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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions output formats and that reports are 'generated,' but doesn't disclose whether this is a read-only operation, if it requires specific permissions, whether it's computationally intensive, or what happens when save_to_file=true (where files are saved, naming conventions, overwrite behavior). For a tool with 5 parameters and no annotation coverage, this is insufficient.

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: first states the core purpose, second specifies output formats. There's no wasted language, though it could be slightly more front-loaded by mentioning key parameters. Every sentence earns its place by adding distinct 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 5 parameters with nested objects, no annotations, and no output schema, the description is moderately complete but has significant gaps. It covers the what (comparison) and output formats but lacks behavioral context (permissions, side effects), usage guidance, and output details. For a comparative analysis tool that likely produces complex reports, more context about report structure, data sources, or limitations 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 all parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'productivity statistics' which loosely relates to metrics, and 'HTML and Markdown formats' which relates to output_format, but provides no additional semantic context about parameter interactions or interpretation. 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 tool's purpose: 'Compare productivity statistics between two time periods' with specific outputs ('HTML and Markdown formats showing trends and changes'). It distinguishes from siblings like generate_daily_summary or get_quick_stats by focusing on comparative analysis rather than single-period reporting. However, it doesn't explicitly differentiate from all siblings (e.g., get_summary could potentially involve comparisons).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this comparative tool is preferred over single-period tools like generate_daily_summary or get_quick_stats, nor does it specify prerequisites or appropriate contexts for comparison. The agent must infer usage from the purpose alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating a summary and saving it (implied by 'save_to_file' parameter), but doesn't cover critical aspects like authentication requirements, rate limits, data privacy implications, or what happens when saving fails. For a tool that aggregates personal data from multiple sources, 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 with two sentences that directly address purpose and usage. It's front-loaded with the core functionality and avoids unnecessary fluff. However, the second sentence could be slightly more specific (e.g., 'Use for end-of-day wrap-ups' instead of 'Perfect for'), keeping it efficient but not perfectly polished.

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 (aggregating data from multiple sources, 4 parameters, no output schema, and no annotations), the description is moderately complete. It covers the 'what' and hints at the 'why,' but lacks details on behavioral aspects, error handling, or output structure. Without annotations or an output schema, users might not fully understand what to expect from the tool's operation.

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%, meaning all parameters are documented in the schema itself. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain the 'include_sections' options further). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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: 'Generate a concise daily productivity summary from today's Slack, Calendar, and Gmail activity.' It specifies the verb (generate), resource (daily productivity summary), and data sources (Slack, Calendar, Gmail). However, it doesn't explicitly differentiate from sibling tools like 'generate_weekly_summary' or 'get_summary', 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 implied usage context with 'Perfect for end-of-day wrap-ups and next-day planning,' suggesting when to use it. However, it lacks explicit guidance on when to choose this tool over alternatives like 'generate_weekly_summary' or 'get_quick_stats,' and doesn't mention any prerequisites or exclusions, leaving room for ambiguity.

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 'Returns structured summary with optional HTML/Markdown output' and implies data aggregation, but lacks critical behavioral details: whether this is a read-only operation, if it requires specific permissions, processing time, rate limits, or what happens when 'save_to_file' is true. For a complex 6-parameter tool with no annotations, this is insufficient.

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 concise and front-loaded, stating the core purpose in the first clause. Both sentences are relevant: the first defines the tool's function, the second adds output details. 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 the tool's complexity (6 parameters, data aggregation from multiple sources, no output schema, and no annotations), the description is incomplete. It doesn't explain the summary's structure, data sources' integration, error handling, or output behavior beyond format options. For a tool that likely involves significant processing and file operations, more context is needed.

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 parameters are well-documented in the schema. The description adds minimal value beyond the schema: it mentions 'optional HTML/Markdown output' which relates to 'output_format', and 'comprehensive weekly productivity summary' hints at 'include_sections'. However, it doesn't provide additional context about parameter interactions or semantics not 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 tool's purpose: generating a comprehensive weekly productivity summary from specific data sources (Slack, Calendar, Gmail). It specifies the verb 'generate' and resource 'weekly productivity summary', but doesn't explicitly differentiate from siblings like 'generate_daily_summary' or 'get_summary' beyond the weekly focus.

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. With siblings like 'generate_daily_summary', 'get_quick_stats', and 'get_summary', there's no indication of comparative use cases, prerequisites, or exclusions. The agent must infer usage from the tool name alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'fast lightweight query' which hints at performance characteristics, but doesn't describe what specific metrics are returned, whether authentication is required, rate limits, error conditions, or the format of the response. For a tool with no annotations and no output schema, 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 two concise sentences that efficiently convey purpose and key characteristics. Every word earns its place: 'Get quick productivity metrics' establishes the core function, '(Slack, Calendar, Gmail)' specifies scope, 'without generating a full summary' differentiates from siblings, and 'Fast lightweight query' adds behavioral context. 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 no annotations, no output schema, and 3 parameters, the description is incomplete. While it clearly states purpose and differentiates from some siblings, it lacks details about what metrics are returned, authentication requirements, rate limits, or error handling. For a tool that presumably returns productivity data across multiple services, more context about the output format and behavioral constraints 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%, with all three parameters (days_back, start_date, end_date) well-documented in the schema including descriptions, defaults, and constraints. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating value.

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 'Get' and the resource 'quick productivity metrics (Slack, Calendar, Gmail)', specifying what data sources are included. It distinguishes from siblings by noting it provides 'quick metrics without generating a full summary', differentiating it from summary-generation tools like generate_daily_summary or get_summary. However, it doesn't explicitly contrast with compare_periods or list_summaries.

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 stating 'without generating a full summary' and 'fast lightweight query', suggesting this tool is for quick overviews rather than detailed reports. It doesn't explicitly state when to use this tool versus alternatives like compare_periods or list_summaries, nor does it provide exclusions or prerequisites. The guidance is present but not comprehensive.

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 it 'retrieves' a summary, implying a read operation, but doesn't disclose any behavioral traits such as error handling (e.g., what happens if no summary matches), performance characteristics, rate limits, or authentication needs. It lacks details on what 'retrieve' entails beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('Retrieve a specific weekly summary') and adds necessary qualification ('by filename or date range'). There's zero waste, and it's appropriately sized for the tool's complexity.

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 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return format, error conditions, or how parameters interact (e.g., whether 'filename' overrides 'date range'). For a retrieval tool with multiple input options, more context is needed to guide effective 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 fully documents all parameters. The description adds minimal value by mentioning 'filename or date range', which loosely maps to parameters but doesn't provide additional semantics beyond what's in the schema (e.g., it doesn't explain interactions between parameters or usage scenarios). 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 action ('Retrieve') and resource ('weekly summary'), specifying it can be retrieved by 'filename or date range'. It distinguishes from siblings like 'list_summaries' (which likely lists multiple) by focusing on retrieving a specific summary. However, it doesn't explicitly differentiate from 'get_quick_stats' or 'compare_periods', keeping it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'filename' vs 'date range', or how it differs from 'list_summaries' (which might list multiple summaries) or 'get_quick_stats' (which might provide aggregated data). There's no explicit context 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?

No annotations are provided, so the description carries full burden. It mentions listing from a directory but doesn't disclose behavioral traits like whether it's read-only (implied by 'List' but not explicit), potential rate limits, authentication needs, error handling, or output format details. This leaves significant gaps for a tool with no structured safety hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and every part earns its place, making it highly concise and well-structured for quick understanding.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what the returned summaries look like (e.g., list of files, metadata), potential pagination, or error cases. For a list operation with three parameters and no structured output info, more context is needed to guide effective 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?

The input schema has 100% description coverage, fully documenting all three parameters (limit, sort, format) with defaults and enums. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without extra value.

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 action ('List') and resource ('previously generated weekly summaries from the summaries directory'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_summary' or 'get_quick_stats', which might also retrieve summary-related data, so it doesn't reach the highest 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 such as 'get_summary' (which might fetch a single summary) or 'generate_weekly_summary' (which creates new ones). There's no mention of prerequisites, exclusions, or specific contexts, leaving usage unclear relative to siblings.

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