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 retrieves data from browser history, implying a read-only operation, but does not specify permissions required, data privacy implications, rate limits, or error handling. The description adds basic context but lacks detailed behavioral traits beyond the core functionality.

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 clear sections for arguments and returns. Every sentence earns its place by providing essential information without redundancy. It is appropriately sized for a tool with two parameters and an output schema.

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 (2 parameters, no annotations, but with an output schema), the description is mostly complete. It covers purpose, parameters, and return format. However, it lacks details on behavioral aspects like permissions or errors, which are important for a tool accessing browser history. The output schema existence reduces the need to explain return values, but some gaps remain.

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 fully documents both parameters: 'limit' with its default and maximum values, and 'browser' with its enum values and default. This adds complete semantic meaning beyond the bare schema, effectively compensating for the lack of schema 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 specific action ('Get') and resource ('most frequently visited sites from browser history'), distinguishing it from sibling tools like 'get_recent_history' (which would presumably return recent visits rather than most frequent) and 'search_history' (which would search rather than aggregate by frequency). The verb+resource combination is precise and unambiguous.

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 specifying it retrieves 'most frequently visited sites,' which suggests aggregation by visit count rather than recency or search. However, it does not explicitly state when to use this tool versus alternatives like 'get_recent_history' or 'search_history,' nor does it provide exclusions or prerequisites. The context is clear but lacks explicit comparative guidance.

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 the tool 'gets' data (implying read-only), but doesn't address important behavioral aspects like authentication requirements, rate limits, privacy implications, or whether it accesses local browser data versus a centralized service. The description is minimal on 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 perfectly structured and concise. It starts with the core purpose, then provides clear parameter documentation in a well-organized format, and ends with return value information. Every sentence earns its place with no 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?

Given the tool's moderate complexity (2 parameters, read operation), the description is quite complete. It explains the purpose, documents both parameters thoroughly, and describes the return format. With an output schema present, the description doesn't need to detail return values extensively. The main gap is the lack of sibling tool differentiation.

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 provides excellent parameter semantics despite 0% schema description coverage. It clearly explains what 'limit' means ('Maximum number of results to return') with default and max values, and explains 'browser' as 'Which browser to query' with a complete list of options. This fully compensates for the lack of schema 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 a specific verb ('Get') and resource ('most recent browsing history entries'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_most_visited' or 'search_history', which would be needed for 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 the sibling tools 'get_most_visited' and 'search_history'. It mentions what the tool does but offers no context about when it's appropriate versus alternatives, leaving the agent to guess 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search behavior, return format, and parameter defaults, but does not address potential limitations like performance, privacy implications, or error handling. It adds value beyond the schema but could be more comprehensive for a tool accessing browser history.

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 Args and Returns. Every sentence adds value without redundancy, and the formatting enhances readability. It efficiently conveys necessary information in a compact form.

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 (3 parameters, no annotations, but with output schema), the description is mostly complete. It covers purpose, parameters, and return format, but lacks details on behavioral aspects like error cases or performance. The output schema existence reduces the need to explain return values in depth, but some operational context is missing.

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 0%, so the description must compensate fully. It provides detailed semantics for all three parameters: 'search_term' (text to search in URLs/titles), 'limit' (default and max values), and 'browser' (enum options with default). This adds significant meaning beyond the basic schema types and enums.

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 ('search'), resource ('browser history'), and scope ('URLs and titles containing the search term'). It distinguishes from sibling tools like 'get_most_visited' and 'get_recent_history' by emphasizing search functionality rather than retrieval of predefined categories.

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 specifying search capabilities, but does not explicitly state when to use this tool versus alternatives like 'get_most_visited' or 'get_recent_history'. It provides clear parameter defaults and constraints, which aids usage decisions, but lacks direct sibling tool comparisons or exclusion criteria.

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