not clarify SKU format or special constraints, but the schema already provides the essential type 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 uses a specific verb ('Vérifie') with a clear resource ('stock de plusieurs produits'), making the tool's purpose unambiguous. It clearly distinguishes itself from sibling tools like get_categories or get_recommendations, which serve different functions.

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 when stock levels need to be checked for multiple products, which is clear from the purpose. However, it does not explicitly mention alternatives or exclusion scenarios, nor does it reference sibling tools, so guidance is only implicit.

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 burden. It discloses that the output is hierarchical, which is a useful behavioral trait, but it does not specify whether the operation is read-only, the return format (nested tree vs flat list), or any other behaviors. For a zero-parameter tool, this is acceptable but thin.

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 a single short phrase, extremely concise and front-loaded. Every word earns its place with no 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?

Given the zero-parameter, no-output-schema, and no-annotation context, the description is simple but covers the core return value ('hierarchical list'). It could be more explicit about the output representation, but for a basic category listing tool, it is reasonably 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?

The tool has zero parameters, so schema coverage is 100%. The description adds no parameter information, but none is needed. Baseline for 0 parameters is 4.

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 'hierarchical list of categories' clearly identifies the resource (categories) and the hierarchical structure, distinguishing it from sibling tools like search_products and get_product. However, it is a noun phrase rather than an explicit verb+resource construction.

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?

No explicit when-to-use or alternative guidance is given. Usage is implied: the tool should be used when a hierarchical category list is needed. It does not contrast with sibling tools, so guidance is minimal.

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 the transparency burden, but it only says 'complete details' without specifying response structure, whether it's read-only, or potential limitations. It assumes a get operation but doesn't explicitly disclose behavior beyond the name.

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?

A single, concise sentence with no redundancy. It is appropriately sized for the simple purpose.

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?

No output schema or annotations, so the description should explain return values and behavioral constraints. It only says 'complete details' but leaves the actual content and any side effects unspecified, making it incomplete for an agent to predict response.

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 covers all parameters with descriptions (sku and store). The description's 'by SKU' restates the sku parameter's role, adding no new meaning beyond schema. Baseline of 3 applies due to high 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 retrieves complete product details using a SKU identifier. It distinguishes itself from sibling tools like check_stock and search_products by specifying the resource and key identifier.

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 when a SKU is available and full product information is needed, but it does not explicitly mention when to prefer this over sibling tools or provide exclusion 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?

No annotations are provided, and the description does not disclose behavioral traits beyond the core function. It doesn't clarify if this is a read operation, what the response format is, or any side effects. The mention of a Transformer model is a minor detail but not a behavioral disclosure.

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?

A single, focused sentence that delivers the core purpose without unnecessary words. Perfectly sized.

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 is minimal. It explains the basic function but doesn't cover return values, error conditions, or how recommendations are generated. Acceptable for a simple tool but not comprehensive.

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 provides full descriptions for both 'basket' and 'k' (100% coverage). The description adds no additional parameter semantics beyond restating the basket concept.

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 clear verb ('Obtient') and resource ('recommandations de produits'), and specifies the input context ('panier'). It clearly distinguishes from sibling tools like search_products 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 usage context is implied by 'basées sur un panier' — it should be used when the agent has a basket and wants recommendations. No explicit alternatives or exclusions are mentioned, but sibling tools are clearly different.

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 burden of behavioral disclosure. It reveals valuable behavior: coverage represents the proportion of search terms contained in each product, and reference_exacte indicates whether the search matched a product reference exactly. It also documents the side-effect of filters on reference_exacte. However, it does not describe the full response shape, pagination behavior, or error conditions, leaving meaningful 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 two short, front-loaded sentences with no filler. The first sentence establishes the core purpose, and the second explains the distinctive return semantics. Every word contributes, making it appropriately sized for a search tool.

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 tool with nested filters, pagination, and no output schema, the description is adequate but not complete. It explains the key response fields but omits the full result structure, pagination defaults and limits, and any guidance on how store_id affects pricing context. These omissions are more significant because there are no annotations or output schema to fill the 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 the baseline is 3. The description adds semantic meaning beyond the schema: it explains how coverage is computed, what reference_exacte means, and that using filters forces reference_exacte to always be false. This extra context goes beyond what the schema already documents.

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: 'Recherche de produits dans le catalogue' – a specific verb and resource. It goes beyond the name by explaining the coverage field and reference_exacte flag, which helps distinguish it from sibling tools like get_product and get_recommendations, though it does not explicitly name those 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 implies this tool is for searching products by term or exact reference, but it does not explicitly say when to use it over get_product, check_stock, or get_recommendations. It does provide useful usage context, especially the warning that using filters disables exact-reference matching, but there is no when-not-to-use guidance.

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