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

Annotations already provide strong behavioral hints (readOnlyHint: true, openWorldHint: true, idempotentHint: true, destructiveHint: false). The description adds valuable context by specifying the data source (IGN Admin Express dataset) and clarifying the return format (GeoJSON FeatureCollection). While it doesn't mention rate limits or authentication needs, it provides useful behavioral information beyond what annotations offer.

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 perfectly structured and front-loaded: purpose statement first, then data source, followed by parameter guidance, return format, and examples. Every sentence earns its place, with no redundant information. The examples are particularly efficient at demonstrating usage patterns.

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 query tool with comprehensive annotations (readOnly, openWorld, idempotent) and 100% schema coverage, the description provides excellent context. It explains the data source, clarifies parameter dependencies, specifies the return format (GeoJSON FeatureCollection), and includes practical examples. The absence of an output schema is compensated by the clear return format description.

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?

With 100% schema description coverage, the baseline is 3. The description adds significant value by explaining parameter relationships ('Either provide geom OR (lon + lat), but not both') and providing concrete examples showing how parameters work together. It also clarifies the purpose of the 'type' parameter by listing the three boundary types, though this information is already in the schema's enum.

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: 'Query French administrative boundaries (communes, departments, regions)' and specifies it accesses data from the IGN Admin Express dataset. This distinguishes it from sibling tools like ign_get_communes_by_postal_code or ign_get_cadastre_communes by focusing specifically on administrative boundaries rather than postal codes or cadastral data.

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 guidance: 'Either provide geom OR (lon + lat), but not both' and includes three concrete examples showing when to use different parameter combinations. The examples clearly demonstrate different use cases (point queries vs. intersection queries) with specific parameter configurations.

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?

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond this: it discloses the data source (FranceAgriMer based on INAO data), notes that an IGN API key is required (with a link to obtain one), and specifies the return format (GeoJSON FeatureCollection with specific attributes). This enriches the agent's understanding of authentication needs and output structure.

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 and appropriately sized. It starts with a clear purpose statement, followed by data source info, a concise Args section, Returns details, practical examples, and a necessary note about API key requirements. Each sentence adds value without redundancy, making it efficient and easy to parse.

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 (spatial query with authentication), the description is complete. It covers purpose, data source, parameters (with schema doing heavy lifting), return format, examples, and authentication requirements. Although there is no output schema, the description adequately explains the GeoJSON FeatureCollection structure, making it sufficient for agent use.

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 minimal value beyond the schema: it briefly mentions the 'geom' parameter as 'GeoJSON geometry to intersect' and notes the API key requirement, but does not provide additional syntax or format details. This meets the baseline for 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's purpose: 'Query wine appellation zones (AOC, IGP, VSIG) in France.' It specifies the exact resource (wine appellations), geographic scope (France), and types (AOC/IGP/VSIG). This distinguishes it from sibling tools that handle administrative limits, cadastre, communes, etc., making the purpose specific and well-differentiated.

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 this tool through examples ('What wine appellations cover this vineyard?' and 'Find AOC zones in Bordeaux region'), which implicitly guides usage for spatial queries about wine appellations. However, it does not explicitly state when NOT to use it or name alternatives among sibling tools, though the examples help infer appropriate 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?

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable context about the return format ('GeoJSON FeatureCollection'), pagination behavior (through _limit and _start parameters), and the default response format (implied through the Returns section), which goes beyond what annotations provide.

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 with clear sections (purpose, Args, Returns) and uses bullet points for readability. It's appropriately sized at 5 sentences, though the Args section could be more concise by integrating parameter descriptions rather than listing them separately.

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 (6 parameters, no output schema), the description provides good coverage: clear purpose, parameter listing, return format specification, and pagination context. With annotations covering safety and idempotency, and the description adding return format details, it's mostly complete though could benefit from more usage guidance relative to siblings.

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 67% (4 of 6 parameters have descriptions). The description adds minimal parameter semantics beyond the schema - it lists parameters but doesn't provide additional meaning about how they interact (e.g., that geom and code_insee are alternative filtering methods). The schema already documents geom, _limit, _start, and response_format well, 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 clearly states the specific action ('Get commune boundaries') and resource ('from the cadastral database'), distinguishing it from siblings like 'ign_get_administrative_limits' or 'ign_get_communes_by_postal_code' by specifying it's cadastral data rather than administrative or postal-based data.

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 through parameter descriptions (e.g., using geom for intersection, code_insee for specific communes), but doesn't explicitly state when to use this tool versus alternatives like 'ign_get_administrative_limits' or 'ign_get_communes_by_postal_code'. No explicit when-not or alternative guidance is provided.

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?

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable context about the API source ('IGN API Carto cadastre module'), data source options with a recommendation ('pci' recommended), and pagination behavior (via _limit and _start parameters). It doesn't contradict annotations and enhances understanding of the tool's operational 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 well-structured with clear sections (purpose, usage context, Args, Returns, Examples), each sentence adds value, and there's no redundancy. It efficiently conveys necessary information without verbosity, making it easy to scan and understand.

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 (10 parameters, geographic data querying), the description provides comprehensive context: purpose, usage scenarios, parameter explanations with examples, return format details (GeoJSON FeatureCollection with specific properties), and data source guidance. Even without an output schema, the Returns section adequately describes the response structure and content.

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?

With 100% schema description coverage, the schema already documents all parameters thoroughly. The description adds meaningful context by grouping parameters logically (geometry intersection vs. administrative codes), providing real-world examples that map use cases to parameter combinations, and explaining the Returns section in detail with property descriptions. This goes beyond the schema's technical definitions to show how parameters work together.

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 specific action ('search for cadastral parcels'), resource ('in France'), and method ('queries the IGN API Carto cadastre module'). It distinguishes this tool from siblings by specifying it's for parcels rather than administrative limits, communes, or other geographic features, making its purpose unambiguous and differentiated.

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 states when to use this tool ('useful for property identification, urban planning, and administrative procedures') and provides concrete examples with parameter mappings (e.g., 'Find parcels in commune 75101' -> code_insee='75101'). This gives clear guidance on appropriate use cases and how to structure 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable context beyond annotations: it specifies the API source (IGN API Carto codes-postaux module), explains the French postal code system behavior, and describes the return format options. No contradiction with annotations exists.

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 and appropriately sized. It starts with a clear purpose statement, adds necessary context about the French postal system, then provides structured sections for Args, Returns, and Examples. 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?

For a read-only query tool with comprehensive annotations and 100% schema coverage, the description provides excellent completeness. It explains the tool's purpose, source API, French postal code nuance, parameter usage with examples, and return format details. The lack of an output schema is compensated by the detailed Returns section showing the JSON structure.

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?

With 100% schema description coverage, the schema already fully documents both parameters (code_postal pattern and response_format enum with defaults). The description adds minimal value beyond the schema: it provides example postal codes ('75001', '69000') and clarifies that response_format controls output format. This meets the baseline 3 for 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 specific action ('Retrieve French communes associated with a postal code'), identifies the resource ('communes/municipalities'), and distinguishes from siblings by specifying it queries the 'Carto codes-postaux module' for postal code to commune mapping. It explicitly mentions the French context where one postal code can cover multiple communes, which differentiates it from tools like administrative limits or cadastre queries.

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 about when to use this tool: for finding all communes that share a given French postal code. It explains the French postal system nuance (one code can cover multiple communes). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different types of geographic 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?

Annotations already cover key traits (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds valuable context beyond annotations by listing available layers (e.g., municipality, zone-urba) and specifying the return type (GeoJSON FeatureCollection), which helps the agent understand data structure and scope. It does not mention rate limits or authentication needs, but with annotations provided, this is sufficient for a high score.

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 and appropriately sized, with clear sections (overview, available layers, args, returns, examples). Every sentence adds value, such as explaining data types or providing practical examples. It could be slightly more concise by integrating the layer list into the schema context, but overall it is efficient and front-loaded with key 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?

Given the complexity (7 parameters, 100% schema coverage, rich annotations), the description is complete enough. It covers purpose, data scope, parameters, return format, and usage examples. Although there is no output schema, the description specifies the return type (GeoJSON FeatureCollection), which compensates adequately. The annotations provide safety and behavioral context, making this description comprehensive for the tool's needs.

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 baseline is 3. The description adds minimal parameter semantics beyond the schema: it lists layer options and provides examples linking parameters to use cases (e.g., layer='municipality' for RNU checks). However, it does not explain parameter interactions or provide additional syntax details, staying close to 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: 'Query the Géoportail de l'Urbanisme (GPU) for urban planning documents and zones.' It specifies the exact resource (French urban planning data) and distinguishes from siblings by focusing on urban planning rather than administrative limits, cadastre, or other geographic data. The title reinforces this with 'Get urban planning data (GPU).'

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 this tool through examples (e.g., 'Is this commune under RNU?' or 'What's the zoning at this address?'), which implicitly guides usage. However, it does not explicitly state when to use alternatives or exclude specific scenarios, and it does not mention sibling tools like ign_get_cadastre_parcelles for comparison, leaving some guidance gaps.

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?

Annotations already cover read-only, open-world, idempotent, and non-destructive characteristics. The description adds valuable context about the specific geographic scope (France), available data layers with detailed explanations, and the return format (GeoJSON FeatureCollection). This goes beyond what annotations provide without contradicting them.

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 and front-loaded with the core purpose, followed by organized sections for layers, arguments, returns, and examples. Every sentence serves a clear purpose with zero wasted content, making it easy for an agent to parse and understand quickly.

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 (6 parameters, geographic queries) and rich annotations, the description provides good contextual coverage. It explains the geographic scope, available data layers, and return format. The main gap is the lack of an output schema, but the description compensates by specifying the return type as GeoJSON FeatureCollection.

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?

With 100% schema description coverage, the baseline is 3. The description adds minimal parameter semantics beyond the schema - it lists available layers with brief explanations and provides example usage patterns, but doesn't significantly enhance understanding of parameter interactions or edge cases beyond what's already documented 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 specific action ('Query protected natural areas in France') and resource types (Natura 2000, ZNIEFF, national parks, etc.). It distinguishes this tool from sibling tools by focusing exclusively on natural area data rather than administrative, cadastral, or other geographic data types.

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 implied usage through the examples showing when to use specific layer parameters, but lacks explicit guidance on when to choose this tool versus alternatives. No sibling tool comparisons or exclusion criteria are provided, leaving the agent to infer based on tool names 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?

The description adds valuable behavioral context beyond what annotations provide. While annotations already indicate read-only, open-world, idempotent, and non-destructive operations, the description explains the data source (RPG for CAP subsidies), version differences (V1 vs V2 with different data structures), and return format details (GeoJSON FeatureCollection with specific fields per version). This provides important operational context that annotations alone don't cover.

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 perfectly structured and concise. It starts with the core purpose, provides essential context about the RPG and version differences, then clearly lists parameters and returns with specific examples. Every sentence earns its place, and the information is front-loaded with the most important details first. The example at the end provides practical guidance 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?

Given the tool's complexity (6 parameters, version differences, geographic queries) and the absence of an output schema, the description provides excellent completeness. It explains the data source, version differences, parameter purposes, return format details, and includes a practical example. The combination of annotations and description gives the agent everything needed to use this tool effectively in the context of its sibling tools.

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?

With 100% schema description coverage, the baseline would be 3, but the description adds significant semantic value. It explains the significance of the 'annee' parameter in relation to data versions (V1 vs V2), provides context for 'code_cultu' as a crop culture code filter, and clarifies that 'geom' must be a GeoJSON geometry. The description also explains what the return data contains for each version, which helps understand parameter implications.

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: 'Query the Registre Parcellaire Graphique (RPG) for agricultural parcel information.' It specifies the exact resource (RPG agricultural land use data) and distinguishes it from siblings by focusing on agricultural parcels rather than administrative limits, cadastre, or other geographic data. The description provides context about CAP subsidies and version differences, making the purpose highly specific and differentiated.

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 guidance by explaining when to use different versions (V1 for 2010-2014, V2 for 2015+), which helps the agent select appropriate parameters. It also distinguishes this tool from siblings by focusing specifically on agricultural parcel data, unlike administrative or cadastral tools. The example at the end further clarifies typical usage 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?

The description adds valuable behavioral context beyond annotations: it specifies the coordinate system limitation ('Only WGS84 geometries are supported'), describes the return format ('GeoJSON FeatureCollection'), and provides practical examples. While annotations cover safety (readOnly, non-destructive), the description adds implementation details that help the agent use the tool correctly.

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 with clear sections (purpose, args, returns, notes, examples) and front-loads the core functionality. While comprehensive, some redundancy exists between the description and schema (e.g., parameter descriptions), making it slightly less concise than ideal.

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 query tool with comprehensive annotations (readOnly, openWorld, idempotent) and full schema coverage, the description provides excellent contextual completeness. It explains the tool's generic nature, coordinate system constraints, return format, and includes practical examples - giving the agent everything needed to use this tool 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?

With 100% schema description coverage, the baseline is 3. The description adds minimal additional parameter semantics beyond what's in the schema - it repeats the source and geom descriptions and provides examples, but doesn't significantly enhance understanding of parameter usage or relationships.

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 as a 'generic query interface for Geoportail WFS layers' that 'allows querying any WFS layer by intersection with a geometry.' It distinguishes itself from sibling tools by being generic rather than specific to particular datasets like administrative limits or cadastre parcels.

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 this tool ('querying any WFS layer by intersection with a geometry') and includes a note about coordinate system limitations. However, it doesn't explicitly state when NOT to use it or provide direct alternatives among the sibling tools for specific use cases.

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