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 'Supports range annotations and dashboard association' and has a default tag, which adds useful behavioral context beyond the basic 'add' action. However, it doesn't cover important aspects like permissions needed, whether annotations are editable/deletable, or response format, leaving gaps for a mutation tool.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes key features. Every clause adds value (e.g., event types, range support, default tag), with no redundant or vague phrasing, though it could be slightly more structured for clarity.

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

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

For a mutation tool with no annotations and no output schema, the description is moderately complete. It covers the tool's purpose and some behavioral traits but lacks details on permissions, error handling, or return values. Given the complexity of creating annotations with multiple parameters, more context would be beneficial to fully guide 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 fully documents all 6 parameters. The description adds minimal value beyond the schema by mentioning 'range annotations' (hinting at time/timeEnd usage) and 'dashboard association' (hinting at dashboardUid), but doesn't provide additional syntax or format details. 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 action ('Add a Grafana annotation') and resource ('on a sensor for test events'), with specific event types listed (start/end/result/rate change). It distinguishes from sibling tools by focusing on annotation creation rather than querying or dashboard management, though it doesn't explicitly name alternatives for similar annotation tasks.

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 through 'for test events' and 'Tagged with ramp-test by default,' suggesting it's designed for test-related annotations. However, it doesn't provide explicit guidance on when to use this versus other annotation methods or tools, nor does it mention prerequisites or 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 of behavioral disclosure. It mentions 'suggestions,' implying advisory or interactive behavior, but doesn't clarify if this is a read-only helper, whether it requires specific permissions, what the output format is, or any rate limits. For a tool with no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It avoids redundancy and wastes no words, 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 (5 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits, usage context, and output expectations. While the schema covers parameters, the description doesn't compensate for missing annotations or provide enough context for effective tool invocation in a multi-tool environment.

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 itself. The description adds no additional semantic context about parameters beyond what's in the schema (e.g., it doesn't explain how 'labels' and 'filter' interact or provide examples of query building). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't 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: 'Help build a LogQL query with suggestions for log stream selectors and filters.' It specifies the verb ('build'), resource ('LogQL query'), and scope ('log stream selectors and filters'). However, it doesn't explicitly differentiate from sibling tools like 'query_loki' or 'build_prometheus_query', which would be needed for a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'query_loki' (which likely executes queries) or 'build_prometheus_query' (for Prometheus queries), nor does it specify prerequisites or exclusions. This leaves the agent without context for 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 of behavioral disclosure. It states the tool 'help build' and provides 'suggestions,' implying it's a read-only, non-destructive helper, but doesn't clarify if it requires specific permissions, how suggestions are generated, or what the output format is. For a tool with no annotations, this leaves significant behavioral gaps.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It uses clear language ('Help build,' 'suggestions') and avoids redundancy, making it easy to parse quickly.

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

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

Given the tool's complexity (4 parameters, nested objects) and lack of annotations and output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., a query string, suggestions list), how to interpret suggestions, or any behavioral constraints. For a helper tool with no structured output, more context is needed to guide effective use.

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

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

The input schema has 75% description coverage, with clear documentation for 'filters,' 'function,' and 'timeWindow.' The description adds minimal value beyond the schema by hinting at 'suggestions for metric names and operators,' which loosely relates to the 'metric' and 'function' parameters but doesn't provide additional syntax or format details. Given the high schema coverage, a 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's purpose: 'Help build a Prometheus query with suggestions for metric names and operators.' It specifies the action ('build'), resource ('Prometheus query'), and scope ('suggestions for metric names and operators'). However, it doesn't explicitly differentiate from sibling tools like 'query_prometheus' (which executes queries) or 'build_logql_query' (for Loki queries), so it falls short of a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a metric name, or contrast it with sibling tools like 'query_prometheus' for executing queries or 'build_logql_query' for Loki queries. Without this context, the agent must infer usage 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.

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 checks existence but doesn't describe what 'exists' means (e.g., active vs. archived), the return format (e.g., boolean, status code, error message), or any side effects like rate limits or authentication requirements. This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and uses clear terminology, making it easy to parse quickly.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain the return value (e.g., what 'exists' entails or the response format), behavioral details like error handling, or usage context relative to sibling tools. For a tool with no structured output information, more descriptive content 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 'uid' and 'name' parameters. The description adds minimal value by mentioning these parameters but doesn't provide additional context, such as whether both can be used together or if the check is case-sensitive. 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's purpose as checking if a datasource exists, specifying the verb 'check' and resource 'datasource'. It distinguishes itself from sibling tools like 'get_datasource_by_name' or 'get_datasource_by_uid' by focusing on existence verification rather than retrieval, though it doesn't explicitly mention this distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_datasource_by_name' or 'get_datasource_by_uid'. It doesn't specify prerequisites, such as whether both UID and name can be provided simultaneously or if one is prioritized, nor does it mention any exclusions or context for usage.

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

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

No annotations are provided, so the description carries full burden. It mentions 'using baseline data' which adds some context about data sources, but doesn't disclose critical behavioral traits: whether this is a read-only analysis or has side effects, what the comparison output looks like, whether it's computationally intensive, or if there are rate limits. For a comparison 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 front-loads the core functionality. Every word earns its place: 'Compare two firmware builds' establishes the action, 'across all sensor types and profiles' defines scope, and 'using baseline data' adds important context. No wasted words 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?

Given the tool's moderate complexity (comparison operation with 2 parameters) and lack of both annotations and output schema, the description is minimally adequate. It covers what the tool does but leaves significant gaps: no output format, no behavioral constraints, and no usage guidance. The description meets basic requirements but doesn't provide enough context for confident tool selection and invocation.

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

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

Schema description coverage is 100%, with both parameters clearly documented as 'First build name' and 'Second build name'. The description adds no additional parameter semantics beyond what the schema provides. It doesn't clarify build name format, valid values, or relationship between the two builds. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Compare two firmware builds across all sensor types and profiles using baseline data.' It specifies the verb ('compare'), resource ('firmware builds'), and scope ('across all sensor types and profiles'). However, it doesn't explicitly differentiate from sibling tools like 'fingerprint_regression' or 'fleet_regression_sweep' that might involve similar comparison concepts.

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. Given the many sibling tools related to testing, regression, and analysis, the agent receives no help in selecting this specific comparison tool over others.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers none. It doesn't mention whether this is a mutating operation, what permissions are required, whether it's idempotent, what happens on success/failure, or any side effects. The description fails to provide any behavioral context beyond the basic action implied by 'create'.

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 maximally concise at just three words, with no wasted language. While this conciseness comes at the expense of completeness, the description is perfectly structured as a simple declarative statement without unnecessary elaboration.

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

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

For a complex creation tool with 11 parameters, no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It provides no information about what the tool actually does, how to use it, what parameters mean, or what to expect as a result. The description fails to provide the minimal context needed 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?

With 11 parameters and 0% schema description coverage, the description provides no information about any parameters. It doesn't explain what 'condition', 'data', 'folderUID', 'ruleGroup', or any other parameters mean, their expected formats, or how they relate to creating an alert rule. The description fails completely to compensate for the schema's lack of 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 'Create a new alert rule' is a tautology that restates the tool name without adding meaningful context. It specifies the verb 'create' and resource 'alert rule', but lacks any detail about what an alert rule is, its purpose in the system, or how it differs from similar tools like 'update_alert_rule' or 'get_alert_rule'.

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

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

The description provides no guidance on when to use this tool versus alternatives. There are multiple sibling tools related to alert rules (delete_alert_rule, get_alert_rule, update_alert_rule, list_alert_rules), but the description offers no comparison, prerequisites, or context for choosing this specific creation tool over others.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes an alert rule, implying a destructive mutation, but doesn't specify critical details like required permissions, whether deletion is permanent or reversible, error conditions (e.g., invalid UID), or side effects. This is inadequate for a destructive operation.

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

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

The description is a single, direct sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient. Every word earns its place, achieving maximum clarity in minimal space.

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 destructive tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks behavioral details (e.g., permanence, permissions), parameter guidance, and usage context. While concise, it doesn't provide enough information for safe and effective use by an AI 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 0%, so the description must compensate. It mentions the 'uid' parameter but provides no semantic context beyond 'by UID'—no explanation of what a UID is, how to obtain it, format expectations, or examples. This leaves the single required parameter poorly understood despite the description's attempt to reference it.

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 ('Delete') and resource ('an alert rule by UID'), making the purpose unambiguous. It distinguishes from siblings like 'create_alert_rule' and 'update_alert_rule' by specifying deletion, though it doesn't explicitly differentiate from other deletion tools like 'delete_dashboard' 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing the UID from 'get_alert_rule' or 'list_alert_rules'), when deletion is appropriate, or what happens after deletion (e.g., irreversibility). This leaves the agent without context 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?

With no annotations, the description carries full burden but only states the action. It doesn't disclose critical behavioral traits: whether deletion is permanent, requires specific permissions, affects linked resources, or returns confirmation. The existence of 'restore_dashboard_version' suggests potential reversibility, but this isn't mentioned.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately understandable.

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 destructive tool with no annotations and no output schema, the description is insufficient. It lacks details on permissions, consequences, reversibility, error handling, or response format. Siblings like 'restore_dashboard_version' hint at versioning, but this context isn't integrated.

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 meaning by specifying that the 'uid' parameter identifies the dashboard to delete. With 0% schema description coverage and only one parameter, this adequately compensates, though it doesn't explain UID format or sourcing (e.g., from 'get_dashboard_by_uid').

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 ('Delete') and resource ('a dashboard by UID'), making the purpose unambiguous. It distinguishes from siblings like 'get_dashboard_by_uid' (read) and 'update_dashboard' (modify), but doesn't explicitly differentiate from other destructive operations like 'delete_alert_rule'.

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

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing dashboard UID from 'get_dashboard_by_uid' or 'search_dashboards'), nor does it clarify if deletion is permanent or reversible (though 'restore_dashboard_version' exists as a sibling).

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 lacks critical behavioral details. It mentions deployment and optional patching but doesn't disclose whether this is a read-only or destructive operation, what permissions are required, how errors are handled, or what the output looks like. For a deployment 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 efficiently structured in two sentences: the first states the core purpose, and the second adds optional functionality. Every word earns its place with no redundancy or fluff, making it easy to parse 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?

Given the complexity of a deployment tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'deploy' entails (e.g., creates new dashboard, overwrites existing), what happens on success/failure, or return values. For a tool that likely modifies Grafana state, more behavioral 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?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds marginal value by implying that 'compare' and 'profile' work together for baseline comparisons, but doesn't provide additional syntax, format details, or examples beyond what the schema specifies. 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 action ('deploy') and target ('RAMP Performance Analysis dashboard to a sensor's Grafana'), with an optional enhancement ('patch with baseline comparison panels'). It distinguishes from siblings like 'update_dashboard' by focusing on deployment of a specific dashboard rather than general updates. However, it doesn't explicitly contrast with 'generate_dashboard_url' or 'restore_dashboard_version', which slightly limits differentiation.

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

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

The description implies usage when deploying a dashboard with optional comparison features, but provides no explicit guidance on when to choose this tool over alternatives like 'update_dashboard' or 'generate_dashboard_url'. It mentions the optional patching but doesn't specify prerequisites or exclusions, leaving usage context partially inferred.

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 tool's action (run diagnostic battery) and output format (drop sources by layer, bottleneck classification, leading indicators), which adds value beyond the input schema. However, it doesn't disclose potential side effects, execution time, permission requirements, or error conditions, leaving gaps for a diagnostic tool.

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

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

The description is efficiently structured in two sentences: the first states the action and purpose, the second specifies the return format. Every element earns its place with no redundant information, making it appropriately sized and front-loaded for quick comprehension.

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

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

Given the tool's complexity (diagnostic analysis with multiple output components), no annotations, and no output schema, the description does well by explaining what the tool does and what it returns. However, it could be more complete by mentioning execution characteristics or error handling. The absence of an output schema means the description must cover return values, which it does adequately but not exhaustively.

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 (sensor, from, to) with their types and formats. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 where the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('run a comprehensive diagnostic battery') and resource ('against a sensor'), and distinguishes it from siblings by focusing on drop diagnosis rather than monitoring, querying, or testing functions. It specifies what the tool does (identify where drops occur and why) and what it returns (drop sources by layer, bottleneck classification, leading indicators).

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 (when drops need investigation on a sensor) but doesn't explicitly state when to use this tool versus alternatives like 'sensor_status', 'sensor_performance_verdict', or 'query_sensor_metric'. It suggests a comprehensive diagnostic approach but lacks explicit guidance on prerequisites or 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?

With no annotations provided, the description carries the full burden. It discloses that the tool performs a network scan (port scanning) and returns specific data fields, which is useful behavioral context. However, it lacks details on potential side effects (e.g., network load), authentication needs, rate limits, or error handling, leaving gaps for a tool that interacts with network resources.

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 action and outcome efficiently. It avoids unnecessary words and clearly communicates the tool's purpose and output, making it easy to parse without redundancy.

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

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

Given the tool's complexity (network scanning with no parameters) and lack of annotations and output schema, the description provides a basic overview but is incomplete. It mentions the return data structure but not the format (e.g., JSON array), error cases, or scanning scope (e.g., default ports). For a tool that performs active network operations, more behavioral context would be beneficial.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description correctly states no parameters are required ('Scan ports...' implies a default scan behavior), adding clarity beyond the schema. A baseline of 4 is appropriate as it effectively communicates the lack of inputs.

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 ('Scan ports for active SSH-tunneled Corelight sensor Grafana instances') and the resource ('sensors'), specifying it returns connected sensors with specific attributes (hostname, port, Grafana version, Prometheus status). It distinguishes from siblings like 'sensor_status' or 'explore_sensor_metrics' by focusing on discovery rather than status checking or metric exploration.

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 active sensors via port scanning, but does not explicitly state when to use this tool versus alternatives like 'sensor_status' (which might check known sensors) or 'explore_sensor_metrics' (which queries metrics). No exclusions or prerequisites are mentioned, leaving usage context somewhat inferred.

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. While it describes the grouping behavior ('grouped by subsystem'), it doesn't mention important aspects like whether this is a read-only operation, potential performance impact, authentication requirements, rate limits, or what the return format looks like. 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 a single, well-structured sentence that efficiently communicates the tool's purpose without any wasted words. It's front-loaded with the core action and includes all essential information in a compact form.

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

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

Given the tool's moderate complexity (listing metrics with grouping), no annotations, and no output schema, the description provides adequate basic information but lacks details about return format, error conditions, or behavioral constraints. It's minimally viable but leaves gaps that could hinder effective tool selection and invocation.

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

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

With 100% schema description coverage and only one parameter ('sensor' with clear description), the description doesn't need to add parameter details. The baseline is 3, but since there's only one parameter and the schema fully documents it, a score of 4 reflects that the description appropriately focuses on tool purpose rather than redundant parameter explanations.

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

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

The description clearly states the action ('List all available Prometheus metric names'), the resource ('on a sensor'), and the grouping method ('grouped by subsystem'). It specifically distinguishes this from sibling tools like 'query_sensor_metric' (which queries specific metrics) and 'get_prometheus_metadata' (which gets metadata rather than listing metric names).

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 available metrics on a specific sensor, but doesn't explicitly state when to use this versus alternatives like 'get_prometheus_metadata' or 'query_sensor_metric'. It provides basic context but lacks explicit guidance on exclusions or prerequisites.

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 combining data to fingerprint a root cause, which implies analysis/read-only behavior, but doesn't specify whether this requires specific permissions, has side effects, involves data processing limits, or what the output format looks like. For a diagnostic tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes to understanding what the tool does, making it appropriately concise and well-structured.

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

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

Given the complexity of regression diagnosis and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'fingerprint' entails, what format the output takes, or any behavioral constraints. For a tool that likely involves data analysis and diagnosis, more context is needed to understand its full scope and 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?

Schema description coverage is 100%, so the schema already documents all three parameters (sensor, build, profile) with descriptions. The tool description doesn't add any additional meaning about these parameters beyond what's in the schema. It doesn't explain how they relate to 'performance verdict' or 'live diagnostic data' mentioned in the purpose. 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: 'Combine performance verdict with live diagnostic data to fingerprint the root cause of a regression.' It specifies the action (combine/fingerprint), resources (performance verdict, live diagnostic data), and outcome (root cause of regression). However, it doesn't explicitly differentiate from sibling tools like 'diagnose_drops' or 'fleet_regression_sweep' that might have related 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 prerequisites (e.g., needing a performance verdict first), exclusions, or how it differs from similar tools like 'diagnose_drops' or 'fleet_regression_sweep' in the sibling list. Usage is implied but not explicitly stated.

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

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 sweep checks sensors against baseline profiles and optionally fingerprints regressions, but lacks critical details: whether this is a read-only or destructive operation, permission requirements, execution time, error handling, or what 'optional fingerprinting' entails. For a tool performing system-wide regression testing, 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, well-structured sentence that efficiently conveys the core functionality without unnecessary words. It could be slightly more front-loaded by emphasizing the main action first, but it's appropriately sized and avoids redundancy.

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

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

Given the complexity of running a regression sweep across all sensors, the lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., report format, success/failure indicators), error conditions, or dependencies on other tools like 'discover_sensors'. For a tool with significant operational impact, this leaves too many unknowns.

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% for the single parameter 'build', which is documented in the schema as 'Build name from baselines.json to check against'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. 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 action ('Run a regression sweep') and scope ('across all discovered sensors against a specific build'), with specific details about checking sensors against baseline profiles and optionally fingerprinting regressions. However, it doesn't explicitly differentiate from sibling tools like 'fingerprint_regression' or 'compare_builds', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'fingerprint_regression' or 'compare_builds', nor does it mention prerequisites such as needing discovered sensors or baseline profiles. It only states what the tool does, not when it should be selected.

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 parallel execution and the return structure (per-sensor results and fleet summary), but lacks details on performance characteristics (e.g., timeouts, rate limits), error handling, or side effects. For a tool that likely involves significant computation, this leaves critical behavioral traits undocumented.

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, consisting of two sentences that efficiently convey the core action and output. There is no wasted text, and it avoids redundancy, though it could be slightly more structured by explicitly separating purpose from output details.

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

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

Given the complexity of running performance verdicts across multiple sensors, the lack of annotations, and no output schema, the description is incomplete. It doesn't explain the format of 'per-sensor results' or 'fleet summary', potential errors, or dependencies on other tools like 'discover_sensors'. This leaves significant gaps for an AI agent to understand the tool's full context.

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 both parameters ('build' and 'profile') clearly documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints on values. This meets the baseline score of 3 since the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('run performance verdict') and resources ('all discovered sensors'), and it specifies the parallel execution mode. However, it doesn't explicitly differentiate from the sibling tool 'sensor_performance_verdict', which appears to be a similar single-sensor version, leaving some ambiguity about when to choose one over the other.

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

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

The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'sensor_performance_verdict' tool. It mentions running against 'all discovered sensors' but doesn't clarify prerequisites (e.g., whether sensors must be discovered first via 'discover_sensors') or contextual constraints, offering minimal usage direction.

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 behavior by describing what it calculates ('maximum sustainable traffic rate', 'headroom per subsystem', 'limiting factor'), but doesn't mention output format, computational requirements, or potential side effects. It adequately describes the core behavior without rich contextual details.

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

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

Two sentences with zero waste. The first sentence states the core purpose, and the second adds valuable detail about outputs. Every word earns its place, and the description is appropriately sized for a single-parameter tool.

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 (forecasting based on utilization), no annotations, and no output schema, the description does well by explaining what it calculates and what insights it provides. It could be more complete by mentioning output format or data sources, but for a tool with 100% schema coverage and clear purpose, it's largely sufficient.

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 only one parameter ('sensor' hostname), so the schema already documents the parameter. The description adds context by explaining this is for traffic rate forecasting based on resource utilization, giving meaning to why the sensor parameter matters. With zero parameters requiring compensation, baseline 4 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 with specific verbs ('extrapolate', 'shows', 'identifies') and resources ('maximum sustainable traffic rate', 'sensor', 'subsystems'). It distinguishes from siblings by focusing on traffic rate forecasting rather than querying, monitoring, or testing 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 implies usage when needing to understand sensor capacity limits ('headroom per subsystem'), but doesn't explicitly state when to use this tool versus alternatives like 'sensor_status' or 'sensor_trend'. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description carries full burden. It states the tool generates a URL but doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, what format the URL takes, or if there are rate limits. The description is minimal and lacks essential context for safe and effective use.

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. There is no wasted language, and it directly states what the tool does with optional features mentioned concisely. It earns its place without unnecessary elaboration.

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

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

Given the complexity (6 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain the return value, error conditions, or how parameters interact. For a tool that generates URLs with multiple options, more context is needed to ensure proper usage without trial and error.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'optional time range and variables', which loosely maps to 'from', 'to', and 'variables' parameters, but doesn't explain the other parameters like 'dashboardUid', 'refresh', or 'panelId'. The description adds minimal meaning beyond the schema, failing to adequately document the 6 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 'generate' and the resource 'URL for a specific dashboard', making the purpose understandable. It distinguishes from siblings like 'generate_panel_url' and 'generate_explore_url' by specifying it's for dashboards, though it doesn't explicitly contrast with them. The description is specific but lacks explicit sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'generate_panel_url' or 'generate_explore_url'. It mentions 'optional time range and variables' but doesn't explain when these should be used or what scenarios warrant this tool over others. No explicit when/when-not instructions 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 states the tool generates a URL but does not describe what the output looks like (e.g., format, structure), any side effects (e.g., if it creates or modifies resources), authentication needs, rate limits, or error conditions. For a tool with 8 parameters and no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, clear sentence that efficiently states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly. This is an example of optimal conciseness for a basic description.

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 (8 parameters, no schema descriptions, no annotations, and no output schema), the description is insufficient. It does not explain parameter semantics, output format, behavioral traits, or usage context. For a tool that likely generates URLs with specific structures and dependencies, more detail is needed to ensure correct invocation.

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

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

The schema description coverage is 0%, meaning none of the 8 parameters have descriptions in the schema. The tool description does not add any information about parameter meanings, usage, or relationships (e.g., how 'type' influences other parameters like 'dashboardUid' or 'panelId'). This leaves parameters largely undocumented, failing to compensate for the schema gap.

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

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

The description clearly states the tool's purpose: 'Generate a deeplink URL for Grafana dashboards, panels, or explore view.' It specifies the verb ('generate') and resource ('deeplink URL') with the target context ('Grafana dashboards, panels, or explore view'). However, it does not explicitly differentiate from sibling tools like 'generate_dashboard_url', 'generate_panel_url', or 'generate_explore_url', which appear to serve similar purposes, preventing a score of 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions the types of deeplinks (dashboard, panel, explore) but does not specify when to choose this tool over sibling tools like 'generate_dashboard_url' or 'generate_panel_url', nor does it outline any prerequisites or exclusions. This lack of contextual guidance limits its utility for an AI 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 generates a URL but doesn't describe what the URL is used for, whether it's a read-only operation, what format the URL takes, or any side effects. For a tool with 6 parameters and 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 a single, efficient sentence that front-loads the core purpose. There is no wasted verbiage or redundancy. It directly states what the tool does without unnecessary elaboration.

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

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

Given the complexity (6 parameters, 1 required), lack of annotations, 0% schema description coverage, and no output schema, the description is inadequate. It doesn't explain the Explore view context, parameter interactions, expected output format, or error conditions. For a URL-generation tool with multiple optional parameters, more contextual information is needed.

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

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

Schema description coverage is 0%, meaning none of the 6 parameters have descriptions in the schema. The description only vaguely mentions 'optional datasource and query', which maps to datasourceUid and query parameters, but ignores the other 4 parameters (from, to, refresh, queryType). It adds minimal value beyond the parameter names, failing to compensate for the low schema coverage.

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

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

The description clearly states the action ('Generate a URL') and the target ('for the Explore view'), with mention of optional parameters ('with optional datasource and query'). It distinguishes from siblings like generate_dashboard_url and generate_panel_url by specifying the Explore view context. However, it doesn't explicitly differentiate from generate_loki_explore_url or generate_prometheus_explore_url, which appear to be more specific variants.

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 optional parameters but doesn't explain when this tool is appropriate compared to siblings like generate_loki_explore_url or generate_prometheus_explore_url, nor does it mention prerequisites or exclusions. The agent must infer usage 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.

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool generates a URL but doesn't explain what the URL is used for (e.g., linking to a Grafana Explore view), whether it performs any validation, or if it has side effects like logging. For a tool with 5 parameters and 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 directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for a simple tool, with no wasted 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 complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover parameter meanings, usage context, or behavioral details like what the generated URL looks like or how to use it. For a tool that likely produces a web link for log analysis, more context is needed to guide effective use.

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

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

The description adds no meaning beyond the input schema, which has 0% schema description coverage. It doesn't explain what parameters like 'datasourceUid', 'query', 'from', 'to', or 'refresh' represent, their expected formats (e.g., time strings for 'from'/'to'), or how they affect the generated URL. With 5 parameters and no schema descriptions, the description fails to compensate, leaving parameters undocumented.

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

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

The description clearly states the tool's purpose: 'Generate an Explore URL for Loki log queries.' It specifies the verb ('Generate'), resource ('Explore URL'), and domain ('Loki log queries'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'generate_explore_url' or 'generate_prometheus_explore_url', which would require explicit comparison.

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 'generate_explore_url' (generic) or 'generate_prometheus_explore_url', nor does it specify prerequisites, such as needing a valid datasource or query. Without this context, an agent might struggle to choose the correct tool.

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

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

No annotations are provided, so the description carries full burden. It states the tool generates a URL but doesn't disclose behavioral traits: whether this requires authentication, what format the URL is in (e.g., web link, API endpoint), if it's idempotent, or any rate limits. The description is minimal and lacks critical operational context for a tool with 6 parameters.

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. Every word earns its place with no redundancy or fluff. It's appropriately sized for a straightforward tool.

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 (6 parameters, nested objects, no output schema, and no annotations), the description is inadequate. It doesn't explain what the generated URL is used for, how to interpret parameters like 'variables' or 'refresh', or what the output looks like. For a tool with significant parameter complexity, this leaves too many 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 0%, so the description must compensate. It only mentions 'optional time range' which loosely maps to 'from' and 'to' parameters, but doesn't explain the other 4 parameters (dashboardUid, panelId, refresh, variables) or their purposes. The description adds minimal value beyond the schema, failing to address the coverage gap.

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 ('Generate a URL') and the target ('for a specific panel'), with additional scope ('with optional time range'). It distinguishes from sibling 'generate_dashboard_url' by focusing on panels rather than dashboards. However, it doesn't specify what type of panel or URL (e.g., Grafana panel view URL), leaving some ambiguity.

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 mentions 'optional time range' which implies when to use that feature, but provides no guidance on when to choose this tool over alternatives like 'generate_dashboard_url', 'generate_explore_url', or other URL generation siblings. No prerequisites, exclusions, or comparative context is given.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a URL but doesn't describe what the URL is for (e.g., linking to a Grafana Explore view), whether it requires authentication, any rate limits, or the format of the output. For a tool with 7 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 directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('Generate an Explore URL'), making it easy to parse. However, it could be slightly more informative without losing conciseness, such as by hinting at the output type.

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 (7 parameters, no schema descriptions, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior, parameter meanings, or output format, which are critical for an agent to use it correctly. Sibling tools like 'generate_loki_explore_url' suggest this is part of a family, but the description doesn't leverage that context.

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 0%, meaning none of the 7 parameters have descriptions in the schema. The description only vaguely mentions 'specific options' without explaining what the parameters (datasourceUid, query, from, to, refresh, step, range) mean or how they affect the generated URL. This fails to compensate for the lack of schema documentation, leaving parameters largely unexplained.

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

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

The description clearly states the tool's purpose: 'Generate an Explore URL for Prometheus queries with specific options.' It specifies the verb ('Generate'), resource ('Explore URL'), and domain ('Prometheus queries'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generate_explore_url' or 'generate_loki_explore_url,' which would require mentioning it's specifically for Prometheus vs. other data sources.

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 'generate_explore_url' (generic) or 'generate_loki_explore_url' (for Loki), nor does it specify prerequisites, such as needing a valid datasourceUid or query. This lack of context leaves the agent to infer usage based on tool names alone.

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

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

No annotations are provided, so the description carries the full burden. It states it 'gets' information, implying a read-only operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or response format. For a tool with no annotation coverage, this minimal description leaves critical operational details 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, clear sentence with no wasted words. It's front-loaded with the core purpose ('Get detailed information about a specific alert rule'), making it easy to parse quickly. Every part of the sentence contributes directly to understanding 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 complexity (a read operation with a required parameter), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover parameter meaning, behavioral context, or return values, leaving the agent with insufficient information to use the tool effectively beyond its basic purpose.

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 1 parameter with 0% description coverage, and the tool description adds no parameter semantics. It doesn't explain what 'uid' represents (e.g., a unique identifier for the alert rule), its format, or where to obtain it. With low schema coverage, the description fails to compensate, leaving the parameter undocumented.

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 ('alert rule'), specifying it retrieves 'detailed information about a specific alert rule'. It distinguishes from siblings like 'list_alert_rules' (which lists multiple) and 'create_alert_rule'/'update_alert_rule'/'delete_alert_rule' (which modify). However, it doesn't explicitly mention the sibling differentiation, keeping it at 4 rather than 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a rule UID), contrast with 'list_alert_rules' for browsing, or specify use cases like troubleshooting or editing. With multiple sibling tools for alert rules, this lack of context is a significant gap.

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

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

No annotations are provided, so the description carries full burden. It states 'Get detailed information,' implying a read-only operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what 'detailed information' includes (e.g., fields returned). This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is a single, clear sentence with no wasted words, front-loading the key action and resource. It's appropriately sized for a simple retrieval tool, 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, no output schema, and low schema description coverage (0%), the description is incomplete. It doesn't explain what 'detailed information' entails, how to handle errors, or usage context, which is inadequate for a tool that likely returns structured data about contact points in a system like Grafana.

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 1 parameter with 0% description coverage, so the description must compensate. It mentions 'a specific contact point,' which hints that the 'uid' parameter identifies the contact point, but doesn't explain the UID format, source, or constraints. This adds minimal meaning beyond the schema, resulting in a baseline score due to incomplete compensation.

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

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

The description clearly states the action ('Get detailed information') and resource ('about a specific contact point'), making the purpose understandable. It doesn't explicitly differentiate from sibling tools like 'list_contact_points' or 'test_contact_point', but the specificity of 'specific contact point' versus 'list' implies individual retrieval versus listing.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'list_contact_points' or 'test_contact_point'. It lacks context about prerequisites, such as needing a contact point UID, or exclusions, leaving the agent to infer usage from the 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.

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 'gets' information, implying a read-only operation, but doesn't clarify authentication needs, rate limits, or what 'current' means (e.g., based on session, user context, or default). 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 a single, efficient sentence with no wasted words. It's front-loaded with the core action ('Get current organization information'), making it easy to parse. Every word earns its place, achieving optimal conciseness.

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

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

Given the complexity (a read operation with no parameters) and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'current' means, what information is returned, or any prerequisites. For a tool that likely requires context (e.g., user authentication), this leaves too many unanswered questions.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied since the schema fully covers the lack of parameters, and the description doesn't need to compensate.

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

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

The description 'Get current organization information' clearly states the verb ('Get') and resource ('current organization information'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'get_current_user' or 'get_team_by_uid' that also retrieve organizational data, leaving the scope somewhat vague.

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

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

No guidance is provided on when to use this tool versus alternatives. The description doesn't specify if this retrieves the user's default organization, a specific context, or how it differs from tools like 'get_current_user' or 'list_teams'. This lack of context makes it unclear when an agent should select this tool.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't describe what 'current user information' includes (e.g., ID, name, permissions), whether it requires authentication, or how it determines 'current' context (e.g., session-based). This leaves significant gaps for an agent to understand the tool's behavior.

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

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

The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient and easy to parse without unnecessary elaboration.

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

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It states what the tool does but lacks details on return values, authentication needs, or error conditions. For a tool that likely returns user data, more context on output structure would be helpful, though it meets basic requirements.

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, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter details, but with no parameters, a baseline of 4 is appropriate as it avoids redundancy while being complete for this case.

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 'Get current user information' clearly states the verb ('Get') and resource ('current user information'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_users' or 'get_current_organization', which would require explicit comparison to earn a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication context), contrast with 'list_users' for broader user listings, or specify scenarios where current user data is needed over organizational data from 'get_current_organization'.

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 only states the basic operation. It doesn't disclose behavioral traits like whether this is a read-only operation (implied by 'Get' but not explicit), authentication requirements, rate limits, error handling, or response format. This leaves significant gaps for an agent to understand tool behavior.

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

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

The description is a single, efficient sentence with no wasted words. It front-loads the core purpose ('Get full dashboard details') and adds necessary qualification ('using its unique identifier'), 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, no output schema, and a parameter with 0% schema coverage, the description is incomplete. It doesn't explain what 'full details' returns, error conditions, or dependencies, leaving the agent with insufficient context for reliable use in a complex environment with many sibling tools.

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

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

The description adds minimal semantics beyond the input schema, which has 0% coverage. It clarifies that 'uid' is a 'unique identifier' for the dashboard, but doesn't specify format, source, or constraints (e.g., length, where to find it). With one parameter and low schema coverage, this provides some context but falls short of fully compensating.

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

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

The description clearly states the action ('Get full dashboard details') and resource ('dashboard'), and specifies the method ('using its unique identifier'). It distinguishes from siblings like 'search_dashboards' (which likely returns multiple dashboards) and 'get_dashboard_versions' (which focuses on version history). However, it doesn't explicitly mention what 'full details' includes, keeping it from 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 when you have a specific dashboard UID and need detailed information, contrasting with 'search_dashboards' for broader queries. However, it lacks explicit guidance on when not to use it (e.g., if you only need basic info or panel queries) or alternatives for related tasks, leaving some ambiguity.

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

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

No annotations are provided, so the description must fully disclose behavior. It states what data is retrieved but omits critical details: whether this is a read-only operation, if it requires specific permissions, how errors are handled (e.g., invalid UID), or the response format. For a tool with no annotations, this leaves significant behavioral gaps.

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

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

The description is a single, clear sentence that front-loads the key action and data retrieved without unnecessary words. It efficiently conveys the core functionality, making it easy to parse 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?

Given no annotations, no output schema, and low parameter semantics, the description is incomplete. It lacks details on behavioral traits (e.g., safety, permissions), response structure, and error handling, which are crucial for a tool that retrieves data from a dashboard. This leaves the agent with insufficient context for reliable use.

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

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

The input schema has one parameter ('uid') with 0% description coverage, so the schema provides no semantic context. The description implies the 'uid' is for a dashboard but does not specify format, source, or constraints. It adds minimal value beyond the schema, resulting in a baseline score due to the single parameter.

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

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

The description clearly states the action ('Get') and the specific data retrieved ('title, query string, and datasource information from every panel in a dashboard'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_dashboard_by_uid' or 'generate_panel_url', which might retrieve dashboard metadata or URLs instead of panel queries.

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

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

The description provides no guidance on when to use this tool versus alternatives, such as 'get_dashboard_by_uid' for general dashboard info or 'query_prometheus' for executing queries. It lacks context on prerequisites, like needing a dashboard UID, or exclusions, leaving the agent to infer usage from the tool name alone.

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

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

No annotations are provided, so the description carries full burden. 'Get version history' implies a read-only operation, but it doesn't disclose whether this requires specific permissions, what format the history returns, whether results are paginated, or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 without any wasted words. It's appropriately sized for a simple retrieval tool and front-loads the essential information.

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

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

For a tool with no annotations, no output schema, and 0% parameter documentation, the description is inadequate. It doesn't explain what 'version history' includes, how results are structured, or any behavioral constraints. The agent would need to guess about the return format and operational characteristics.

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% schema description coverage and only one parameter ('uid'), the description doesn't add any parameter-specific information beyond what's implied by the tool name. It doesn't explain what the 'uid' represents, where to find it, or format requirements. However, with just one parameter, the baseline is higher than for multi-parameter tools with poor 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 ('Get') and resource ('version history for a dashboard'), making the purpose immediately understandable. It doesn't explicitly distinguish from siblings like 'get_dashboard_by_uid' or 'restore_dashboard_version', but the focus on 'version history' provides reasonable differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when this tool is appropriate versus other dashboard-related tools, or any limitations on its use. The agent must infer usage from the tool name alone.

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

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

With no annotations provided, the description carries full burden but only states it 'gets detailed information' without specifying what that information includes, whether it's a read-only operation, error behavior, or performance characteristics. It lacks crucial context about what 'detailed information' means for a datasource.

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

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

The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a simple lookup tool and front-loads the essential information.

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

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

For a tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It doesn't explain what 'detailed information' includes, error conditions, or how this differs from other datasource tools. The agent lacks crucial context to use this tool 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 0%, and the description only mentions 'using its name' without explaining what constitutes a valid datasource name, format requirements, or case sensitivity. With one undocumented parameter, the description adds minimal value beyond what's obvious from the parameter name.

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

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

The description clearly states the verb ('Get detailed information') and resource ('about a datasource') with a specific lookup method ('using its name'). It distinguishes from sibling 'get_datasource_by_uid' by specifying name-based lookup, but doesn't explicitly differentiate from 'check_datasource_exists' or 'list_datasources'.

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

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

No guidance is provided on when to use this tool versus alternatives like 'get_datasource_by_uid', 'check_datasource_exists', or 'list_datasources'. The description only states what the tool does, not when it's appropriate versus other datasource-related tools.

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

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

No annotations are provided, so the description carries the full burden. It states the tool retrieves 'detailed information' but doesn't disclose behavioral traits such as whether it's a read-only operation, what happens if the UID doesn't exist (e.g., error handling), rate limits, authentication needs, or the format of the returned data. The description is minimal and lacks critical operational context.

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

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

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to scan. Every part of the sentence contributes to understanding the tool's purpose without unnecessary elaboration.

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

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

Given the tool's complexity (a read operation with one parameter) and the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling or data format, and with no output schema, it should ideally hint at what 'detailed information' includes. For a tool in a datasource management context, more guidance is needed 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?

The description adds minimal meaning beyond the input schema. It specifies that the 'uid' parameter is used to identify the datasource, which is somewhat redundant with the tool name. With 0% schema description coverage, the description doesn't compensate by explaining the UID format, examples, or constraints. However, since there's only one parameter, the baseline is higher, but it still lacks enrichment.

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

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

The description clearly states the verb ('Get') and resource ('detailed information about a datasource'), and specifies the lookup method ('using its UID'). It distinguishes from sibling tools like 'get_datasource_by_name' by explicitly mentioning UID. However, it doesn't fully differentiate from 'list_datasources' or 'check_datasource_exists' in terms of scope or detail level.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_datasource_by_name', 'list_datasources', or 'check_datasource_exists'. It doesn't mention prerequisites (e.g., needing the UID) or exclusions (e.g., not for bulk operations). Usage is implied by the name 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 the full burden. It describes a read operation ('Get'), which implies it is likely non-destructive and read-only, but does not confirm this or disclose other behavioral traits such as authentication requirements, rate limits, pagination, or error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes helpful examples without unnecessary elaboration. Every word earns its place, making it easy for an agent to parse 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?

Given the tool's low complexity (single parameter, read operation) and high schema coverage, the description is adequate but incomplete. It lacks output information (no output schema provided) and behavioral details like pagination or error handling, which are important for a list/filter tool. This results in a minimal viable description with 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?

The input schema has 100% description coverage, with the single parameter 'type' documented as 'The datasource type to filter by'. The description adds minimal value beyond this by providing examples (e.g., prometheus, loki, mysql), which offer context but no additional syntax or format details. This meets the baseline of 3 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 ('Get') and resource ('datasources') with a specific filtering criterion ('of a specific type'), making the purpose unambiguous. It distinguishes from sibling tools like 'list_datasources' (which presumably lists all datasources without filtering) and 'get_datasource_by_name'/'get_datasource_by_uid' (which retrieve single datasources). However, it does not explicitly mention these distinctions, keeping it at a 4 instead of a 5.

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

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

The description implies usage context by specifying 'of a specific type' and providing examples (e.g., prometheus, loki, mysql), suggesting it should be used when filtering by type is needed. However, it does not explicitly state when to use this tool versus alternatives like 'list_datasources' or 'get_datasource_by_name', nor does it provide exclusions or prerequisites. This leaves 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 full burden. It states a read operation ('Get'), but doesn't disclose behavioral aspects like authentication requirements, rate limits, error handling, or what happens if no default exists. The description is minimal and lacks operational context needed for safe invocation.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately understandable without unnecessary elaboration.

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

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

For a zero-parameter read tool with no annotations and no output schema, the description is minimally adequate. It states what the tool does but lacks context about return format, error conditions, or organizational scope. Given the simplicity (no parameters), it meets basic requirements but leaves operational details unspecified.

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 no parameter documentation is needed. The description doesn't add parameter semantics (none exist), which is appropriate. A baseline of 4 reflects that the description doesn't need to compensate for any parameter gaps.

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

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

The description clearly states the verb 'Get' and the resource 'default datasource for the organization', making the purpose unambiguous. It doesn't explicitly distinguish from sibling tools like 'get_datasource_by_name' or 'get_datasources_by_type', but the specificity of 'default' provides some implicit 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 like 'get_datasource_by_name' or 'list_datasources'. The description implies it retrieves a specific organizational default, but doesn't clarify prerequisites, error conditions, or comparative use cases with sibling tools.

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

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

No annotations are provided, so the description carries full burden. It states the tool 'gets' folder details, implying a read-only operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, error handling (e.g., invalid UID), or what happens if the folder doesn't exist. This leaves significant gaps in understanding how the tool behaves in practice.

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, front-loaded sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple retrieval tool, 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, no output schema, and low schema coverage, the description is incomplete. It lacks details on return values (e.g., structure of folder details), error cases, and operational context, making it insufficient for an AI agent to reliably invoke the tool without additional assumptions.

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 1 parameter (uid) with 0% description coverage, and the description adds minimal semantics by specifying it's 'by UID'. However, it doesn't explain what a UID is (e.g., format, source, uniqueness), its constraints, or examples, failing to compensate for the low 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 'Get folder details by UID' clearly states the action (get) and resource (folder details), but it's vague about what 'details' includes (e.g., metadata, contents, permissions). It distinguishes from siblings like 'list_folders' by specifying retrieval by UID, but lacks specificity on the scope of returned 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?

No guidance is provided on when to use this tool versus alternatives like 'list_folders' or 'get_dashboard_by_uid' (a sibling tool with similar pattern). The description implies usage when you have a folder UID, but doesn't specify prerequisites, error conditions, or contextual best practices.

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 label names but does not describe the return format (e.g., list structure, pagination), error handling, rate limits, or authentication requirements. For a tool with zero annotation coverage, this is a significant gap in transparency, though it at least correctly implies a read-only operation without contradictions.

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

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

The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse. There is no wasted verbiage, earning a high score for conciseness.

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

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, output, or usage context. Without annotations or an output schema, more guidance would be beneficial, but it is not completely inadequate, aligning with a score of 3.

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

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

Schema description coverage is 100%, meaning the input schema already documents all three parameters (datasourceUid, start, end) with descriptions. The description adds no additional meaning beyond the schema, such as explaining why start and end times might be optional or how they affect the label retrieval. Thus, it meets the baseline of 3 where the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Get all label names available in a Loki datasource.' It specifies the verb ('Get'), resource ('label names'), and scope ('in a Loki datasource'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'get_loki_label_values' or 'get_prometheus_labels,' which prevents a score of 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_loki_label_values' (for values of a specific label) or 'get_prometheus_labels' (for Prometheus instead of Loki), nor does it specify prerequisites or contexts for usage. This lack of comparative or contextual advice limits its utility for an AI 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe important behavioral aspects: whether this is a read-only operation, what format the returned values take (list, array, etc.), if there are rate limits, authentication requirements, or potential side effects. For a tool with 4 parameters and 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, clear sentence with zero wasted words. It's front-loaded with the core purpose and efficiently communicates the essential function. Every word earns its place, making it easy to parse quickly.

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

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

Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (values format, structure), doesn't mention the optional time range parameters, and provides no behavioral context. For a data retrieval tool in a monitoring/observability context, this leaves significant gaps for an AI 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 4 parameters thoroughly. The description mentions 'a specific label' and 'Loki datasource,' which aligns with the 'label' and 'datasourceUid' parameters, but adds no additional semantic context beyond what the schema provides. The time range parameters ('start' and 'end') aren't referenced at all in the description.

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

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

The description clearly states the action ('Get all values') and target resource ('for a specific label in a Loki datasource'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'get_loki_labels' (which gets label names) and 'get_prometheus_label_values' (which is for Prometheus). However, it doesn't explicitly mention the time range filtering capability that the parameters provide.

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 'get_loki_labels' (for getting label names) or 'get_prometheus_label_values' (for Prometheus data), nor does it specify prerequisites or contextual constraints. The agent must infer usage 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.

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 series but doesn't describe the return format (e.g., JSON structure), potential errors (e.g., invalid selectors), rate limits, or authentication needs. For a read operation with no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Get series') and includes key details (label combinations, selectors, datasource). There is no wasted verbiage or redundancy, making it highly concise and well-structured for quick understanding.

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

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

Given the complexity (a read operation with 4 parameters, no annotations, and no output schema), the description is incomplete. It lacks information on the return values (e.g., what the series data looks like), error handling, and usage context compared to siblings. For a tool with no output schema, this omission is particularly problematic.

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

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

Schema description coverage is 100%, with all parameters well-documented in the schema (e.g., 'datasourceUid' as the Loki datasource UID, 'match' as series selectors). The description adds no additional parameter semantics beyond what the schema provides, such as examples of label matchers or time format details. 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 action ('Get series') and the resource ('from a Loki datasource'), specifying it retrieves label combinations matching selectors. It distinguishes itself from siblings like 'get_loki_labels' or 'query_loki' by focusing on series retrieval rather than labels or querying logs. However, it doesn't explicitly differentiate from 'get_prometheus_series', which might be a similar tool for a different datasource.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid datasource UID), exclusions, or comparisons to sibling tools like 'get_loki_labels' (for labels) or 'query_loki' (for log queries). This leaves the agent to infer usage based on the 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.

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 retrieves statistics, implying a read-only operation, but doesn't describe what statistics are returned, potential rate limits, authentication requirements, error conditions, or whether the operation is safe/destructive. This leaves significant gaps for an agent to understand the tool's behavior.

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

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

The description is a single, clear sentence with no wasted words. It front-loads the core purpose efficiently, 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what statistics are returned (e.g., metrics, time ranges, or data structure), potential side effects, or error handling. For a tool that likely returns complex performance data, this leaves the agent with insufficient context to use it effectively.

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

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

The input schema has 100% description coverage, with the single parameter 'datasourceUid' documented as 'The Loki datasource UID.' The description adds no additional parameter context beyond what the schema provides, such as format examples or where to find the UID. The baseline score of 3 reflects adequate but minimal value added.

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 statistics about ingestion and query performance from a Loki datasource.' It specifies the verb ('Get'), resource ('statistics'), and scope ('Loki datasource'), but doesn't explicitly differentiate it from sibling tools like 'get_loki_labels' or 'query_loki' that also interact with Loki datasources.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid datasource UID), compare it to similar tools (e.g., 'get_loki_series' for different data), or specify use cases (e.g., monitoring performance vs. querying logs).

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets' data but doesn't describe what format the results come in, whether there are rate limits, authentication requirements, or any side effects. For a read operation with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 without any wasted words. It's appropriately sized for a straightforward data retrieval tool and gets directly to the point.

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 tool with good schema coverage but no annotations or output schema, the description is minimally adequate. It states what the tool does but doesn't provide important context about return format, error conditions, or usage scenarios. The combination of description and schema covers the basics but leaves gaps in behavioral understanding.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description doesn't add any additional context about parameter usage beyond what's in the schema descriptions. The baseline of 3 is appropriate when the schema does the heavy lifting for 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 action ('Get') and resource ('all label names from a Prometheus datasource'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_prometheus_label_values' or 'get_prometheus_series', but the specificity of 'label names' provides some implicit distinction.

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

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

No guidance is provided on when to use this tool versus alternatives like 'get_prometheus_label_values' (which gets values for a specific label) or 'get_loki_labels' (for Loki datasources). The description only states what it does, not when it's appropriate or what prerequisites might exist.

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 doesn't describe how it behaves: no information about rate limits, authentication requirements, error conditions, response format, or whether it's a read-only operation (though implied by 'Get'). This leaves significant gaps for an agent to understand operational 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 a single, clear sentence that communicates the core purpose without any wasted words. It's appropriately sized for a tool with a straightforward function and well-documented schema.

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 insufficient. It doesn't explain what the tool returns (list of values? structured response?), doesn't mention behavioral aspects like performance or limitations, and provides no usage context. The agent would need to guess about important operational details.

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

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

The schema description coverage is 100%, so all parameters are documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema descriptions (e.g., it doesn't clarify the relationship between start/end parameters or provide examples). This meets the baseline expectation when schema coverage is complete.

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

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

The description clearly states the action ('Get all values') and resource ('for a specific label from a Prometheus datasource'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_prometheus_labels' or 'get_loki_label_values', but the specificity of 'Prometheus' and 'label values' provides inherent distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_prometheus_labels' (which likely lists label names) or 'get_loki_label_values' (for Loki datasources). There's no mention of prerequisites, constraints, or typical scenarios for usage.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get metadata' implies a read-only operation, it doesn't specify what 'metadata' includes (e.g., metric names, types, help text), whether there are rate limits, authentication requirements, or how results are structured. This leaves significant gaps for a tool that fetches data from an external system.

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

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

The description is a single, clear sentence that efficiently communicates the core purpose without unnecessary words. It's front-loaded with the essential information and has zero wasted content.

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 retrieves data from an external system (Prometheus) with no annotations and no output schema, the description is insufficient. It doesn't explain what 'metadata' encompasses, how results are returned, potential limitations, or error conditions. Given the complexity of interacting with a monitoring system, more contextual information 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 fully documents both parameters (datasourceUid and limit). The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining what a 'Prometheus datasource UID' represents or providing context for the limit parameter. 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 action ('Get metadata') and target resource ('all metrics from a Prometheus datasource'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_prometheus_labels' or 'get_prometheus_series' that also retrieve Prometheus data, so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for Prometheus data retrieval (e.g., get_prometheus_labels, get_prometheus_series), there's no indication of what makes this tool distinct or when it's the appropriate choice.

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 but offers minimal information. It doesn't specify whether this is a read-only operation, what permissions are required, how results are formatted (e.g., JSON array), or any rate limits. The description only states what the tool does functionally, not how it behaves operationally.

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 with zero wasted words. It's appropriately sized for a tool with good schema documentation and gets straight to the point without unnecessary elaboration.

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

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

For a tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (series metadata? actual data points?), doesn't mention error conditions, and provides no behavioral context. The 100% schema coverage helps, but the description alone leaves significant gaps in understanding the tool's complete 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%, providing clear documentation for all 4 parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3. It doesn't explain relationships between parameters (e.g., that 'start' and 'end' are optional time bounds for the series search).

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 ('Find series matching label matchers') and resource ('from a Prometheus datasource'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_prometheus_labels' or 'get_prometheus_label_values', which also query Prometheus metadata but for different resources.

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 'query_prometheus' (which might retrieve actual metric data) or 'get_prometheus_metadata' (which might provide different metadata), leaving the agent without context for 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 full burden. It states a read operation ('Get'), which implies non-destructive behavior, but doesn't disclose any behavioral traits such as error handling (e.g., what happens if UID is invalid), rate limits, authentication requirements, or response format. This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes directly to understanding 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 no annotations, no output schema, and low schema coverage (0%), the description is incomplete. It lacks details on behavioral aspects (e.g., errors, auth), return values, or parameter specifics. For a read tool with one parameter, this minimal description leaves too many open questions about how 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 0%, so the description must compensate. It mentions 'by UID', which aligns with the 'uid' parameter in the schema, adding semantic context that this is the identifier for retrieval. However, it doesn't explain the UID format, constraints, or examples. With one parameter and low schema coverage, this provides basic but incomplete parameter meaning.

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

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

The description clearly states the action ('Get') and resource ('team details by UID'), making the purpose understandable. It distinguishes from sibling 'list_teams' by specifying retrieval of a single team via UID rather than listing all teams. However, it doesn't explicitly mention what 'team details' includes or the specific scope.

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 team details for a specific UID, suggesting an alternative to 'list_teams' for bulk retrieval. However, it lacks explicit guidance on when to use this versus other team-related tools (none exist in siblings) or prerequisites like authentication needs. The context is clear 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 full burden for behavioral disclosure. It states this is a read operation, which is clear, but lacks details on permissions, error handling, or output format. It doesn't mention if the tool requires specific access rights, how it handles invalid paths, or what the return structure looks like (e.g., JSON with the listed metrics). This leaves significant gaps for a tool that reads test results.

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 key action ('Read') and specifies the exact metrics retrieved. There is no wasted language, and it directly communicates the tool's purpose without redundancy or unnecessary details.

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

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

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers what the tool does but lacks behavioral context (e.g., output format, error cases) and usage guidelines. For a simple read tool, this might suffice, but it doesn't provide a complete picture for reliable agent invocation without additional inference.

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 'path' parameter well-documented as 'Full path to the test run directory'. The description adds no additional parameter semantics beyond implying that the path leads to a RAMP test run. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't enhance or clarify parameter usage further.

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 ('Read') and the resource ('final result from a RAMP test run'), specifying the exact metrics retrieved (Gbps, kpps, klogps, status). It distinguishes from sibling tools like 'get_test_vitals' or 'summarize_run' by focusing on final results rather than ongoing metrics or summaries. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that a test run must be completed), nor does it differentiate from similar tools like 'get_test_vitals' or 'summarize_run'. Without any usage context, the agent must infer when this tool is 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, the description carries full burden but only states it's a read operation. It doesn't disclose behavioral traits like authentication needs, rate limits, return format (e.g., JSON structure, pagination), or error conditions. The phrase 'all VITAL metric samples' suggests comprehensive retrieval but lacks detail on scope constraints.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly.

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

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

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'VITAL metric samples' entail (e.g., types of metrics), the return format, or any behavioral context like data volume or access requirements, leaving significant gaps for 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%, with the parameter 'path' documented as 'Full path to the test run directory'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating value.

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

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

The description clearly states the verb 'Read' and the resource 'VITAL metric samples from a RAMP test run', specifying it's time-series data. It distinguishes from siblings like 'get_test_result' by focusing on vitals/metric samples rather than overall results, though it doesn't explicitly name alternatives.

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 time-series vital metrics from a test run, but doesn't explicitly state when to use this vs. alternatives like 'get_test_result' or 'query_sensor_metric'. No guidance on prerequisites or exclusions is 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 states it 'gets' presets (implying read-only), but doesn't disclose what format the presets come in, whether authentication is needed, if there are rate limits, or what happens on failure. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 exactly what the tool does without any wasted words. It's appropriately sized for a zero-parameter tool and front-loads the essential information.

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

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

For a zero-parameter read operation with no output schema, the description is minimally adequate but could be more complete. It doesn't explain what 'presets' means in practice, what format they come in, or provide any examples. Given the lack of annotations and output schema, more context about the return value would be helpful.

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

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

The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't waste space discussing non-existent parameters, earning a baseline score of 4 for not adding unnecessary information.

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

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

The description clearly states the action ('Get') and resource ('common time range presets for Grafana'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'validate_time_range' or explain what distinguishes 'presets' from other time range 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. With sibling tools like 'validate_time_range' and various query-building tools that might involve time ranges, there's no indication of when this preset retrieval is appropriate versus other time-related operations.

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: it stops any running test and restarts at the new rate, indicating it is a mutating operation with side effects. However, it lacks details on permissions, error handling, or rate limits, which are relevant for a tool that modifies system state.

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 and adds crucial behavioral context in the second. Both sentences are necessary and efficient, with no redundant or vague language, making it appropriately sized for the tool's complexity.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, behavior, and parameters adequately, but lacks details on output format or error conditions, which would be helpful for a mutating tool. The absence of an output schema means the description could do more to explain what happens after invocation.

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

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

The schema description coverage is 100%, with clear descriptions for both parameters ('Replayer ID' and 'Rate in Gbps'). The description adds minimal value beyond the schema by specifying 'rate in Gbps' in the opening sentence, but does not provide additional context like valid ranges or examples beyond what the schema already documents.

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 ('Set'), the target resource ('Ixia traffic replayer'), and the parameter ('specific rate in Gbps'), making the purpose explicit. It distinguishes itself from sibling tools like 'ixia_status' and 'ixia_stop' by focusing on rate configuration rather than status checking or stopping.

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 ('to set the Ixia traffic replayer to a specific rate') and implies when not to use it (e.g., for checking status or stopping, which are covered by 'ixia_status' and 'ixia_stop'). However, it does not explicitly name alternatives or list exclusions, which prevents a score of 5.

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 what information is returned (status attributes), but it doesn't disclose behavioral traits such as whether this is a read-only operation, potential errors (e.g., if the replayer doesn't exist), or any rate limits. The description is minimal and lacks crucial operational context.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Check the current status') and lists the returned attributes. There is no wasted verbiage, making it highly concise and well-structured for quick understanding.

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

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

Given the lack of annotations and output schema, the description is incomplete. It mentions what status attributes are returned but doesn't cover behavioral aspects like error handling or operational constraints. For a tool with no structured support, the description should provide more context to be fully helpful to an AI agent.

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

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

The input schema has 100% description coverage, with the single parameter 'replayer' documented as 'Replayer ID'. The description doesn't add any meaning beyond this, such as examples or format details. With high schema coverage, the baseline score of 3 is appropriate, as the schema already provides adequate parameter semantics.

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 ('Check') and resource ('Ixia traffic replayer'), and it enumerates the status attributes returned (running/stopped, rate, test model). However, it doesn't explicitly differentiate from sibling tools like 'test_status' or 'sensor_status', which might have overlapping domains, so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives. There are sibling tools like 'test_status' and 'sensor_status' that might be related, but the description doesn't mention them or clarify the specific context for 'ixia_status' (e.g., for Ixia-specific hardware vs. general tests).

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

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

No annotations are provided, so the description carries full burden. It states the tool stops/halts traffic generation, implying a destructive mutation, but doesn't disclose behavioral traits like whether this requires specific permissions, if it's reversible (e.g., via 'ixia_set_rate' to restart), rate limits, or error conditions. The description adds minimal context beyond the basic action.

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

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

The description is two concise sentences with zero waste, front-loaded with the core action ('Stop the Ixia traffic replayer') and followed by clarifying detail ('Halts all traffic generation...'). Every word earns its place without redundancy or ambiguity.

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

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

Given no annotations and no output schema, the description is complete for a simple stop command but lacks details on behavioral aspects (e.g., permissions, reversibility) and output (what confirmation or error is returned). It's adequate for basic use but has gaps for a mutation tool in a complex testing environment.

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

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

Schema description coverage is 100%, with the parameter 'replayer' documented as 'Replayer ID'. The description adds meaning by specifying this tool acts 'on the specified replayer', reinforcing the parameter's role. With 1 parameter and high schema coverage, the baseline is strong, and the description provides adequate complementary 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 ('Stop', 'Halts') and resource ('Ixia traffic replayer', 'all traffic generation'), with precise scope ('on the specified replayer'). It distinguishes from sibling tools like 'ixia_status' (check status) and 'ixia_set_rate' (adjust rate).

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 halting traffic generation is needed, but lacks explicit guidance on when to use this tool versus alternatives like 'stop_ramp_test' (which might stop broader tests) or prerequisites (e.g., replayer must be running). No exclusions or direct comparisons to siblings 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?

With no annotations provided, the description carries full burden but discloses nothing about behavioral traits. It doesn't mention if this is a read-only operation, requires permissions, has rate limits, or describes output format. This is inadequate for a tool with no annotation coverage.

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

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

The description is a single, efficient sentence with no wasted words. It's appropriately sized for a simple listing tool and front-loaded with the core action.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't address behavioral aspects, parameter usage, or output format, leaving significant gaps for an AI agent to understand and invoke the tool correctly.

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

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

Schema description coverage is 0%, and the description provides no information about the 'folderUID' parameter. It doesn't explain what this parameter does, when to use it, or its effect on the listing. The description fails to compensate for the lack of schema documentation.

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

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

The description states the action ('List') and resource ('alert rule groups'), but it's vague about scope and doesn't differentiate from sibling tools like 'list_alert_rules'. It lacks specificity about what 'all' means in context.

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

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

No guidance is provided on when to use this tool versus alternatives like 'list_alert_rules' or 'get_alert_rule'. The description offers no context, 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 for behavioral disclosure but offers none. It doesn't mention whether this is a read-only operation, what permissions are required, whether results are paginated, or what format the output takes. This leaves critical behavioral aspects undocumented.

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

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

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple listing operation and front-loads the essential information.

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

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

For a tool with 2 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain parameter usage, behavioral characteristics, or output format. Given the complexity of alert rules in Grafana and the existence of similar sibling tools, more context is needed 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?

The schema has 0% description coverage for its 2 parameters (folderUID, ruleGroup), and the tool description provides no information about them. While the description mentions 'all alert rules', it doesn't explain how these parameters might filter or scope the results, leaving their purpose completely undocumented.

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

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

The description clearly states the action ('List') and resource ('all alert rules in Grafana'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from its sibling 'list_alert_rule_groups', which could cause confusion about scope differences.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_alert_rule' (for single rules) or 'list_alert_rule_groups' (for grouped rules). There's no mention of prerequisites, context, or 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?

With no annotations provided, the description carries full burden for behavioral disclosure. 'List all API keys' implies a read-only operation but doesn't specify permissions required, pagination behavior, rate limits, or output format. This is inadequate for a tool that likely handles sensitive authentication data.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and understandable.

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 lists sensitive API keys with no annotations and no output schema, the description is insufficient. It doesn't address security implications, return format, or operational constraints, leaving significant gaps in understanding how to use this tool effectively.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, earning a baseline score of 4 for not adding unnecessary information.

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

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

The description 'List all API keys' clearly states the verb ('List') and resource ('API keys'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_datasources' or 'list_users' beyond the resource type, missing explicit sibling distinction.

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, or exclusions, leaving the agent to infer usage based on the name alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists data and allows optional filtering, but doesn't describe key behaviors like whether it's read-only (implied by 'list'), what format the output is (e.g., JSON array, paginated), error handling, or rate limits. For a tool with 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 appropriately sized and front-loaded: it's a single, efficient sentence that states the core purpose and key usage detail (optional filtering) without waste. Every word earns its place, making it easy to parse quickly.

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

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

Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter usage but lacks details on output format, error cases, or behavioral traits. For a simple list tool, this is acceptable but not comprehensive.

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 the parameter 'sensorType' fully documented in the schema. The description adds minimal value beyond the schema by mentioning example values ('AP1100', 'AP3000') and clarifying that omission lists all builds, but this is largely redundant. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'List available builds and profiles from baselines.json' specifies the verb ('list') and resource ('builds and profiles from baselines.json'). It distinguishes itself from siblings like 'compare_builds' or 'list_test_runs' by focusing on baselines data. However, it doesn't explicitly differentiate from potential similar tools (none exist in the sibling list), so it's not a perfect 5.

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

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

The description provides implied usage guidance: it mentions optional filtering by sensor type (e.g., 'AP1100', 'AP3000'), which suggests when to use the parameter. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to 'list_test_runs' or other list tools) and doesn't specify prerequisites or exclusions, leaving room for ambiguity.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool lists contact points but doesn't describe return format, pagination, sorting, permissions required, rate limits, or error conditions. For a read operation with zero 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 a single, efficient sentence that states exactly what the tool does with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and understandable.

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, read-only operation) and lack of output schema, the description is minimally adequate. However, without annotations or output schema, it should ideally provide more behavioral context about what 'list' entails (e.g., format, limitations). The description covers the basic purpose but leaves operational details unspecified.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, focusing instead on the tool's purpose. This meets the baseline for tools with no 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 action ('List') and resource ('notification contact points'), making the tool's purpose immediately understandable. It distinguishes itself from sibling tools like 'get_contact_point' (singular) by specifying 'all' contact points. However, it doesn't explicitly differentiate from other list tools like 'list_alert_rules' or 'list_datasources' 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or comparisons to sibling tools like 'get_contact_point' (for retrieving a specific contact point) or 'test_contact_point' (for testing functionality). The agent must infer usage from the tool name alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List all configured datasources') but doesn't describe what 'details' include, whether the list is paginated, if it requires specific permissions, or any rate limits. This leaves significant gaps for a tool that likely returns sensitive configuration data.

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 any unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly.

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

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral details like response format or access requirements. For a tool that lists datasources—potentially sensitive configurations—more context would be helpful, but the absence of complex schema elements keeps it from being severely incomplete.

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

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

The input schema has 0 parameters with 100% coverage, so there are no parameters to document. The description appropriately doesn't mention any parameters, which is correct for this case. A baseline of 4 is applied since no parameter information is needed beyond what the schema already indicates (empty object).

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 configured datasources with their details'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_datasource_by_name' or 'get_datasources_by_type', which are more targeted retrieval operations.

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

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

The description provides no guidance on when to use this tool versus alternatives. There are several sibling tools for datasource retrieval (e.g., 'get_datasource_by_name', 'get_datasources_by_type'), but the description doesn't mention any context or exclusions for choosing this broad listing tool over more specific ones.

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. 'List all folders' implies a read-only operation, but it does not specify permissions required, pagination behavior, rate limits, or what 'all' entails (e.g., recursive listing). For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is extremely concise with 'List all folders', a single phrase that front-loads the core action. There is no wasted language or unnecessary elaboration, making it efficient and easy to parse for an AI agent.

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 listing operations (which often involve permissions, pagination, or filtering) and the lack of annotations and output schema, the description is incomplete. It does not address behavioral aspects like return format, error handling, or how 'all' is defined, leaving gaps for proper tool invocation.

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

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

The input schema has 0 parameters with 100% description coverage, meaning no parameters are documented in the schema. The description does not mention any parameters, which is appropriate since there are none. This aligns with the baseline for zero parameters, as no additional semantic information is needed beyond the schema.

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

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

The description 'List all folders' clearly states the verb ('List') and resource ('folders'), providing a basic purpose. However, it lacks specificity about scope (e.g., all folders in what context?) and does not differentiate from sibling tools like 'get_folder_by_uid' or 'search_dashboards', which might also involve folder operations. It avoids tautology but remains vague.

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 does not mention any context, prerequisites, or exclusions, such as when to prefer 'list_folders' over 'get_folder_by_uid' for specific folder retrieval. Without such information, usage is implied but not explicit.

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. 'List all service accounts' implies a read-only operation but doesn't disclose behavioral traits like pagination, sorting, filtering, authentication needs, rate limits, or what 'all' entails (e.g., across an organization or project). For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, and there's no redundancy 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of service account objects, their properties, or any metadata), behavioral aspects like pagination, or error conditions. For a tool that likely interacts with a system like Grafana or cloud services, more context is needed 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?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter semantics, and it doesn't incorrectly suggest any parameters. A baseline of 4 is appropriate for a zero-parameter tool with complete 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 'List all service accounts' clearly states the action (list) and resource (service accounts) with a specific verb. However, it doesn't distinguish this tool from other list_* siblings like list_users or list_teams, which follow the same pattern but target different resources. The purpose is clear but lacks sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. There are multiple other list_* tools (e.g., list_users, list_teams, list_datasources) that likely operate similarly but on different resources, but the description doesn't help an agent choose between them. No context, exclusions, or prerequisites are mentioned.

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

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

With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits. It doesn't mention pagination behavior (implied by parameters), rate limits, authentication needs, or what 'all teams' entails (e.g., archived teams). This leaves significant gaps for a read operation.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded and directly states the tool's purpose without unnecessary elaboration, 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, 0% schema coverage, no output schema, and two parameters, the description is incomplete. It lacks details on pagination, return format, error handling, or scope limitations, which are critical for a list operation in this context with many sibling tools.

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

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

Schema description coverage is 0%, but the description doesn't add any parameter details beyond what's inferred from 'List all teams'. The parameters 'page' and 'perpage' are undocumented in both schema and description, though their purpose is somewhat obvious for pagination. This meets the baseline for minimal compensation.

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 'List all teams in the organization' clearly states the verb ('List') and resource ('teams in the organization'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_team_by_uid' or 'list_users', which reduces it from a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_team_by_uid' for retrieving a specific team. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based on the tool name alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists test runs and supports filtering, but doesn't describe key behaviors such as pagination, sorting, error handling, or what the output format looks like (e.g., list of objects, JSON structure). For a read operation with zero 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 a single, efficient sentence that front-loads the core action ('List RAMP test runs') and adds filtering details without waste. Every word contributes to understanding the tool's purpose and capabilities, 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 has no annotations, no output schema, and a simple input schema with full coverage, the description is minimally adequate. It covers the basic purpose and filtering options, but lacks details on behavioral aspects like output format or error conditions. For a read-only tool in this context, it meets the minimum viable threshold but could be more complete.

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 both parameters ('date' and 'sensor') fully documented in the input schema. The description adds marginal value by mentioning filtering by date or sensor name, but doesn't provide additional semantics beyond what the schema already specifies (e.g., date format details or sensor name examples). 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 verb ('List') and resource ('RAMP test runs from the results directory'), making the purpose understandable. It distinguishes from some siblings like 'start_ramp_test' or 'stop_ramp_test' by focusing on listing rather than controlling tests, but doesn't explicitly differentiate from other list tools like 'list_baselines' or 'list_datasources' in terms of domain context.

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

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

The description implies usage by mentioning filtering capabilities ('Filter by date or sensor name'), which suggests when to use this tool for retrieving test runs with specific criteria. However, it doesn't provide explicit guidance on when to choose this over alternatives like 'get_test_result' or 'summarize_run', nor does it mention prerequisites or exclusions.

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