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 states the tool returns a dictionary with document summaries, which is helpful, but doesn't mention important behavioral aspects like whether this is a read-only operation, if there are rate limits, authentication requirements, error conditions, or what happens with invalid categories. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 appropriately sized and front-loaded with the core purpose in the first sentence. The additional sentences about returns and parameters are useful but could be slightly more streamlined. There's minimal waste, though the mention of 'ctx' (not in schema) adds minor confusion.

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 an output schema (which handles return values), one parameter with low schema coverage, and no annotations, the description does a reasonably complete job. It explains the purpose, parameter semantics, and return format. However, it lacks behavioral context (like safety or limitations) and usage guidelines relative to siblings, which are important for a tool in this 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?

The schema has 0% description coverage, so the description must compensate. It provides the parameter 'category' with example values (agents, mcp, infrastructure), which adds meaningful context beyond the bare schema. However, it doesn't explain what these categories represent or if there are other valid values, preventing a perfect score. The 'ctx' parameter mentioned in the description isn't in the schema, which is confusing but doesn't severely impact the score since the schema defines the actual parameters.

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 a summary of all standards in a category' and specifies it returns 'titles and descriptions of all documents in the specified category for quick reference.' This is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'list_standards_categories' or 'search_standards', 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 mentions the category parameter values (agents, mcp, infrastructure), but doesn't explain when to choose this tool over 'list_standards_categories' (which might list categories) or 'search_standards' (which might search within standards). There's no mention of prerequisites, exclusions, or specific use cases.

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. It discloses that the tool returns a list with descriptions and document counts, which adds behavioral context beyond a simple list. However, it lacks details on pagination, rate limits, authentication needs, or error handling, which are important for a read operation with potential data volume.

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, starting with the core purpose. The additional sentences about returns and args/returns are useful but could be slightly more integrated. There's minimal waste, though the 'Args: ctx' and 'Returns:' sections are somewhat redundant given the structured fields.

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, read-only operation), the description is reasonably complete. It explains what the tool does and what it returns, and with an output schema present, it doesn't need to detail return values. However, for a tool with siblings, more guidance on differentiation would enhance 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description correctly omits parameter details, focusing on the tool's purpose and output. This aligns well with the schema, earning a high score as it doesn't introduce unnecessary complexity.

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 ('List') and resource ('standards categories'), specifying it returns 'all top-level categories in the standards documentation with descriptions and document counts'. It distinguishes from siblings like 'search_standards' by focusing on listing categories rather than searching content. However, it doesn't explicitly contrast with 'get_standards_summary', which might be a similar listing operation.

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 retrieving top-level categories, but provides no explicit guidance on when to use this tool versus alternatives like 'search_standards' or 'get_standards_summary'. It mentions 'all top-level categories' which suggests a broad listing, but doesn't state exclusions or prerequisites, leaving usage context somewhat vague.

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 tool 'returns relevant excerpts with context' and includes an example, but fails to describe critical behaviors such as search algorithm details, pagination, rate limits, authentication requirements, or error handling. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.

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 a purpose statement, parameter explanations, return value note, and example. It's appropriately sized and front-loaded with the core functionality. The example is helpful but could be slightly more concise; overall, most sentences earn their place without significant 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 3 parameters with 0% schema coverage and no annotations, the description does a decent job explaining parameters and purpose. However, it lacks details on behavioral aspects like search scope, result formatting, or error conditions. The presence of an output schema helps, but the description doesn't fully compensate for the missing behavioral context, making it adequate but with clear 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 description coverage is 0%, so the description must compensate. It provides clear semantics for all three parameters: 'query' as search keywords/phrases, 'category' as an optional filter with specific values (agents, mcp, infrastructure), and 'max_results' with its default. This adds substantial meaning beyond the bare schema, though it doesn't detail format constraints or advanced query syntax.

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 performs a 'full-text search across all standards documents and returns relevant excerpts with context', which is a specific verb+resource combination. It distinguishes itself from siblings like 'get_standards_summary' or 'list_standards_categories' by focusing on search functionality rather than listing or summarizing. However, it doesn't explicitly contrast with 'validate_*' tools, which serve different purposes.

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 searching standards documents, but provides no explicit guidance on when to use this tool versus alternatives like 'get_standards_summary' or 'list_standards_categories'. It mentions optional parameters like 'category' filtering, which hints at context, but lacks clear when/when-not statements or named alternatives for different search 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool validates against standards and returns a ValidationResult, but lacks details on permissions required, rate limits, error handling, or whether it's a read-only operation. For a validation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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: the first sentence states the core purpose, followed by additional details and parameter/return explanations. It avoids unnecessary fluff, though the 'Args:' and 'Returns:' sections could be integrated more seamlessly. Overall, it's efficient with minimal 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 moderate complexity (validation against standards), no annotations, and an output schema (ValidationResult), the description is partially complete. It covers the purpose and basic parameters but lacks behavioral context, usage guidelines, and detailed parameter semantics. The presence of an output schema reduces the need to explain return values, but other gaps remain, making it adequate but with clear room for improvement.

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 minimal parameter semantics: it explains that 'agent_card' is 'The agent card JSON to validate' and mentions 'ctx: MCP context'. With 0% schema description coverage and 1 parameter, this provides some clarification beyond the bare schema, but doesn't detail JSON structure requirements, validation rules, or context usage. The baseline is 3 due to low parameter count, but the description doesn't fully compensate for the lack of schema documentation.

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: 'Validate an Agent Card against A2A protocol standards' and 'Checks if the agent card follows the Darwin platform A2A standards and contains all required fields.' This specifies the verb (validate/check), resource (agent card), and standard (A2A protocol/Darwin platform). However, it doesn't explicitly differentiate from sibling tools like 'validate_mcp_tool_definition' or 'validate_azure_resource_name' beyond mentioning the specific resource type.

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 'validate_mcp_tool_definition' or 'validate_azure_resource_name', nor does it specify prerequisites, exclusions, or appropriate contexts for validation. The agent must infer usage based solely on the tool name and description without explicit 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 full burden. It discloses that the tool performs validation and returns a ValidationResult with issues found, which is useful behavioral context. However, it doesn't mention error handling, performance characteristics, rate limits, or authentication requirements that might be relevant for a validation tool.

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 purpose statement, parameter explanations, return value description, and a clear example. Every sentence earns its place, though the example could be slightly more concise. The information is front-loaded with the core purpose 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 an output schema (which handles return values), 2 parameters with 0% schema coverage, and no annotations, the description does a good job explaining what the tool does, what parameters mean, and what to expect. The example provides concrete usage. It could be more complete by explaining the ValidationResult structure or providing more context about the Darwin platform.

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 0%, so the description must compensate. It provides clear explanations for both parameters: 'resource_name' as 'The resource name to validate' and 'resource_type' as 'Type of Azure resource' with examples. This adds meaningful context beyond the bare schema, though it could provide more guidance on valid resource_type values.

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 verb ('validate') and resource ('Azure resource name'), and distinguishes it from siblings by focusing on naming conventions for Azure resources. It explicitly mentions checking against 'Darwin platform naming conventions for Azure resources', which provides domain-specific context.

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 validating Azure resource names against naming conventions, but doesn't explicitly state when to use this tool versus alternatives or when not to use it. No comparison is made with sibling tools like 'validate_agent_card' or 'validate_mcp_tool_definition', leaving the agent to infer appropriate contexts.

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 states the tool performs validation and returns a 'ValidationResult with any issues found,' which implies a read-only, non-destructive operation. However, it lacks details on permissions required, error handling, rate limits, or what constitutes a valid 'MCP context' in the parameters. For a validation tool with zero 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 well-structured with a purpose statement, bullet-point-like 'Args' and 'Returns' sections, and uses clear, efficient language. It avoids redundancy and is appropriately sized for a validation tool. The front-loaded purpose statement helps, though the formatting could be more polished (e.g., using proper markdown).

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 (validation with 3 parameters, nested objects in schema, and an output schema), the description is moderately complete. It explains parameters and the return value, and an output schema exists, so it needn't detail return values further. However, with no annotations and 0% schema coverage, it lacks behavioral context like error cases or validation standards specifics, making it adequate but with clear gaps for a tool of this nature.

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 0%, so the schema provides no parameter descriptions. The description includes an 'Args' section listing parameters: 'tool_name', 'tool_description', 'parameters', and 'ctx', and a 'Returns' section explaining the output. This adds meaningful semantics beyond the bare schema, covering all 3 required parameters and the output. However, it doesn't detail parameter formats (e.g., JSON Schema specifics for 'parameters') or context requirements, leaving some ambiguity.

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: 'Validate an MCP tool definition against standards' and specifies 'Checks if the tool follows Darwin platform MCP tool design standards.' This provides a specific verb ('validate') and resource ('MCP tool definition'), though it doesn't explicitly differentiate from sibling tools like 'validate_agent_card' or 'validate_azure_resource_name' which validate different types of resources.

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_standards_summary', 'list_standards_categories', or 'search_standards' that might be related to standards, nor does it specify prerequisites or contexts for validation. Usage is implied only by the tool's name and purpose statement.

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