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 analysis outputs (sentiment, themes, leads) but doesn't describe how the analysis is performed, what the return format looks like, whether it requires authentication, rate limits, or potential errors. 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 a single, efficient sentence that states the tool's purpose without unnecessary words. It's front-loaded with the core action ('analyze comments') and key outputs. However, it could be slightly more structured by separating the analysis outputs for clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (analysis tool with no annotations and no output schema), the description is incomplete. It doesn't explain the return values, error conditions, or how the analysis is conducted. For a tool that performs sentiment and theme analysis, more context on output format and limitations would be necessary for an AI 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 schema description coverage is 100%, so the input schema already documents both parameters ('postUrl' and 'maxComments') with clear descriptions. The description adds no additional semantic context beyond what the schema provides, such as URL format examples or analysis depth implications. 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 tool's purpose: analyzing Instagram post comments to identify sentiment, themes, and potential leads. It specifies the resource (Instagram post comments) and the analysis outputs (sentiment, themes, leads). However, it doesn't explicitly differentiate from sibling tools like 'identify_leads' or 'generate_engagement_report', which might have overlapping 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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over sibling tools like 'identify_leads' (which might focus on lead identification specifically) or 'generate_engagement_report' (which could involve broader metrics). No exclusions or prerequisites are 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 the full burden of behavioral disclosure. It states what the tool does but lacks details on permissions, rate limits, data freshness, or output format. For a tool that likely accesses external data (Instagram accounts), this omission is significant and leaves behavioral traits unclear.

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 is front-loaded and appropriately sized, making it easy for an agent 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 comparing engagement metrics across accounts, the lack of annotations and output schema means the description is incomplete. It doesn't explain what the comparison outputs (e.g., a table, summary, or raw data), how metrics are calculated, or any limitations, which are crucial for effective tool 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 schema description coverage is 100%, with clear descriptions for both parameters (e.g., 'List of Instagram account handles to compare' and 'Metrics to compare (default: all)'). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for adequate but not enhanced 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 ('compare') and the resource ('engagement metrics across different Instagram accounts'), providing a specific purpose. However, it doesn't explicitly differentiate this tool from its sibling tools (like 'generate_engagement_report' or 'analyze_post_comments'), which might also involve engagement metrics analysis.

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 for comparison, or how it differs from sibling tools such as 'generate_engagement_report' or 'analyze_post_comments', 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool extracts insights but doesn't describe how it works (e.g., data sources, processing methods), potential limitations (e.g., accuracy, privacy constraints), or output format. For a tool with 2 parameters and no annotations, 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 without unnecessary details. It avoids redundancy and waste, making it appropriately sized for a tool with 2 parameters. However, it could be slightly more structured by hinting at the tool's scope or limitations.

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 (demographic analysis with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what insights are extracted (e.g., age, location), how results are returned, or any behavioral traits. For a tool that likely involves data processing and user analysis, more context 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 100% description coverage, clearly documenting both parameters. The description adds no additional meaning beyond the schema, such as explaining the context of 'accountOrPostUrl' or the implications of 'sampleSize'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description doesn't compensate or 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 clearly states the tool's purpose: extracting demographic insights from users engaged with a post or account. It specifies the verb 'extract' and the resource 'demographic insights', but it doesn't explicitly differentiate from sibling tools like 'analyze_post_comments' or 'generate_engagement_report', which might also involve user analysis. This makes it clear but not fully sibling-distinctive.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions analyzing 'users engaged with a post or account', but doesn't specify scenarios, prerequisites, or exclusions compared to sibling tools like 'identify_leads' or 'compare_accounts'. This lack of explicit when/when-not instructions leaves 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 full burden for behavioral disclosure. It states the tool generates a report but doesn't describe what 'comprehensive' entails, the format of the output, whether it requires authentication, rate limits, or processing time. For a reporting tool with no annotations, this is a significant gap in transparency about how the tool behaves and what to expect.

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 without unnecessary words. It's appropriately sized for a tool with three parameters and clear purpose. However, it could be slightly more front-loaded by specifying the key differentiator (e.g., 'comprehensive' versus other tools) more explicitly.

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 generating a comprehensive report, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'engagement' metrics are included, the report format, whether it's downloadable or displayed, or any limitations. For a tool that presumably produces rich output, the description leaves too much undefined about what the agent can expect.

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 (account, startDate, endDate) with clear descriptions. The description adds no additional parameter semantics beyond what's in the schema. This meets the baseline expectation when the schema does the heavy lifting, but doesn't provide extra value like explaining how date ranges affect report scope or account format requirements.

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 ('generate') and resource ('engagement report for an Instagram account'). It distinguishes from siblings like 'analyze_post_comments' or 'compare_accounts' by focusing on comprehensive reporting rather than specific analyses. However, it doesn't explicitly differentiate from potential overlap with 'extract_demographics' or 'identify_leads' which might be part of engagement reporting.

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, appropriate contexts, or exclusions. For example, it doesn't clarify if this should be used for periodic reporting versus ad-hoc analysis, or how it differs from using sibling tools in combination. This leaves the agent without clear decision-making criteria.

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 'identify potential leads' but does not specify output format, rate limits, authentication needs, or whether it performs read-only or mutative operations. 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 that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, 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 lead identification with two parameters (including a nested object) and no output schema or annotations, the description is incomplete. It fails to explain what the tool returns, how leads are identified, or any behavioral traits, leaving the agent with insufficient context for 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 ('accountOrPostUrl' and 'criteria') and their sub-properties. The description does not add any meaning beyond this, such as explaining how 'engagement patterns' relate to the parameters or providing usage examples, 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 states the purpose as 'Identify potential leads based on engagement patterns,' which specifies the action (identify) and resource (leads) with a general method (engagement patterns). However, it does not distinguish this tool from sibling tools like 'analyze_post_comments' or 'generate_engagement_report,' which might also involve engagement analysis, making it vague in 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?

No explicit guidance is provided on when to use this tool versus alternatives such as 'analyze_post_comments' or 'extract_demographics.' The description implies usage for lead identification but lacks context on prerequisites, exclusions, or specific scenarios, offering minimal direction.

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