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 action ('Add') but doesn't clarify if this is a mutation (likely yes), what permissions are required, whether the operation is idempotent, or what happens on success/failure. For a tool that modifies data, this lack of detail is a significant gap.

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 with no wasted words. It is front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for conciseness.

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 likely performs a mutation (adding to a blacklist), the absence of annotations and output schema means the description should compensate by explaining behavioral aspects like side effects or return values. It fails to do so, leaving the agent with incomplete information for safe and 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 already documents both parameters ('campaign_id' and 'zone_ids') adequately. The description adds no additional semantic context beyond what the schema provides, such as explaining what 'zones' represent or how blacklisting affects campaign performance, meeting 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 action ('Add') and target resources ('zones to campaign blacklist'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'add_to_whitelist' or 'auto_blacklist_zones', which would require specifying what makes this tool distinct (e.g., manual vs. automatic blacklisting).

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?

No guidance is provided on when to use this tool versus alternatives like 'add_to_whitelist' or 'auto_blacklist_zones'. The description implies usage for adding zones to a blacklist but doesn't specify prerequisites, exclusions, or contextual triggers, leaving the agent to 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a mutation ('Add'), but does not specify permissions required, whether the operation is idempotent, or how errors are handled. This leaves significant gaps in understanding the tool's 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 a single, direct sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core action without unnecessary elaboration.

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 lack of annotations and output schema, the description is incomplete for a mutation tool. It does not explain what happens after adding zones (e.g., confirmation, error responses, or effects on campaign performance), leaving critical contextual 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?

The input schema has 100% description coverage, so the schema already documents both parameters ('campaign_id' and 'zone_ids'). The description adds no additional meaning beyond what the schema provides, such as format details or constraints, resulting in a baseline score.

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 ('Add') and the target ('zones to campaign whitelist'), making the purpose understandable. However, it does not differentiate this tool from its sibling 'add_to_blacklist' or specify what 'whitelist' entails in this context, 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 like 'add_to_blacklist' or other campaign-related tools. It lacks context about prerequisites, such as whether the campaign must be active or if zones need to exist, leaving usage unclear.

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 will 'find and blacklist,' implying a potentially destructive write operation, but doesn't clarify permissions needed, whether changes are reversible, rate limits, or what happens during execution. The 'dry_run' parameter in the schema suggests a safety feature, but the description doesn't highlight this behavioral trait.

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 with the core action ('find and blacklist') and resource, making it easy to parse quickly. Every word earns its place in conveying the essential function.

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 of an automated blacklisting tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'underperforming' means, how zones are selected, what the output looks like, or potential side effects. For a tool that likely modifies campaign settings, more context is needed to use it safely and 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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining how 'min_spend' and 'max_conversions' interact to define 'underperforming.' However, with high schema coverage, a baseline score of 3 is appropriate as the description doesn't need to compensate.

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 ('find and blacklist') and resource ('underperforming zones for a campaign'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'find_underperforming_zones' or 'add_to_blacklist', which could cause confusion about when to use each.

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. There are multiple sibling tools related to zones, blacklisting, and performance analysis (e.g., 'find_underperforming_zones', 'add_to_blacklist', 'find_top_zones'), but the description doesn't mention any of them or specify contexts where this automated approach is preferred over manual alternatives.

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. While 'Create a copy' implies a write operation, it doesn't specify whether this requires special permissions, whether the clone inherits all settings from the original, what happens to the original campaign, or what the expected response looks like. Significant behavioral 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 extremely concise - a single sentence with zero wasted words. It's front-loaded with the core purpose and doesn't include any unnecessary elaboration or redundant 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?

For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't address what the cloned campaign includes (settings, creatives, targeting), whether the clone is automatically started, what permissions are required, or what the tool returns. Given the complexity of campaign cloning, 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?

With 100% schema description coverage, the input schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain format requirements, constraints, or provide examples for the 'new_name' parameter.

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 ('Create a copy') and resource ('existing campaign'), making the purpose immediately understandable. However, it doesn't distinguish this from sibling tools like 'create_campaign' or explain how cloning differs from creating a new campaign from scratch.

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 'create_campaign' or 'update_campaign'. There's no mention of prerequisites, typical use cases, or when cloning might be preferred over other campaign management approaches.

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 compares performance but doesn't explain what 'performance' entails (e.g., metrics like clicks, conversions), whether it's a read-only operation, if it requires specific permissions, or how results are returned (e.g., as a report or summary). This leaves significant 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 a single, efficient sentence: 'Compare performance between two time periods.' It's front-loaded with the core purpose and has zero wasted words, making it highly concise and well-structured.

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 of comparing performance across periods, no annotations, and no output schema, the description is incomplete. It doesn't clarify what 'performance' means, the format of results, or behavioral aspects like data access or limitations, making it inadequate for informed 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?

The input schema has 100% description coverage, clearly documenting all parameters (e.g., date formats, optional campaign filtering). The description adds no additional parameter semantics beyond implying time periods are involved, so it meets the baseline score of 3 where 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: 'Compare performance between two time periods.' It specifies the verb ('compare') and resource ('performance'), making it understandable. However, it doesn't distinguish this from sibling tools like 'get_campaign_performance' or 'get_performance_report', which might offer similar functionality, so it misses full differentiation.

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 siblings such as 'get_campaign_performance' or 'get_performance_report', leaving the agent to guess based on context 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 full burden for behavioral disclosure. It states this is a creation tool but doesn't mention required permissions, whether the campaign starts automatically, what happens on failure, rate limits, or what the return value contains. This leaves significant gaps for a mutation 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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for the tool's complexity and front-loads the 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?

For a mutation tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what happens after creation, error conditions, or how this differs from similar tools. The agent would need to guess about important behavioral aspects.

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 good documentation for all parameters. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline for high schema coverage but doesn't enhance understanding of parameter relationships or usage patterns.

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 ('Create') and resource ('new advertising campaign'), making the purpose evident. However, it doesn't distinguish this tool from its sibling 'clone_campaign' or 'update_campaign', which also involve campaign creation/modification scenarios.

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 'clone_campaign' or 'update_campaign'. There's no mention of prerequisites, constraints, or typical use cases beyond the basic action.

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 finds campaigns based on criteria but doesn't explain what 'scaling' entails (e.g., increasing budget, expanding targeting), whether it's a read-only analysis or triggers actions, or any limitations like data freshness or permissions required. This leaves significant gaps in understanding the tool's 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 a single, efficient sentence: 'Find campaigns ready for scaling based on ROI and conversion volume.' It's front-loaded with the core purpose and wastes no words, making it easy to parse quickly.

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 (analyzing campaigns for scaling decisions), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like what 'scaling' means, output format, or error handling. For a tool with 4 parameters and analytical purpose, 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, providing details for all parameters (e.g., min_roi as 'Minimum ROI percentage (default: 50)'). The description adds minimal value beyond the schema by implying ROI and conversion volume are key criteria but doesn't elaborate on semantics like how ROI is calculated or what 'conversions' refer to. Baseline 3 is appropriate since 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: 'Find campaigns ready for scaling based on ROI and conversion volume.' It specifies the verb ('find'), resource ('campaigns'), and criteria ('ROI and conversion volume'), making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'find_top_zones' or 'find_underperforming_zones', which might also identify campaigns or zones for optimization, so it's not a perfect 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 prerequisites, exclusions, or compare it to siblings such as 'find_top_zones' or 'find_underperforming_zones', which could serve similar analytical purposes. Without this context, users might struggle to choose the right tool.

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 finds 'best performing zones' and hints at a use case for whitelisting, but does not describe what the tool returns (e.g., list of zones with metrics), whether it's read-only or has side effects, or any performance characteristics like rate limits. This leaves significant gaps in understanding the tool's 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 very concise with two short sentences, making it easy to read and front-loaded with the main purpose. However, it could be more structured by explicitly linking parameters to the 'best performing' criteria, but it avoids unnecessary verbosity.

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 of a tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., a list of zones with performance metrics), how results are ordered, or any behavioral nuances. This leaves the agent with insufficient context to use the tool effectively beyond basic parameter input.

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, so parameters are well-documented in the schema. The description does not add any meaning beyond the schema, such as explaining how 'best performing' is determined from the parameters (e.g., using min_conversions and min_roi). Baseline score of 3 is appropriate as the schema handles parameter documentation adequately.

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 states the tool finds 'best performing zones' and mentions 'useful for whitelist candidates,' which gives a general purpose but lacks specificity about what constitutes 'best performing' (e.g., based on conversions, ROI, etc.). It distinguishes from some siblings like 'find_underperforming_zones' but not clearly from others like 'get_zone_performance' or 'find_scaling_opportunities.'

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 minimal guidance by noting it's 'useful for whitelist candidates,' implying a context for use, but does not specify when to use this tool versus alternatives like 'find_underperforming_zones' or 'get_zone_performance,' nor does it mention prerequisites or exclusions. No explicit when/when-not instructions are given.

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's output purpose ('blacklist candidates') but lacks details on permissions needed, rate limits, whether it's a read-only operation, what the return format looks like, or any side effects. For a tool with 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 extremely concise with two short sentences that are front-loaded and waste no words. The first sentence states the core purpose, and the second adds practical context, making it efficient and easy to parse.

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 (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and a usage hint but lacks details on behavior, output format, or error handling. With no annotations to fill gaps, the description should do more to be complete for an agent to use it 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 input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by implying the tool uses spend and conversion metrics but doesn't explain parameter interactions or provide additional context like how 'min_spend' and 'max_conversions' combine to define 'underperforming.' Baseline 3 is appropriate given 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: 'Find zones that are spending money but not converting.' It specifies the verb ('find') and resource ('zones') with a clear criterion (spending without conversion). However, it doesn't explicitly differentiate from siblings like 'find_top_zones' or 'find_scaling_opportunities' beyond the implied focus on underperformance.

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: 'Useful for blacklist candidates.' This suggests when to use the tool (to identify zones for potential blacklisting) but doesn't explicitly state when not to use it or name alternatives like 'auto_blacklist_zones' or 'get_zone_performance' for comparison. The guidance is helpful 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Get list') but doesn't clarify if this is a read-only operation, requires authentication, has rate limits, or what the return format might be (e.g., list of strings, objects). This leaves significant gaps for an AI agent to understand the tool's 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 a single, efficient sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'available ad formats' entails (e.g., types, structures, or usage context), leaving the AI agent to guess about return values and behavioral traits. For a tool with no structured data support, more detail is needed to be fully 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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. A baseline of 4 is appropriate as the description doesn't need to compensate for missing parameter information.

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 ('list of available ad formats'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'get_available_countries' or 'get_balance' beyond the resource type, 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. With sibling tools like 'get_campaign_details' and 'get_performance_report', there's no indication of whether this tool is for general reference, campaign setup, or another specific context. No exclusions or prerequisites are mentioned.

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 retrieves a list, implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns structured data (e.g., with country codes or names), or if the list is static or dynamic. 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 a single, clear sentence: 'Get list of available countries for targeting.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool with no parameters. Every part of the sentence earns its place by specifying the action and resource.

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, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavioral aspects like return format or usage context. Without annotations or output schema, the description should ideally provide more context (e.g., what 'available' means or how the list is used), but it's not entirely incomplete for a basic retrieval tool.

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, meaning there are no parameters to document. The description doesn't need to add parameter semantics, so it meets the baseline of 4 for this dimension, as it doesn't have to compensate for any gaps in 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: 'Get list of available countries for targeting.' It specifies the verb ('Get') and resource ('list of available countries'), making it easy to understand what the tool does. However, it doesn't distinguish itself from potential sibling tools that might also retrieve country-related data, such as if there were a 'get_targetable_countries' tool.

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, context (e.g., for campaign setup or targeting configuration), or exclusions. Given the sibling tools include campaign-related operations like 'create_campaign' and 'update_campaign', it's unclear if this tool is used independently or as part of those workflows.

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 it's a read operation ('Get'), but doesn't mention authentication requirements, rate limits, data freshness, or what happens on errors. 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 a single, clear sentence with zero wasted words. It's front-loaded with the essential information and appropriately sized for a simple read operation with no parameters.

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 simple read operation with no parameters and no output schema, the description covers the basic purpose adequately. However, without annotations or output details, it lacks information about return format, currency, or data precision that would be helpful for an agent.

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 schema fully documents the absence of inputs. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose without redundancy.

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 ('Get') and resource ('current account balance'), making the purpose immediately understandable. It doesn't distinguish from siblings, but since there are no other balance-related tools among the siblings, this is sufficient for clarity.

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, frequency, or context for retrieving balance information, leaving the agent to infer usage 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), but does not mention permissions, rate limits, error handling, or what happens if the campaign_id is invalid. 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 a single, efficient sentence that front-loads the core purpose ('Get complete details for a specific campaign') and adds specific scope ('including targeting, creatives, and settings'). There is zero wasted text, making it highly concise and well-structured.

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 (one parameter, no output schema, no annotations), the description is minimally adequate. It covers what the tool does but lacks details on behavioral aspects like permissions or error handling, which are important for a read operation in a server with many sibling tools.

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, with the single parameter 'campaign_id' documented as 'Campaign ID'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline 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 tool's purpose with a specific verb ('Get') and resource ('campaign'), and specifies the scope of details ('complete details including targeting, creatives, and settings'). However, it does not explicitly differentiate from sibling tools like 'list_campaigns' or 'get_campaign_performance', which slightly limits its clarity in 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 provides no guidance on when to use this tool versus alternatives such as 'list_campaigns' for overviews or 'get_campaign_performance' for metrics. It lacks explicit when/when-not instructions or prerequisites, 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read-only operation, the description doesn't address critical aspects like authentication requirements, rate limits, error conditions, or what happens if dates are omitted. 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 a single, well-structured sentence that efficiently conveys the core purpose without any wasted words. It's front-loaded with the main action and resource, making it easy to parse quickly.

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 of a performance summary tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'calculated metrics and insights' include, how results are formatted, or any behavioral constraints. For a tool that likely returns detailed data, 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 already documents all three parameters (campaign_id, date_from, date_to) with their types and basic descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how date ranges affect the performance summary or if dates are optional. 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 with a specific verb ('Get') and resource ('performance summary for a specific campaign'), and adds valuable context about what the summary includes ('calculated metrics and insights'). However, it doesn't explicitly differentiate this tool from similar siblings like 'get_campaign_details' or 'get_performance_report', 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. With multiple sibling tools related to campaign performance (e.g., 'get_campaign_details', 'get_performance_report', 'get_creative_performance'), there's no indication of what makes this tool distinct or when it should be preferred over others.

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 but offers minimal behavioral insight. It implies a read-only operation but does not disclose details like data freshness, rate limits, authentication needs, or what 'performance statistics' entail (e.g., metrics, format). This leaves significant gaps for an agent to understand the tool's 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 a single, efficient sentence with no wasted words, making it easy to parse. However, it is front-loaded but overly brief, potentially sacrificing clarity for brevity, as it lacks elaboration that could aid agent selection.

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 of performance statistics and lack of annotations or output schema, the description is incomplete. It does not explain what 'creatives' refers to, what statistics are returned, or any behavioral traits, leaving the agent with insufficient context to use the tool effectively without trial and error.

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%, with clear parameter descriptions in the input schema. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or default behaviors. This meets the baseline for high schema coverage but does not enhance parameter understanding.

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 states the tool's purpose as 'Get performance statistics for creatives,' which is clear but vague. It specifies the verb 'Get' and resource 'performance statistics for creatives,' but does not differentiate from siblings like 'get_campaign_performance' or 'get_zone_performance,' leaving ambiguity about scope and distinctions.

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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context, prerequisites, or exclusions, such as whether it's for specific creative types or how it differs from other performance-related tools like 'get_performance_report' or 'compare_periods.'

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 'Get' operation, implying read-only behavior, but doesn't mention authentication requirements, rate limits, pagination, error handling, or what the output format looks like (e.g., structured data vs. raw metrics). 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 a single, efficient sentence that front-loads the core purpose. It lists relevant metrics concisely without unnecessary elaboration. However, it could be slightly more structured by explicitly mentioning it's for aggregated reporting versus other performance tools.

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 minimally adequate for a read operation but incomplete. It specifies the metrics returned but lacks details on output format, error cases, or behavioral constraints. With 4 parameters and sibling tools that may overlap, more context would help an agent use it correctly.

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 input schema fully documents all four parameters (date_from, date_to, group_by, campaign_id) with descriptions and defaults. The description adds no additional parameter semantics beyond implying metrics are returned, which doesn't compensate for schema details. This meets the baseline of 3 when schema coverage is high.

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 retrieves detailed performance statistics with specific metrics listed (impressions, clicks, conversions, etc.). It uses a specific verb ('Get') and identifies the resource ('performance statistics'), but it doesn't explicitly differentiate from sibling tools like 'get_campaign_performance' or 'compare_periods' that might also provide performance 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 no guidance on when to use this tool versus alternatives. With sibling tools like 'get_campaign_performance', 'compare_periods', and 'find_top_zones' that likely overlap in functionality, there's no indication of this tool's specific scope, prerequisites, or when other tools might be more appropriate.

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 is 'useful for whitelist/blacklist optimization,' which hints at read-only analysis, but doesn't explicitly state whether this is a read operation, what permissions are needed, or how results are returned (e.g., pagination, format). For a tool with no annotations, 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 extremely concise with just two short sentences that are front-loaded with the core purpose. Every word earns its place, and there's no redundant or unnecessary information, making it efficient and easy to parse.

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 (5 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose and a use case but lacks details on behavioral traits, output format, or error handling. Without annotations or an output schema, the description should do more to compensate, but it only provides basic context, leaving gaps for an agent to infer behavior.

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 input schema. The description doesn't add any additional meaning beyond what's in the schema (e.g., it doesn't explain how 'zone/placement' relates to parameters or provide usage examples). With high schema coverage, the baseline score of 3 is appropriate 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: 'Get performance statistics grouped by zone/placement.' It specifies the verb ('Get') and resource ('performance statistics') with grouping criteria. However, it doesn't explicitly differentiate from sibling tools like 'get_campaign_performance' or 'get_performance_report' which might overlap in functionality.

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 'Useful for whitelist/blacklist optimization,' which suggests when this tool might be valuable. However, it doesn't explicitly state when to use this tool versus alternatives like 'find_top_zones' or 'find_underperforming_zones,' nor does it mention any prerequisites or exclusions 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 the full burden. It mentions optional filters and return fields, but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, any rate limits, pagination handling, or default sorting. For a listing tool with no annotations, 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 highly concise and front-loaded, consisting of two clear sentences. The first states the action and optional filters, and the second specifies return fields. There is no wasted verbiage, making it easy to parse quickly.

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 (listing with filters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and return fields but lacks behavioral context and usage guidelines. For a tool with no output schema, it helpfully lists return fields, but overall completeness is limited by missing operational details.

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 input schema fully documents the parameters (status, ad_format, name) with descriptions and enums. The description adds minimal value beyond this, only noting that filters are 'optional' and implying they refine the listing. No additional syntax, format, or usage details are provided beyond what the schema offers.

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: 'List all campaigns with optional filters.' It specifies the verb ('List') and resource ('campaigns'), and mentions the return fields. However, it doesn't explicitly differentiate from sibling tools like 'get_campaign_details' or 'get_campaign_performance', which might offer more detailed or performance-focused campaign 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 no guidance on when to use this tool versus alternatives. With siblings like 'get_campaign_details' (likely for specific campaigns) and 'get_campaign_performance' (likely for metrics), there's no indication of when this listing tool is preferred, such as for overviews or bulk retrieval. Usage is implied but not stated.

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 but offers minimal behavioral insight. It implies a mutation action ('Activate/start') but doesn't disclose permission requirements, whether the operation is reversible, rate limits, or what happens if some campaigns fail to start. This is inadequate for a mutation 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 extremely concise (one sentence) and front-loaded with the core action. There's no wasted verbiage or unnecessary elaboration, making it efficient for quick comprehension.

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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'Activate/start' entails operationally, what the expected outcome is, or potential error conditions. Given the complexity of campaign management, 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%, with the single parameter 'campaign_ids' clearly documented in the schema. The description adds no additional parameter context beyond what's already in the structured data, so it 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 action ('Activate/start') and resource ('one or more campaigns'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'stop_campaigns' or 'create_campaign' beyond the basic verb difference.

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?

No guidance is provided about when to use this tool versus alternatives like 'create_campaign' or 'stop_campaigns'. The description doesn't mention prerequisites (e.g., campaigns must exist and be in a stoppable state) or contextual constraints.

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 but offers minimal behavioral insight. It implies a mutation ('Pause/stop') but doesn't specify if this is reversible, requires specific permissions, affects campaign data, or has side effects like stopping associated ads.

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 extremely concise—a single sentence with zero wasted words—and front-loads the core action. Every word earns its place, making it easy to parse quickly.

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 mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error handling, return values, or how it interacts with sibling tools, leaving significant gaps for an agent to use it correctly.

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 input schema fully documents the 'campaign_ids' parameter. The description adds no additional meaning about parameter usage, such as ID formats or handling of invalid IDs, meeting 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 action ('Pause/stop') and resource ('one or more campaigns'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'start_campaigns' beyond the obvious verb difference, missing an opportunity to clarify scope or permanence.

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?

No guidance is provided on when to use this tool versus alternatives like 'update_campaign' for partial changes, or prerequisites such as campaign status requirements. The description merely restates the action without contextual usage advice.

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 states this is an update operation, implying mutation, but doesn't disclose behavioral traits such as required permissions, whether changes are reversible, rate limits, or what happens to unspecified settings. The description adds minimal context 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 purpose with specific examples. It avoids unnecessary words and gets straight to the point, though it could be slightly more structured by explicitly mentioning the required 'campaign_id' parameter.

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 of a mutation tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks crucial information such as behavioral details (e.g., error conditions, side effects), usage context, and output expectations, leaving significant gaps for an AI agent to understand the tool fully.

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 full documentation for all 6 parameters. The description adds marginal value by listing examples of updatable settings (budget, bid, targeting, status), which aligns with some parameters but doesn't provide additional semantics beyond what the schema already covers. Baseline 3 is appropriate given 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 'update' and the resource 'campaign settings', with specific examples of what can be updated (budget, bid, targeting, status). It distinguishes from siblings like 'create_campaign' or 'clone_campaign' by focusing on modification rather than creation or duplication, though it doesn't explicitly differentiate from all siblings.

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 (e.g., needing an existing campaign), exclusions (e.g., what cannot be updated), or comparisons to siblings like 'start_campaigns' or 'stop_campaigns' for status changes. Usage is implied but not explicitly stated.

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