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 analyzes impact by comparing metrics before and after deployment, but does not disclose behavioral traits such as whether it requires specific permissions, how it handles missing data, rate limits, or what the output format looks like. This is a significant gap 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 appropriately sized with two sentences that are front-loaded with the main purpose. Every sentence earns its place by stating the action and the benefit, though it could be slightly more structured by explicitly listing parameters or usage scenarios.

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 deployment impact analysis, no annotations, and no output schema, the description is incomplete. It adequately explains the purpose but lacks details on behavioral aspects, output format, and error handling. It meets minimum viability but has clear gaps in providing a full context for the 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add any meaning beyond what the schema provides, such as explaining the significance of entityGuid or deploymentTimestamp in the context of impact analysis. 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 with specific verbs ('analyze', 'compare') and resources ('deployment', 'metrics'), and distinguishes it from siblings by focusing on deployment impact analysis rather than logs, metrics, or queries. It specifies the metrics involved: error rate, throughput, and response time.

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 for identifying performance degradation after deployments, but does not explicitly state when to use this tool versus alternatives like analyze_entity_logs or analyze_golden_metrics. It provides some context ('Helps identify if a deployment caused performance degradation') but lacks clear exclusions or named 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. It mentions the tool is for 'investigating issues' which implies a read-only analysis function, but doesn't specify whether this is a safe read operation, what permissions might be required, whether there are rate limits, or what format/logic the analysis follows. The description is too vague about behavioral traits.

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

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

The description is appropriately concise with two sentences that each serve a clear purpose: the first states what the tool does, the second provides usage context. There's no wasted language, though it could be slightly more structured by explicitly separating purpose from usage guidelines.

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 5-parameter tool with no annotations and no output schema, the description is minimally adequate. It covers the basic purpose and usage context but lacks details about behavioral characteristics, output format, error conditions, or how this tool differs from similar siblings like 'query_logs'. Given the complexity of log analysis, more completeness would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it doesn't explain parameter interactions, provide examples beyond what's in the schema, or clarify semantic relationships between parameters like 'sinceMinutesAgo' and 'limit'.

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: 'Analyze logs for a specific NewRelic entity by GUID' with the specific verb 'analyze' and resource 'logs'. It distinguishes the tool by specifying it's for a 'specific entity' rather than general log analysis, though it doesn't explicitly differentiate from sibling tools like 'query_logs' or 'list_recent_logs'.

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: 'Use this to investigate issues with a particular service, application, or infrastructure component.' This suggests when the tool is appropriate, but it doesn't explicitly state when NOT to use it or mention alternatives like 'query_logs' or 'list_recent_logs' that might serve similar purposes.

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 metric names, titles, and NRQL queries, which covers output behavior. However, it doesn't mention whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or if the data is cached/live. For a tool with zero 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 appropriately sized with three sentences that each add value: defining golden metrics, specifying the return data, and providing usage context. It's front-loaded with the core purpose and avoids redundancy. While efficient, it could be slightly more structured by separating definition from 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 tool has one parameter with full schema coverage but no annotations and no output schema, the description provides adequate context for a read operation by explaining what golden metrics are and what data is returned. However, it lacks details on output format (e.g., structure of returned data), error handling, or dependencies, making it minimally complete but with 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, with the 'guid' parameter fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as explaining entity GUID format or where to obtain it. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to heavily.

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 golden metrics for a NewRelic entity, specifying what golden metrics are and what information is returned (metric names, titles, NRQL queries). It distinguishes from siblings by focusing on recommended key performance indicators rather than logs, transactions, dashboards, or other entity data. However, it doesn't explicitly contrast with tools like 'get_entity' which might provide broader entity information.

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 you need to understand important metrics for an entity, as indicated by 'Use these queries to understand what metrics are important for an entity.' It doesn't provide explicit when-not-to-use guidance or name specific alternatives among siblings like 'get_entity' or 'list_entity_types', leaving usage context somewhat inferred rather than clearly defined.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (analyzes transactions with specific metrics and grouping) and hints at its read-only nature through terms like 'analyze' and 'query,' but doesn't explicitly state whether it's a read operation, its performance characteristics, rate limits, or authentication requirements.

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

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

The description is well-structured and concise. It starts with the core purpose, explains the methodology, lists key parameters implicitly, and ends with use cases. Every sentence adds value without redundancy, making it easy to scan and understand 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 tool with 8 parameters, 100% schema coverage, and no output schema, the description is reasonably complete. It covers the tool's purpose, methodology, and use cases. However, without annotations or output schema, it could benefit from more behavioral details (e.g., read-only confirmation, response format) to fully compensate for the lack of structured metadata.

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 minimal value beyond the schema by mentioning 'NRQL with FACET grouping' and listing example metrics and grouping attributes, but doesn't provide additional syntax, format details, or constraints not already 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: 'Analyze transaction performance using NRQL with FACET grouping.' It specifies the exact event type ('Transaction'), the analysis method (NRQL with FACET), and distinguishes it from siblings like 'execute_nrql_query' (general query) or 'analyze_golden_metrics' (predefined metrics).

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: 'Useful for identifying slow transactions, high-error endpoints, or traffic patterns.' It mentions specific use cases but doesn't explicitly state when NOT to use it or name alternatives like 'execute_nrql_query' for custom queries or 'analyze_golden_metrics' for standard metrics.

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 key behavioral traits: the tool is a 'local utility that does not make API calls' (clarifying it's not a network operation), supports specific time formats (relative and absolute), and mentions use cases. However, it doesn't cover potential error conditions or performance characteristics.

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 stated first, followed by format support, use cases, and behavioral clarification. Every sentence adds value with zero waste, making it easy for an agent to quickly understand the tool's 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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, supported formats, use cases, and behavioral characteristics. However, it doesn't describe the return format (epoch milliseconds) or potential error scenarios, which would be helpful for a conversion utility.

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 fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions supported time formats generally but doesn't provide additional syntax or format details for the parameters.

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 specific verbs ('convert human-readable time strings to epoch milliseconds') and resources (time strings). It distinguishes itself from sibling tools by being a local utility for time conversion rather than an API-based analysis or query 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 clear context for when to use this tool ('useful for constructing NRQL time ranges or comparing timestamps'), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. The guidance is helpful but lacks explicit exclusions.

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 'Returns raw query results with metadata', which adds some behavioral context about output. However, it doesn't disclose critical traits like whether this is a read-only operation (implied but not stated), potential rate limits, authentication requirements, error handling, or data scope limitations. For a tool executing arbitrary queries with no annotation coverage, this 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 two concise sentences with zero waste. The first sentence states the purpose and output, and the second provides usage guidance. It's 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 complexity (executing arbitrary queries), no annotations, and no output schema, the description is moderately complete. It covers purpose and usage but lacks behavioral details like safety, limits, or error handling. The schema handles parameters well, but the description doesn't compensate for missing annotations or output schema, leaving gaps for an agent to understand full 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%, so the schema already documents both parameters ('query' and 'timeout') thoroughly with examples and constraints. The description adds no additional parameter semantics beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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: 'Execute an arbitrary NRQL query against NewRelic' with the verb 'execute' and resource 'NRQL query'. It distinguishes from siblings by specifying it's for 'custom analytics, metrics, and data exploration', though it doesn't explicitly name alternatives. The purpose is specific but could be more differentiated from other query-related tools like 'query_logs' or 'natural_language_to_nrql_query'.

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 some usage context with 'Use this for custom analytics, metrics, and data exploration', which implies when to use it. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., 'query_logs' for logs, 'analyze_golden_metrics' for predefined metrics). The guidance is implied but lacks explicit exclusions or comparisons.

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 mentions the report provides 'actionable recommendations' but doesn't disclose whether this is a read-only operation, if it requires specific permissions, how long it takes to generate, or what format the output takes. For a tool that presumably analyzes and synthesizes data, this is inadequate behavioral disclosure.

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

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

The description is efficiently structured in two sentences that convey the core functionality and value proposition. The first sentence explains what the tool does, and the second explains the output's purpose. There's no unnecessary verbiage, though it could be slightly more specific about the report's format or delivery method.

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 tool that generates a comprehensive report with actionable recommendations, the description is insufficient given the lack of annotations and output schema. It doesn't explain what format the report takes (text, structured data, file), how recommendations are presented, or what 'comprehensive' entails. With no output schema and behavioral gaps, users can't understand what to expect from this 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?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. It doesn't explain how 'entityGuid' affects report focus or how 'sinceDays' integrates with the 'recent deployments' mentioned.

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 generates a 'comprehensive alert insights report' that combines AI issues, error groups, and recent deployments, with actionable recommendations. It specifies the verb ('generate') and resource ('alert insights report'), but doesn't explicitly differentiate from sibling tools like 'list_recent_issues' or 'get_entity_error_groups' that might provide similar data components.

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 what the report includes but doesn't specify scenarios where this comprehensive report is preferable to using individual sibling tools like 'list_recent_issues', 'get_entity_error_groups', or 'list_change_events' separately.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return content ('dashboard structure including all pages, their widgets, and widget configurations'), which adds value beyond the input schema. However, it lacks details on potential errors, rate limits, authentication needs, or whether the operation is read-only (though implied by 'Get'), leaving some behavioral traits unspecified.

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

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

The description is efficiently structured in two sentences: the first states the action and return details, and the second provides usage context. Every sentence adds value without redundancy, making it appropriately sized and front-loaded with 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?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is fairly complete. It explains what the tool does, what it returns, and when to use it. However, without an output schema, it could benefit from more detail on the return structure or potential limitations, though the current level is adequate for basic 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?

The input schema has 100% description coverage, with the 'guid' parameter clearly documented. The description does not add any additional meaning or context about the parameter beyond what the schema provides, such as how to obtain the GUID or format specifics. Thus, it meets the baseline for high schema coverage without compensating with extra insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get a NewRelic dashboard') and resource ('with full page and widget details'), distinguishing it from sibling tools like 'list_dashboards' which likely returns a list rather than detailed structure. It explicitly mentions what is returned ('dashboard structure including all pages, their widgets, and widget configurations'), making the purpose unambiguous.

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

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

The description provides clear context on when to use this tool ('to understand dashboard layout and widget queries'), which implies it's for detailed inspection rather than listing or searching. However, it does not explicitly state when not to use it or name specific alternatives like 'list_dashboards' for broader overviews, leaving some guidance implicit.

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 discloses the return content (metadata, tags, metrics) and hints at metric variability by entity type, but lacks details on error handling, rate limits, authentication needs, or response format. The description doesn't contradict any annotations (none exist).

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 front-loads the core purpose and efficiently lists return types. Every element (verb, resource, parameter, outputs) earns its place with zero waste or 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?

For a simple read operation with 1 parameter (100% schema coverage) and no output schema, the description is reasonably complete: it states purpose, parameter context, and return content. However, without annotations or output schema, it could better address behavioral aspects like error cases or response structure.

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% (the 'guid' parameter is fully documented in the schema). The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Retrieve detailed information'), target resource ('NewRelic entity by its GUID'), and output scope ('entity metadata, tags, and type-specific metrics'). It distinguishes from siblings like 'search_entity_with_tag' (search vs. retrieve by GUID) and 'list_related_entities' (list vs. retrieve single).

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 you have a specific entity GUID and need detailed metrics, but doesn't explicitly state when to use this vs. alternatives like 'search_entity_with_tag' (for tag-based lookup) or 'list_related_entities' (for relationship exploration). No explicit exclusions or prerequisites are provided.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions grouping behavior ('Groups errors by class and message') and the goal ('identify the most frequent and impactful errors'), but lacks critical details such as authentication requirements, rate limits, pagination, error handling, or the format of returned data. For a tool with no annotation coverage, this 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 that front-loads the core purpose ('Get error groups from TransactionError events') and adds useful context ('Groups errors by class and message to identify the most frequent and impactful errors'). Every part earns its place with no wasted words, making it appropriately sized 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 moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It explains the purpose and grouping logic but lacks details on behavioral aspects (e.g., data format, limitations) and usage context. Without an output schema, it should ideally hint at return values, but it doesn't, leaving gaps for an AI agent to infer.

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%, meaning all parameters are documented in the schema. The description adds no specific parameter information beyond what's in the schema (e.g., it doesn't clarify 'entityGuid' or 'transactionName' usage). With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get error groups from TransactionError events' with specific grouping criteria ('by class and message') and goal ('identify the most frequent and impactful errors'). It uses a specific verb ('Get') and resource ('error groups'), but doesn't explicitly differentiate from sibling tools like 'list_recent_issues' or 'list_recent_logs' that might also handle errors.

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 like 'list_recent_issues' (which might show errors) or 'analyze_transactions' (which might include error analysis), nor does it specify prerequisites or exclusions. Usage is implied through the description of what it does, 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.

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

No annotations are provided, so the description carries full burden. It mentions the tool 'Returns condition details including NRQL query, thresholds, and configuration,' which adds some behavioral context about output. However, it lacks details on permissions, rate limits, pagination (beyond the 'limit' parameter), or whether it's a read-only operation, which is insufficient 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, well-structured sentence that efficiently states the purpose, filtering options, and return details. It is front-loaded with the core function 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 no annotations and no output schema, the description partially compensates by specifying return details. However, for a tool with 4 parameters and complex filtering, it lacks information on error handling, authentication needs, or example outputs, leaving gaps in context 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 fully documents all parameters. The description adds marginal value by summarizing filtering options ('by policy, name, or enabled status'), but doesn't provide additional semantics beyond what the schema already specifies, such as format details or usage examples.

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 ('List') and resource ('NewRelic NRQL alert conditions') with specific filtering options. It distinguishes from siblings like 'list_alert_policies' by focusing on conditions rather than policies, but doesn't explicitly differentiate from other list tools like 'list_dashboards' or 'list_entity_types' beyond the resource type.

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 through the mention of 'optional filtering by policy, name, or enabled status,' suggesting when to apply filters. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_alert_policies' or 'execute_nrql_query' for related data, and 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 the tool lists policies and returns specific fields (ID, name, incident preference settings), which is helpful. However, it doesn't mention pagination behavior, rate limits, authentication requirements, or whether the operation is read-only (though 'List' implies safe read). The description adds basic context but lacks depth 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, well-structured sentence that efficiently communicates the tool's purpose, optional filtering, and return values. Every word earns its place with no redundancy or wasted text. It's front-loaded with the core functionality.

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 read-only listing tool with 2 parameters and no output schema, the description provides adequate basic information but has gaps. It doesn't explain the return format beyond field names, doesn't mention pagination or ordering, and provides no context about error conditions or performance characteristics. With no annotations and no output schema, more behavioral context would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents both parameters (nameFilter and limit). The description mentions 'optional name filtering' which aligns with the schema but adds no additional semantic context beyond what's in the parameter descriptions. 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 verb ('List') and resource ('NewRelic alert policies') with specific scope ('with optional name filtering'). It distinguishes from siblings like 'list_alert_conditions' by focusing on policies rather than conditions, though it doesn't explicitly name alternatives. The purpose is specific and actionable.

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 for retrieving alert policies with optional filtering, but provides no explicit guidance on when to use this tool versus alternatives like 'list_alert_conditions' or 'search_entity_with_tag'. It mentions the optional name filter but doesn't specify scenarios where filtering is necessary 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?

With no annotations provided, the description carries the full burden. It discloses that the tool lists accounts based on the current API key and indicates the configured account, which is useful behavioral context. However, it does not mention potential limitations like rate limits, pagination, or error handling, leaving 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 front-loaded with the core purpose in the first sentence, followed by additional details in a second sentence. It is appropriately sized with no wasted words, efficiently conveying necessary information in two concise sentences.

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 provides basic purpose and some behavioral context but lacks details on return format, error cases, or dependencies. For a simple list tool with zero parameters, it is adequate but could be more complete by specifying output structure or limitations.

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 inputs. The description does not need to add parameter details, and it appropriately focuses on the tool's purpose and output semantics without redundancy. Baseline is 4 for zero parameters.

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 ('List') and resource ('NewRelic accounts accessible with the current API key'), specifying it shows account IDs, names, and the currently configured account. It distinguishes itself from siblings like 'list_entity_types' or 'list_dashboards' by focusing specifically on accounts.

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 accessible accounts and the current configuration, but does not explicitly state when to use this tool versus alternatives like 'get_entity' or 'search_entity_with_tag'. It provides some context but lacks explicit guidance on exclusions or comparisons.

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 source ('NrChangeTracking') and types of events included, but doesn't describe important behavioral aspects like whether this is a read-only operation, pagination behavior, rate limits, authentication requirements, or what the return format looks like. For a list tool with zero annotation coverage, this 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 that communicates the core purpose. It's appropriately sized for a list tool, though it could be slightly more structured by separating the purpose from the examples. There's no wasted verbiage.

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 tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (format, structure, or content), behavioral constraints, or how it differs from similar sibling tools. The description alone is insufficient for an agent to understand the full context of using this 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 schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's in the schema, but the schema does the heavy lifting. The baseline of 3 is appropriate when the schema provides complete parameter 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: 'List change events from NrChangeTracking' with specific examples of what's included ('deployments, configuration changes, and other tracked changes'). It uses a specific verb ('List') and resource ('change events'), though it doesn't explicitly differentiate from sibling tools like 'list_recent_logs' or 'list_recent_issues' which might also list events.

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 like 'analyze_deployment_impact' or 'list_recent_logs' that might be relevant for similar purposes, nor does it specify 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?

With no annotations provided, the description carries full burden and discloses key behavioral traits: it returns specific data ('dashboard names and GUIDs'), mentions pagination behavior ('Use pagination cursor for large result sets'), and implies it's a read-only operation (consistent with 'list' functionality). It doesn't cover rate limits or authentication needs.

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

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

The description is perfectly front-loaded with the core purpose in the first sentence, followed by important behavioral details. Every sentence earns its place with no wasted words, making it highly efficient.

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 list tool with no annotations and no output schema, the description provides good coverage of purpose, usage context, and key behavioral aspects. It could be more complete by explicitly stating it's a read-only operation or mentioning authentication requirements, but it's largely adequate.

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 thoroughly. The description adds minimal value beyond the schema by mentioning 'optional account filtering' which aligns with the accountId parameter, but doesn't provide additional semantic context.

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 ('List NewRelic dashboards'), resource ('dashboards'), and scope ('with optional account filtering'). It distinguishes from sibling tools like 'get_dashboard' by emphasizing discovery of available dashboards before fetching full 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 explicitly states when to use this tool ('Useful for discovering available dashboards before fetching full details'), providing clear context for its purpose versus alternatives like 'get_dashboard' for detailed information.

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

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

No annotations are provided, so the description carries full burden. It mentions the return format ('domain/type combinations') but lacks details on pagination, rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this is a significant gap in behavioral disclosure.

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 and includes helpful examples. Every word earns its place, with no wasted text or 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 tool's simplicity (0 parameters, no output schema), the description is adequate but incomplete. It covers the purpose and return examples but lacks behavioral context (e.g., performance, limitations) that would be helpful for an AI agent, especially with no annotations to supplement.

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 0 parameters and 100% schema description coverage, the baseline is 4. The description adds no parameter information, which is appropriate since there are no parameters to document, maintaining clarity 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 verb ('List') and resource ('all available entity types in NewRelic'), with specific examples of return values like 'APM/APPLICATION, INFRA/HOST, BROWSER/APPLICATION'. It distinguishes from siblings by focusing on entity type enumeration rather than analysis, querying, or listing specific resources like dashboards or alerts.

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 for discovering entity types, but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'get_entity' for details on a specific entity). It lacks context on prerequisites or exclusions, leaving usage inferred rather than clearly defined.

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 API is 'experimental' and that it fetches 'active and recent incidents', but does not disclose critical traits like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the return format looks like (e.g., pagination, structure). 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 two sentences, front-loaded with the core purpose and method, and includes no wasted words. Every phrase ('List recent AI-detected issues', 'Uses the experimental aiIssues API', 'fetch active and recent incidents with priority and state information') directly 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 the complexity of a tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., read-only status, rate limits), does not explain the return values or format, and provides minimal guidance on usage versus siblings. For a tool interacting with an experimental API and filtering issues, more context is needed to ensure the agent can 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?

Schema description coverage is 100%, with each parameter well-documented in the schema (e.g., 'states' with enum values, 'limit' with default and max). The description adds no additional parameter semantics beyond what the schema provides, such as explaining how filters interact or providing 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 specific action ('List recent AI-detected issues'), the resource source ('from NewRelic'), and the method ('Uses the experimental aiIssues API'). It distinguishes itself from siblings like 'list_alert_conditions' or 'search_incident' by focusing on AI-detected issues with priority and state information, not general alerts or incidents.

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 for fetching active and recent incidents with priority and state, but does not explicitly state when to use this tool versus alternatives like 'list_alert_conditions' or 'search_incident'. It provides context ('experimental aiIssues API') but lacks clear exclusions or named alternatives, leaving the agent to infer based on the tool's focus.

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 filtering and searching capabilities but fails to describe critical behaviors such as pagination, rate limits, authentication requirements, response format, or whether this is a read-only operation. For a log retrieval 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 appropriately sized with two concise sentences that front-load the core purpose. Every sentence adds value by specifying the resource and use cases, with no redundant or wasted information. It could be slightly more structured by separating use cases, but it's efficient overall.

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 (4 parameters, no output schema, and no annotations), the description is incomplete. It lacks details on behavioral traits like response format, pagination, or error handling, and doesn't compensate for the absence of annotations. For a log retrieval tool in a server with many siblings, more contextual guidance is needed to ensure proper agent usage.

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 four parameters thoroughly. The description adds marginal value by hinting at filtering by level and custom conditions, but doesn't provide additional syntax, examples, or meaning beyond what's in the schema. This meets the baseline score when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('List') and resource ('recent logs from NewRelic'), and mentions filtering capabilities. It distinguishes itself from siblings like 'query_logs' by emphasizing 'recent' activity and 'overview' rather than complex querying. However, it doesn't explicitly contrast with 'analyze_entity_logs' or other log-related tools, preventing 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 implies usage context ('useful for getting an overview of recent log activity, filtering by level, or searching with custom conditions'), suggesting it's for quick, filtered log retrieval. However, it lacks explicit guidance on when to use this tool versus alternatives like 'query_logs' or 'analyze_entity_logs', and doesn't mention prerequisites or exclusions, leaving some ambiguity for the agent.

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 related entities but doesn't describe what 'related' means operationally (e.g., directionality, depth, or types of relationships), how results are formatted, whether it's paginated, or any rate limits or permissions required. 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 appropriately concise with two sentences: the first states the core purpose, and the second provides clarifying examples. It's front-loaded with the main action, and every sentence adds value by elaborating on relationship types. There's no wasted text, though it could be slightly more structured with explicit usage cues.

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 (retrieving relationships, which can be nuanced), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'related' entails, the format or scope of results, or any behavioral constraints. For a tool that likely returns structured relationship data, more context is needed to guide an agent 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 description adds no parameter-specific information beyond what the input schema provides. The schema has 100% description coverage, with the single parameter 'guid' documented as 'The entity GUID to find relationships for'. Since the schema does the heavy lifting, 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 with a specific verb ('Get') and resource ('entities that are related to a specific entity'), and provides examples of relationship types ('service dependencies, infrastructure relationships, and dashboard associations'). However, it doesn't explicitly distinguish this tool from potential siblings like 'get_entity' or 'search_entity_with_tag', which might also retrieve entity information but with different scopes.

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 sibling tools like 'get_entity' (which might retrieve a single entity) or 'search_entity_with_tag' (which might find entities by tags). The agent must infer usage from the purpose alone.

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

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

With no annotations provided, the description carries full burden. It discloses the tool's purpose and what information is returned ('monitor GUIDs, names, current status, and success rates'), but lacks details about behavioral traits like pagination behavior (though cursor parameter hints at it), rate limits, authentication requirements, or error conditions.

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 with three sentences that each add value: purpose definition, resource explanation, and return information. It's front-loaded with the core functionality, though the middle sentence about synthetic monitors could be considered slightly explanatory rather than strictly necessary.

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 read-only listing tool with 4 parameters and no output schema, the description provides adequate context about what's returned but lacks details about response format, pagination behavior, or error handling. The absence of annotations means the description should ideally cover more 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%, so the schema already fully documents all 4 parameters. The description adds minimal value beyond the schema by mentioning filtering capabilities generally, but doesn't provide additional semantic context about parameter usage or interactions.

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 ('List') and resource ('NewRelic synthetic monitors') with specific scope ('with status and success rate information'). It distinguishes from siblings by focusing on synthetic monitors specifically, unlike other tools that handle alerts, dashboards, entities, or logs.

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

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

The description implies usage context by mentioning filtering capabilities ('Filter by account or status'), but doesn't explicitly state when to use this tool versus alternatives like 'list_alert_conditions' or 'list_dashboards'. No explicit exclusions or sibling comparisons are provided.

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

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

No annotations are provided, so the description carries full burden. It mentions the tool returns 'matching query patterns, templates, and NRQL syntax guidance' but doesn't disclose behavioral traits like whether this is a read-only operation, if it makes external API calls, rate limits, authentication requirements, or error handling. 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 appropriately concise with two sentences that efficiently convey purpose and usage. The first sentence states what the tool does, and the second provides usage guidance. No wasted words or redundant information. Could be slightly improved with more behavioral context but maintains good structure.

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 3 parameters with 100% schema coverage but no annotations and no output schema, the description provides adequate purpose and usage context but lacks behavioral transparency. For a query construction tool that likely involves complex pattern matching and returns guidance rather than data, more detail about output format or limitations would improve completeness. The current description is minimally viable but has clear 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?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema. It mentions 'natural language description' generally but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: converting natural language descriptions into NRQL query suggestions, including patterns, templates, and syntax guidance. It specifies the target system (NewRelic) and distinguishes from siblings like execute_nrql_query by focusing on query construction rather than execution. However, it doesn't explicitly differentiate from all potential query-related tools.

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 usage context: 'Use this when you need help constructing NRQL queries for NewRelic.' This gives explicit guidance on when to use it (query construction assistance) and implies alternatives like execute_nrql_query for actual query execution. It doesn't explicitly list when-not-to-use scenarios or all 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. It does reveal some behavioral traits: it mentions wildcard support with %, optional time range, and that it's for 'targeted log searches'. However, it doesn't disclose important behavioral aspects like whether this is a read-only operation, potential performance implications, rate limits, authentication requirements, or what format/logs are returned. The description adds some value but leaves significant 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 perfectly concise and well-structured. It uses just two sentences: the first states the core functionality with key features, and the second provides usage guidance. Every word earns its place with no redundancy or fluff. The information is front-loaded with the most important details first.

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 6-parameter search tool with no annotations and no output schema, the description is somewhat incomplete. While it covers the basic purpose and some behavioral aspects, it doesn't address what the tool returns (log format, structure, or content), error conditions, or important constraints beyond the basic parameter descriptions. The description is adequate for understanding what the tool does but insufficient for fully understanding how to interpret results.

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 minimal parameter semantics beyond what the schema already provides. It mentions 'field and value' and 'optional time range', which the schema already documents thoroughly with 100% coverage. The description does add the wildcard pattern matching detail for the value parameter, which provides some additional context. Given the high schema coverage, the baseline of 3 is appropriate.

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 as 'Search logs by field and value with optional time range', which is a specific verb+resource combination. It distinguishes itself from siblings like 'list_recent_logs' by emphasizing targeted searches based on specific criteria rather than just listing recent logs. However, it doesn't explicitly differentiate from 'analyze_entity_logs' or 'execute_nrql_query', which might offer 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 some usage guidance with 'Use this for targeted log searches based on specific criteria', which implies this tool is for specific queries rather than general listing. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among the many sibling tools. The guidance is helpful but incomplete for proper tool selection.

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 the search functionality but doesn't disclose behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, or what the output format looks like. For a search tool with no annotation coverage, this leaves significant gaps in understanding how the tool 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 concise and front-loaded with the core purpose in the first sentence. The second sentence adds useful context about common use cases. Both sentences earn their place without redundancy, making it efficient for an AI agent 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 complexity of a search tool with 5 parameters and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., read-only nature, error handling), output format, and doesn't fully compensate for the absence of annotations. For a tool that interacts with a query system like NerdGraph, more context is needed 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?

Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds minimal value beyond the schema by mentioning examples like 'environment, team, service name' for tag usage, but doesn't provide additional syntax or format details. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't significantly 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: 'Search for NewRelic entities using tags and NerdGraph query syntax.' It specifies the resource (NewRelic entities) and method (search using tags/NerdGraph). However, it doesn't explicitly differentiate from sibling tools like 'get_entity' or 'list_entity_types' beyond mentioning tag-based search.

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 guidance: 'Use this to find entities by environment, team, service name, or any custom tag.' This suggests when to use it (for tag-based entity searches) but doesn't explicitly state when not to use it or name alternatives like 'get_entity' for direct entity lookup or 'execute_nrql_query' for other query types. 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'uses NRQL to query the NrAiIncident event type,' which adds some technical context about the underlying mechanism. However, it doesn't describe important behavioral traits like whether this is a read-only operation, what authentication is needed, rate limits, pagination behavior, or what the return format looks like. For a search tool with zero annotation coverage, this is insufficient.

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

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

The description is a single, efficient sentence that directly states the tool's purpose and method. It's appropriately sized and front-loaded with the core functionality. There's no wasted verbiage, though it could potentially be more structured with 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 tool's moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and technical approach but lacks behavioral context, usage guidelines, and details about return values. Without annotations or output schema, the agent has incomplete information about what to expect from this tool's operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description adds marginal value by listing the filtering criteria (state, priority, entity) and mentioning NRQL usage, but it doesn't provide additional semantic context beyond what the schema already specifies (e.g., parameter interactions or query syntax details). With high schema coverage, the baseline score of 3 is appropriate.

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 searches NewRelic incidents with specific filtering capabilities (state, priority, entity) and mentions it uses NRQL to query the NrAiIncident event type. This provides a specific verb ('search') and resource ('NewRelic incidents'), though it doesn't explicitly differentiate from sibling tools like 'list_recent_issues' or 'execute_nrql_query' 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 sibling tools like 'list_recent_issues' or 'execute_nrql_query' that might serve similar purposes, nor does it specify prerequisites or exclusions for usage. The agent must infer usage context from the tool name and parameters alone.

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