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 this is a read operation ('Retrieves') and mentions authentication context ('authenticated user has access to'), but doesn't describe behavioral traits like pagination, rate limits, error conditions, or response format. The description adds some value but lacks comprehensive 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?

Two sentences that are front-loaded with the core purpose, followed by return value clarification. Zero waste - every word contributes to understanding the tool's function and scope.

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 a read-only operation with 0 parameters, the description is adequate but has gaps. It explains what the tool retrieves but doesn't describe the structure of returned data, potential limitations, or error handling. For a tool that returns 'a list of all GA4 accounts and their associated properties,' more detail about the response format 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?

The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and instead focuses on what the tool does and returns.

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 verb ('Retrieves information') and resource ('Google Analytics accounts and properties'), with explicit scope ('all GA4 accounts and their associated properties that the authenticated user has access to'). It distinguishes from siblings like ga4_run_report (which runs reports) and ga4_property_details (which gets details for specific properties).

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 it ('to get information about GA4 accounts and properties the user can access'), but doesn't explicitly state when not to use it or name specific alternatives. For example, it doesn't contrast with ga4_property_details for getting detailed info about a specific property.

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 describes the retrieval behavior and its purpose in a workflow, but lacks details on permissions, rate limits, or response format. This is adequate but has gaps for a tool with no annotation coverage.

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 sentences with zero waste: the first states the purpose, and the second provides usage guidance. It is appropriately sized and front-loaded, with every sentence earning its place.

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 (1 parameter, no output schema, no annotations), the description is mostly complete for its purpose. However, it lacks details on behavioral aspects like permissions or output structure, which would be beneficial since no annotations or output schema exist.

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 the single parameter 'propertyId'. The description does not add any meaning beyond what the schema provides, such as format examples or constraints, resulting in the baseline score of 3.

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 ('Retrieves') and resource ('custom dimensions and metrics defined for a GA4 property'), and distinguishes it from siblings by mentioning its role in discovery before running reports, unlike other tools that fetch summaries, links, annotations, details, or run reports.

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?

It explicitly states when to use this tool ('to discover what custom definitions are available before running reports'), providing clear context and distinguishing it from alternatives like ga4_run_report, which is implied as the next step after discovery.

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 this is a read operation ('Returns a list'), but does not mention permissions required, rate limits, pagination, or error handling. For a tool with zero 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 two sentences with zero waste: the first states the purpose, and the second adds context about data sharing. It is appropriately sized and front-loaded, making it easy to understand quickly without unnecessary details.

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) and lack of annotations, the description is minimally adequate but incomplete. It covers the basic purpose but lacks behavioral details like response format or error cases. For a read-only tool with simple inputs, this is borderline viable but could be more informative.

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 the propertyId parameter thoroughly. The description does not add any meaning beyond what the schema provides, such as explaining why this parameter is needed or how it relates to the output. Baseline 3 is appropriate when 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 specific action ('Returns a list of links') and resource ('Google Ads accounts for a GA4 property'), and distinguishes it from siblings by focusing on account connections rather than reports, summaries, or other property details. It explicitly mentions 'data sharing' to clarify the purpose beyond just listing.

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 needing to see connected Google Ads accounts for data sharing, but does not explicitly state when to use this tool versus alternatives like ga4_property_details or other siblings. No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.

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 this is a read operation ('returns'), implying it's non-destructive, but doesn't cover other aspects like authentication needs, rate limits, error handling, or response format. For a tool with zero annotation coverage, 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: the first sentence states the core purpose, and the second adds useful context about annotations. Every sentence earns its place by clarifying the resource and its typical use, with no redundant or verbose language.

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 (1 parameter, no output schema, no annotations), the description is minimally adequate. It explains what annotations are, which helps contextualize the return value, but lacks details on behavioral traits and usage guidelines. For a read-only tool with simple inputs, this is a baseline level of 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 description adds no parameter-specific information beyond what the schema provides. With 100% schema description coverage, the schema already documents the single parameter 'propertyId' with its format details. The baseline score of 3 reflects that the schema does the heavy lifting, and the description doesn't compensate with additional context like examples or constraints.

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: 'Returns annotations for a GA4 property' with a specific verb ('returns') and resource ('annotations for a GA4 property'). It distinguishes annotations from other GA4 data by explaining they are 'notes that mark specific dates or periods' for events like releases or campaigns, though it doesn't explicitly differentiate from sibling tools like 'ga4_property_details'.

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 such as 'ga4_property_details' or 'ga4_run_report', nor does it specify prerequisites, contexts, or exclusions for usage. The explanation of annotations as notes for events is helpful but doesn't translate into actionable usage instructions.

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 states this is a read operation ('Returns'), but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves beyond basic 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 a single, well-structured sentence that efficiently lists key returned attributes. It's front-loaded with the core purpose and avoids unnecessary details, making every word earn its place without 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 low complexity (1 parameter, no output schema, no annotations), the description adequately covers the basic purpose. However, it lacks context on behavioral aspects (e.g., auth, errors) and doesn't leverage sibling tools for guidance, making it minimally complete 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 input schema has 100% description coverage, fully documenting the single parameter 'propertyId' with format details. The description adds no parameter-specific information beyond what the schema provides, 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 ('Returns') and resource ('details about a specific GA4 property'), listing specific attributes like name, display name, time zone, etc. It distinguishes from siblings by focusing on property metadata rather than reports, summaries, or other operations. However, it doesn't explicitly differentiate from 'ga4_account_summaries' which might also include property details.

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 like 'ga4_account_summaries' (which might include property summaries) or 'ga4_run_report' (for data retrieval). It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.

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 returns real-time data for the last 30 minutes and includes hints about API constraints (e.g., realtime-specific dimensions/metrics, no custom metrics), which adds useful context. However, it does not cover other behavioral aspects like authentication requirements, rate limits, error handling, or data format, leaving gaps for a mutation-like operation.

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 in the first sentence, followed by a structured hints section. It avoids redundancy and wastes no words, though the hints could be slightly more integrated into the main description for optimal flow.

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 (8 parameters, nested objects, no output schema, and no annotations), the description provides a solid foundation with purpose, time scope, and parameter hints. However, it lacks details on output format, error cases, or integration with sibling tools, making it adequate but incomplete for full contextual understanding.

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 8 parameters thoroughly. The description adds value through the hints section, which clarifies valid sources for dimensions and metrics (e.g., realtime API schema, user-scoped custom dimensions) and restrictions (no custom metrics), providing semantic context beyond the schema's technical descriptions. This justifies a baseline score of 3 with some enhancement.

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 ('Runs a Google Analytics Data API realtime report') and resource ('real-time analytics data for the last 30 minutes'), distinguishing it from sibling tools like 'ga4_run_report' (which presumably handles non-realtime reports) and other GA4 tools focused on metadata or configuration.

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 implicitly indicates usage context by specifying 'real-time analytics data for the last 30 minutes' and the hints section clarifies constraints on dimensions and metrics (e.g., 'Realtime reports cannot use custom metrics'). However, it lacks explicit guidance on when to choose this tool over alternatives like 'ga4_run_report' or other siblings.

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 returns analytics data, which implies a read-only operation, but doesn't explicitly confirm it's non-destructive or mention rate limits, authentication needs, or pagination behavior. The hints for arguments add some behavioral context (e.g., format requirements), but key operational details are missing.

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 subsequent hints are well-structured into sections for different arguments, making it easy to scan. However, the hints section is lengthy and could be more concise by integrating some examples directly into the schema or reducing 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 complexity (11 parameters, nested objects, no output schema) and no annotations, the description is moderately complete. It covers the purpose, usage guidelines, and parameter semantics well, but lacks details on behavioral aspects like error handling, rate limits, or output format. Without an output schema, the description should ideally hint at the return structure, which it doesn't.

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 100%, so the baseline is 3. The description adds significant value beyond the schema by providing detailed hints for arguments, including valid sources for dimensions and metrics (standard vs. custom), examples for dateRanges, dimensionFilter, and orderBys, and referencing sibling tools for custom data. This compensates for the schema's lack of examples and external references.

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: 'Runs a Google Analytics Data API report. Returns analytics data based on the specified dimensions, metrics, and date ranges.' It specifies the exact action (runs a report), resource (Google Analytics Data API), and distinguishes it from siblings like ga4_run_realtime_report by focusing on standard reporting rather than real-time 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 clear context for when to use this tool by specifying it's for running reports with dimensions, metrics, and date ranges. It explicitly references ga4_custom_dimensions_metrics for retrieving custom dimensions and metrics, offering an alternative for those specific needs. However, it doesn't explicitly state when not to use it (e.g., vs. ga4_run_realtime_report for real-time data).

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 effectively describes what the tool does (validation with specific checks), how it processes inputs (parsing JSON or using arrays), and performance characteristics (API call optimization from N×M to 1+P). However, it lacks details on error handling, rate limits, or authentication requirements, which are relevant for a tool interacting with GA4 APIs.

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 methods, validation items, API efficiency) and uses bullet points for readability. It is appropriately sized but could be more concise by integrating the API efficiency note into the purpose section, as it slightly elongates the text without adding critical usage guidance.

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 (5 parameters, nested objects, no output schema, no annotations), the description is mostly complete. It covers purpose, usage methods, validation scope, and performance optimizations. However, it lacks details on output format (e.g., what the validation results look like) and error scenarios, which are important for a tool with no output schema provided.

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. The description adds minimal semantic value beyond the schema: it clarifies that gtmEvents and gtmExportJson are alternative methods and mentions default values for startDate/endDate. However, it doesn't explain parameter interactions or provide examples beyond what's in the 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: '검증합니다' (validates) that event parameters sent from GTM are properly collected in GA4. It specifies the exact scope (GTM event parameters → GA4 collection validation) and distinguishes itself from sibling tools like ga4_run_report or ga4_custom_dimensions_metrics, which focus on reporting or metadata rather than 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 provides explicit usage guidelines under '사용 방법' (Usage Methods), detailing two alternative approaches: passing gtmEvents directly or providing gtmExportJson. It also implicitly guides when to use this tool (for validation of GTM-to-GA4 parameter collection) versus siblings (e.g., ga4_run_report for data retrieval, ga4_custom_dimensions_metrics for dimension management).

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