123elec-mcp

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 states the action ('vérifie') but doesn't describe what 'checking stock' entails operationally—whether it's a read-only query, requires authentication, has rate limits, returns real-time vs cached data, or what happens on errors. The description is minimal and lacks behavioral context.

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, efficient sentence in French ('Vérifie le stock de plusieurs produits') with zero waste. It's appropriately sized and front-loaded, clearly stating the core function without unnecessary elaboration.

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 complexity (a read operation with one parameter) and lack of annotations/output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., read-only nature, error handling) or output details (e.g., stock levels, formats), leaving gaps for an agent to infer usage correctly.

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%, with the parameter 'skus' documented as 'Liste des SKUs'. The description adds no additional meaning beyond this, as it doesn't explain SKU format, constraints (e.g., max items), or how stock is returned. Baseline 3 is appropriate since the schema does 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 'Vérifie le stock de plusieurs produits' clearly states the tool's purpose (checking stock for multiple products) with a specific verb ('vérifie') and resource ('stock de plusieurs produits'). It distinguishes from siblings like get_product (single product) and search_products (searching), but doesn't explicitly contrast with get_categories or get_recommendations.

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. It doesn't mention prerequisites, when-not-to-use scenarios, or compare with sibling tools like get_product (for single products) or search_products (which might include stock info). Usage is implied but not articulated.

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. While 'Liste' implies a read operation, it doesn't specify whether this is a safe, read-only function, potential side effects, authentication needs, rate limits, or return format. The description adds minimal behavioral context beyond the basic action.

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, efficient phrase ('Liste hiérarchique des catégories') that conveys the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with key information, making it highly concise and well-structured.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It specifies the hierarchical nature of categories, which adds value, but lacks details on behavioral traits, usage context, or output format. For a read-only list tool, this is minimally viable but not fully 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 tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, as there are none to document. A baseline of 4 is appropriate since no parameters exist, and the description doesn't mislead about inputs.

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 'Liste hiérarchique des catégories' clearly states the tool's purpose as listing hierarchical categories. It uses a specific verb ('Liste') and resource ('catégories'), and the adjective 'hiérarchique' adds useful detail about the structure. However, it doesn't explicitly distinguish this from sibling tools like 'get_product' or 'search_products', 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. It doesn't mention sibling tools like 'get_product' or 'search_products', nor does it specify contexts where listing categories is appropriate (e.g., for navigation vs. product lookup). This leaves the agent without explicit usage instructions.

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 states the tool retrieves 'complete details' but doesn't specify what those details include, whether it's a read-only operation, potential error conditions, authentication requirements, or performance characteristics. The description is too vague to provide meaningful behavioral context beyond the basic purpose.

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 extremely concise - a single French phrase that directly states the tool's purpose. There's no wasted language, repetition, or unnecessary elaboration. Every word earns its place by communicating the essential function of the 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?

Given that this is a data retrieval tool with no annotations and no output schema, the description is insufficiently complete. While it states the purpose, it doesn't describe what 'complete details' actually includes, the format of the response, potential error cases, or how this differs from related tools. For a tool that presumably returns structured product data, more context would be helpful for the agent.

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 parameter 'sku' clearly documented as 'SKU du produit.' The description adds no additional parameter information beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 'Détails complets d'un produit par SKU' clearly states the tool's purpose: retrieving complete product details using a SKU identifier. It specifies both the action ('détails complets') and the resource ('produit'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'search_products' or 'get_recommendations' beyond the SKU-based lookup approach.

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. It doesn't mention when this tool is appropriate (e.g., for known SKUs) versus when to use 'search_products' (for broader queries) or 'check_stock' (for inventory status). There's no context about prerequisites, limitations, or typical use cases, leaving the agent with insufficient information to make informed decisions.

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 mentions the model ('Transformer WattHelper'), which hints at AI-based recommendations, but doesn't describe key behaviors such as response format (e.g., list of SKUs with scores), potential latency, rate limits, or error conditions. For a tool with no annotations, this is a significant gap in 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 a single, efficient sentence that directly states the tool's purpose and includes the model name for added context. It's front-loaded with the core function and avoids unnecessary details. However, it could be slightly more structured by separating usage hints, but it's concise and to the point.

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 complexity of a recommendation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a list of recommended products with metadata), how recommendations are generated, or any prerequisites (e.g., valid SKUs). For a tool that likely returns structured data, this leaves critical gaps for an agent to use it 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?

Schema description coverage is 100%, with clear descriptions for both parameters: 'k' as the number of recommendations and 'basket' as a list of SKUs. The description adds minimal value beyond this, only implying that recommendations are based on the basket. It doesn't provide additional semantics like basket format examples or how the model uses the SKUs. Baseline 3 is appropriate since the schema does 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: 'Obtient des recommandations de produits basées sur un panier' (Gets product recommendations based on a basket). It specifies the verb ('obtient') and resource ('recommandations de produits'), and mentions the model ('modèle Transformer WattHelper'), which adds specificity. However, it doesn't explicitly differentiate from sibling tools like 'search_products' or 'get_product', which could also be used for product-related queries, so it doesn't reach a 5.

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. It doesn't mention scenarios like when a user has items in a basket and wants suggestions, or contrast it with siblings such as 'search_products' for general queries or 'get_product' for specific product details. Without any context on usage, it leaves the agent to infer based on the name and parameters 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'pagination' but doesn't detail how pagination works (e.g., response format, limits, or error handling). It also omits other behavioral traits like rate limits, authentication needs, or whether it's read-only (implied but not stated). For a search tool with no annotation coverage, this is a significant gap in 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 a single, efficient sentence in French that directly states the tool's core functionality. It's front-loaded with essential information ('Recherche de produits par mot-clé avec pagination') and contains no unnecessary words or redundancy, making it highly concise and well-structured.

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 (search with pagination), no annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and hints at pagination but lacks details on behavioral traits, output format, and usage context. This makes it minimally viable but with clear gaps that could hinder effective tool selection and 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 has 100% description coverage, with clear documentation for all three parameters ('search', 'pageSize', 'currentPage'), including defaults and constraints. The description adds minimal value beyond the schema, only implying keyword-based search and pagination without providing additional semantic context. This meets the baseline score of 3 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 tool's purpose: 'Recherche de produits par mot-clé avec pagination' (Search products by keyword with pagination). It specifies the verb ('Recherche'), resource ('produits'), and key functionality ('par mot-clé avec pagination'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_product' or 'get_recommendations', which prevents a score of 5.

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. It doesn't mention sibling tools like 'get_product' (for retrieving a specific product) or 'get_recommendations' (which might offer different search logic), nor does it specify prerequisites or exclusions. This lack of contextual usage information leaves the agent to infer when this tool is appropriate.

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