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 describes what aspects are compared but doesn't cover behavioral traits such as performance characteristics, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding how the tool behaves operationally.

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 and efficiently lists comparison aspects in a single sentence, followed by a second sentence on suitability explanation. It avoids redundancy, though it could be slightly more concise by integrating the two sentences.

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 no annotations and no output schema, the description adequately covers the tool's purpose and parameters but lacks details on behavioral traits and output format. For a comparison tool with 3 parameters and 100% schema coverage, it's minimally viable but incomplete in providing full operational context.

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 value by listing the comparison aspects (e.g., datum, projection method) that map to the 'aspects' parameter, but doesn't provide additional semantic context beyond what's in the schema. Baseline 3 is appropriate when 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 verb 'compare' and the resource 'two CRS', specifying the comparison covers multiple perspectives including datum, projection method, area of use, accuracy, distortion characteristics, compatibility, and use case suitability. It distinguishes from siblings like get_crs_detail (single CRS detail) and recommend_crs (recommendations rather than comparison).

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 when to use this tool by stating it 'explains which is better suited for specific purposes,' suggesting it's for comparative analysis. However, it doesn't explicitly state when not to use it or name alternatives like get_crs_detail for single CRS information or recommend_crs for recommendations without detailed comparison.

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 mentions what the tool provides ('recommended practices, common mistakes, and reference materials') but lacks behavioral details such as response format, error handling, rate limits, or authentication requirements. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 appropriately sized and front-loaded with the core purpose. It efficiently lists topics and what's provided in a single sentence. There's no wasted text, though it could be slightly more structured for clarity.

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 no annotations and no output schema, the description is moderately complete for a read-only tool. It covers the purpose and topics but lacks details on return values, error cases, or operational constraints. It's adequate but has clear gaps in behavioral context.

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 both parameters thoroughly. The description adds minimal value beyond the schema by listing topic areas but doesn't provide additional semantics like parameter interactions or usage examples. Baseline 3 is appropriate as 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: 'Get CRS best practices for specific topics' with a specific verb ('Get') and resource ('CRS best practices'). It lists the topics covered, which helps distinguish it from sibling tools like 'get_crs_detail' or 'recommend_crs', though it doesn't explicitly differentiate from them. The purpose is specific but lacks direct sibling comparison.

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 lists topics covered but doesn't mention when to choose it over sibling tools like 'recommend_crs' or 'troubleshoot'. There are no explicit usage scenarios, prerequisites, or exclusions 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what information is returned but doesn't cover critical aspects like whether this is a read-only operation, potential error conditions (e.g., invalid codes), performance characteristics, or authentication needs. For a tool with zero 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 appropriately concise and front-loaded, stating the core purpose in the first sentence and listing included details efficiently. Every sentence adds value by specifying the scope of information retrieved. Minor improvement could be made by structuring it more explicitly, but it's largely waste-free.

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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers what the tool does but lacks completeness in behavioral aspects and usage context. For a read operation with good schema coverage, this is acceptable but leaves room for improvement in guiding the agent 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?

The description adds no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100% (the 'code' parameter is well-documented in the schema), the baseline score is 3. The description doesn't compensate with additional context like format examples or usage tips, but it doesn't need to given the schema's completeness.

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 ('Get detailed information') and resource ('for a specific EPSG code'), and lists the types of information included (datum, projection method, area of use, accuracy characteristics, intended use cases). However, it doesn't explicitly differentiate from sibling tools like 'search_crs' or 'list_crs_by_region' that might also retrieve CRS information, 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 prerequisites, exclusions, or compare it to sibling tools such as 'search_crs' for broader queries or 'validate_crs_usage' for validation purposes. This lack of contextual usage information leaves the agent without clear direction.

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 mentions 'purpose-based recommendations' which suggests some advisory functionality, but doesn't clarify whether this is a read-only operation, what permissions might be required, how results are structured, or any rate limits. For a tool with 3 parameters and no annotation coverage, this leaves significant behavioral 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 efficiently structured in two sentences. The first sentence states the core purpose, and the second provides helpful region-specific examples. There's no wasted text, and the information is front-loaded with the main functionality stated first.

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 has 3 parameters with 100% schema coverage but no annotations and no output schema, the description provides adequate basic context about what the tool does and some region examples. However, for a tool that apparently provides 'purpose-based recommendations' (suggesting some complexity), the description doesn't explain what form these recommendations take or how results are structured, leaving gaps in completeness.

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 some context by mentioning specific CRS examples for Japan and Global regions, which relates to the 'region' parameter, but doesn't provide additional semantic meaning beyond what's in the schema descriptions. 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: 'Get available CRS list for a region with purpose-based recommendations.' It specifies the verb ('Get'), resource ('CRS list'), and scope ('for a region'), and provides examples for Japan and Global regions. However, it doesn't explicitly differentiate from sibling tools like 'search_crs' or 'recommend_crs' in terms of functionality boundaries.

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 context by mentioning 'purpose-based recommendations' and providing region-specific examples (Japan includes Plane Rectangular CS, Global includes WGS84 and UTM zones). However, it doesn't explicitly state when to use this tool versus alternatives like 'search_crs' or 'recommend_crs', nor does it mention any exclusions or prerequisites for usage.

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 full burden for behavioral disclosure. It mentions support for specific use cases and regional systems, but does not disclose critical traits such as whether this is a read-only operation, if it requires authentication, potential rate limits, or what the output format might be (e.g., a recommended CRS code or detailed analysis). For a recommendation tool with complex inputs, 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 appropriately sized with two sentences: the first states the core purpose and use cases, the second adds regional specificity. It is front-loaded with the main function, and each sentence adds value (e.g., listing supported purposes and Japan-specific zones). Minor room for improvement in structuring the use-case list more clearly, but overall efficient with zero waste.

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 (3 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It lacks information on output format (what the recommendation looks like), behavioral constraints (e.g., is it deterministic or heuristic-based), and how to interpret results relative to sibling tools. Without annotations or output schema, the description should compensate more to guide the agent 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%, so the schema already documents all parameters thoroughly. The description adds no specific parameter semantics beyond what's in the schema—it does not explain how 'purpose' influences recommendations, how 'location' details are prioritized, or how 'requirements' affect the output. Baseline 3 is appropriate as the schema does the heavy lifting, but the description fails to add meaningful context.

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: 'Recommend the optimal CRS based on purpose and location.' It specifies the verb ('Recommend') and resource ('optimal CRS'), and distinguishes from siblings by focusing on recommendation rather than comparison, listing, validation, or other operations. The mention of specific use cases (web mapping, distance calculation, etc.) and regional support (Japan Plane Rectangular CS) further clarifies scope.

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 examples ('Supports web mapping, distance/area calculation...') and regional specificity ('Full support for Japan Plane Rectangular CS...'), suggesting it's for CRS selection tasks. However, it lacks explicit guidance on when to use this tool versus alternatives like 'compare_crs' or 'search_crs', and does not mention prerequisites or exclusions, leaving the agent to infer context from the input schema.

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 adds useful context about search scope (covers Japanese JGD2011, global WGS84, Web Mercator) and searchable fields, but does not disclose critical behavioral traits such as pagination behavior, rate limits, authentication needs, or what happens with no results. The description provides some operational context but leaves gaps in behavioral 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 appropriately sized and front-loaded with the core purpose in the first sentence. Every sentence adds value: the first defines the tool, the second specifies searchable fields, and the third provides coverage context. There is zero waste, and the structure efficiently conveys essential 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 tool's moderate complexity (4 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, usage context, and parameter hints, but lacks details on output format, error handling, or behavioral constraints. With no output schema, the description could benefit from mentioning what results look like, but it adequately supports basic 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by mentioning searchable fields (EPSG code, name, region name, prefecture name) and coverage examples, but does not provide additional syntax, format details, or usage examples beyond what the schema provides. 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 verb ('Search') and resource ('EPSG Coordinate Reference Systems (CRS)'), and distinguishes from siblings by specifying keyword-based search functionality. It explicitly mentions what can be searched (EPSG code, name, region name, prefecture name) and provides coverage examples (JGD2011, WGS84, Web Mercator), making the purpose 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 clear context for when to use this tool (searching CRS by keyword) and implies alternatives by mentioning coverage of specific CRS families, but does not explicitly name sibling tools like 'list_crs_by_region' or 'get_crs_detail' as alternatives. It gives guidance on searchable fields but lacks explicit when-not-to-use statements or direct comparisons to other tools.

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 full burden. It discloses key behavioral traits: it searches multi-step paths, provides accuracy information, and warns about cumulative errors. However, it doesn't mention authentication requirements, rate limits, error handling, or what format the suggestions come in. The behavioral disclosure is adequate but incomplete for a tool with complex transformation logic.

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 front-loaded with the core purpose in the first sentence, followed by specific examples and additional capabilities. Every sentence adds value: the examples clarify scope, and the subsequent sentences explain the search behavior and output characteristics. Zero wasted words.

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 3 parameters (including a complex nested object), no annotations, and no output schema, the description provides good purpose and usage context but lacks details about the output format, error conditions, or performance characteristics. Given the complexity of coordinate reference system transformations, more behavioral context would be helpful despite the excellent parameter documentation in the schema.

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 meaningful context by explaining that the tool handles specific transformation examples (Tokyo Datum to JGD2011, WGS84 to Plane Rectangular CS) and that location is 'for accuracy improvement', which provides valuable semantic context beyond the schema's technical descriptions.

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 specific verbs ('suggest transformation paths', 'searches multi-step paths', 'provides accuracy info', 'warns about cumulative errors') and resources ('between two CRS'). It explicitly distinguishes its functionality from siblings by focusing on transformation pathfinding rather than comparison, listing, validation, or troubleshooting.

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 ('between two CRS' with examples like 'Tokyo Datum to JGD2011'), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. The examples help guide usage, but no explicit exclusions or comparisons to siblings are 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool 'diagnoses', 'identifies causes', and 'provides diagnostic steps and solutions', which suggests it's a read-only advisory tool. However, it doesn't disclose important behavioral traits like whether it performs actual system checks, requires specific permissions, has rate limits, or what format the solutions take (text, links, code snippets).

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 efficiently structured in two sentences: the first states the core purpose with specific examples, the second explains the diagnostic process. Every element earns its place, though it could be slightly more front-loaded by leading with 'Diagnoses CRS-related problems' rather than 'Troubleshoot CRS-related problems.'

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 diagnostic tool with no annotations and no output schema, the description provides adequate purpose and scope but lacks completeness. It doesn't describe what the output will contain (beyond 'diagnostic steps and solutions'), doesn't explain the tool's limitations, and doesn't address how the context object parameters relate to the troubleshooting process. Given the complexity of CRS troubleshooting, more behavioral context would be helpful.

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 both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it mentions symptoms like 'coordinate shifts' and 'calculation errors' which align with the schema's symptom parameter examples, but provides no additional semantic context about how parameters should be used together or special considerations.

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 specific verbs ('troubleshoot', 'diagnoses', 'identifies', 'provides') and resources ('CRS-related problems'), listing concrete examples like coordinate shifts and calculation errors. It distinguishes itself from siblings like compare_crs or validate_crs_usage by focusing on diagnostic problem-solving rather than comparison or validation.

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 for CRS-related troubleshooting scenarios through examples, but doesn't explicitly state when to use this tool versus alternatives like suggest_transformation or get_best_practices. No clear exclusions or prerequisites are provided, leaving the agent to infer context from the symptom examples.

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 full burden. It mentions the tool 'detects' issues and 'provides improvement suggestions', which implies a read-only analysis function, but does not disclose behavioral traits like error handling, performance characteristics, rate limits, or authentication needs. For a validation tool with complex location input, this leaves significant gaps in understanding how it behaves.

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 efficiently structured in two sentences: the first states the core function, and the second enumerates detection capabilities. It is front-loaded with the primary purpose and avoids redundancy. However, it could be slightly more concise by integrating the detection list more seamlessly.

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 (3 parameters with nested objects, no output schema, and no annotations), the description is moderately complete. It covers the what and why of validation but lacks details on output format, error cases, or limitations. Without annotations or output schema, the agent has insufficient information to fully understand the tool's behavior and results.

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%, providing detailed documentation for all parameters. The description adds marginal value by contextualizing the parameters ('for a specific purpose and location') and hinting at the validation logic (e.g., distortion issues relate to 'purpose'), but does not explain parameter interactions or provide syntax examples beyond what the schema already specifies.

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 ('validate whether a CRS is appropriate'), the resource ('a CRS'), and the scope ('for a specific purpose and location'). It distinguishes from siblings by focusing on validation rather than comparison, recommendation, or listing, and explicitly lists the types of issues it detects (deprecated usage, distortion, zone selection).

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 context through the listed detection capabilities (e.g., 'inappropriate zone selection for surveying'), but does not explicitly state when to use this tool versus alternatives like 'recommend_crs' or 'compare_crs'. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate scenarios from the purpose parameter's enum values.

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