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

With no annotations, the description must carry the behavioral burden, and it does disclose that host LLM sampling may be used with a fixed tool-chain fallback. However, it omits safety profile, permissions, failure behavior, and whether the workflow performs only reads or can mutate 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?

Two sentences, front-loaded with the core purpose and followed by the key fallback behavior. There is no wasted text.

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 multi-step orchestration tool with no annotations and 0% schema coverage, the description is too thin: it does not identify constituent tools, explain the goal input, or describe operational constraints. The output schema covers return values, but the input and orchestration behavior remain underspecified.

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 should explain both parameters, but it only indirectly hints at use_sampling and never defines the required goal parameter. An agent cannot infer expected goal format or the effect of setting use_sampling to false.

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 a specific activity — multi-step observability triage — and gives an ordered sequence (stack → logs → health). It distinguishes itself from single-purpose siblings by presenting itself as a workflow, though it does not name the concrete tools it orchestrates.

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

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

It implies usage for triage scenarios and explains a sampling fallback, but it does not explicitly say when to choose this workflow over calling check_stack_status, query_loki_logs, or monitor_server_health individually. There are no 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?

Since no annotations exist, the description must carry the behavioral load. It successfully adds key operational context by stating it 'Uses statistical analysis and configurable thresholds,' informing the agent that this is a dynamic, rather than deterministic, alerting 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 tool description is appropriately sized at two sentences. It uses the first sentence for a high-level summary and the second sentence for the mechanism, creating a logical flow.

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

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

While the description mentions 'configurable thresholds,' it fails to clarify who configures these thresholds or where they are stored. This ambiguity could be a blocker for an agent trying to determine if the alert behavior is fixed or dynamic.

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 coverage is 100%. The description only reinforces that the monitoring is performed on 'MCP server performance metrics' and does not provide any additional semantic meaning or constraint for the 'service_name' parameter beyond what the schema already defines.

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 goal: monitoring performance anomalies and triggering alerts. It distinguishes itself from general monitoring by specifying the use of statistical analysis and configurable thresholds, setting it apart from basic metric collection.

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 is purely functional and offers no comparative guidance. It does not explain when this tool should be used relative to siblings like 'monitor_server_health' or 'collect_performance_metrics,' leaving the user to infer its specific boundary in the observability stack.

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 disclosure burden. It does convey that analysis happens over a time window and that patterns must meet a significance bar, but it omits whether the call is read-only, whether it requires a live Loki connection, and any cost/latency implications of scanning large windows.

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 short sentences, front-loaded with the core action. The second sentence partially restates the first ('identifies common log patterns' echoes 'analyze log patterns'), so it is efficient but not maximally tight.

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?

With an output schema present and full schema coverage, return values and parameters are covered elsewhere. What is missing is the operational context an agent needs to choose this tool over its siblings and to know what kind of LogQL query is appropriate as input.

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

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

Schema description coverage is 100%, so each of the three parameters is already documented in the schema, including the LogQL query base and time-window format. The description adds no further semantics such as whether the base query may contain aggregation or how min_occurrences interacts with the window.

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?

States a specific verb+resource ('Analyze log patterns and anomalies using Loki queries') and elaborates with what it surfaces: patterns, error spikes, potential issues. It is clearly distinguishable from raw retrieval tools like query_loki_logs, though it never names the sibling it differs from.

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?

Usage is only implied by 'analyzing log data over time windows'. There is no explicit statement of when to choose this over query_loki_logs or correlate_logs_and_metrics, and no prerequisites or exclusions are given.

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

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

No annotations are provided, so the description carries the full behavioral burden, and it partially does: it discloses the data source (trace data) and the analytical nature of the operation. However, it says nothing about whether the operation is read-only, how expensive it is over large day ranges, or data retention limits, leaving meaningful gaps for a no-annotation 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 opening sentence is front-loaded and to the point, but the second sentence largely restates the first with softer wording ('provide insights into how MCP servers are being used'), adding little new 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?

With an output schema present, return values need not be explained, and with one simple param and full schema coverage, the definition is serviceable. It still lacks sibling differentiation and any hint of what 'optimization opportunities' concretely means, which matters in a crowded observability toolset.

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% and the single 'days' parameter is fully documented in the schema, so the schema already does the work. The description adds no information about the time window or its defaults, making the baseline 3 correct.

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 gives a clear verb+resource: analyzing patterns in MCP server interactions and usage. It identifies the data source (trace data) and the output goal (insights, optimization opportunities), but it never distinguishes itself from close siblings like trace_mcp_calls, analyze_log_patterns, or generate_performance_reports.

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

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

Usage is only implied through the phrase 'identify optimization opportunities,' which hints at the tool's purpose but gives no explicit when-to-use condition. There is no guidance on when to prefer this over trace_mcp_calls (raw tracing) or generate_performance_reports (reporting).

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 must carry the behavioral burden; it does disclose the target layer (Docker-based services) and what is verified (reachability and correct configuration), which is meaningful context. It does not state that the call is read-only/non-mutating, nor what happens when a service is down (error vs. degraded status), leaving the safety and failure profile implicit.

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 short sentences, front-loaded with the resource scope. The second sentence adds real detail (Docker-based, configured) rather than restating the first, though 'diagnostic tool' is mild filler.

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?

An output schema exists, so return values need no explanation, and there are no parameters to document. The description covers scope, component list, and the layer being probed; the only remaining gap is the read-only/no-side-effect assurance for a diagnostic call.

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 takes zero parameters and the schema is a closed empty object, so there is nothing for the description to disambiguate. Baseline 4 applies.

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?

States a specific verb (check) and resource (health and connectivity of the observability stack) and enumerates the components covered: Loki, Prometheus, Grafana. That scope implicitly separates it from generic siblings like monitor_system_resources and monitor_server_health, but it never names or explicitly rules out those 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?

Calling itself a 'diagnostic tool' that verifies reachability implies the use case (troubleshooting connectivity/configuration), but there is no explicit when-to-use statement, no prerequisites, and no guidance on choosing it over show_status_dashboard, observability_stack_health_card, or monitor_server_health.

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 exist, so the description carries the full burden. It usefully discloses the mechanism (OpenTelemetry, psutil) and that results are persisted for historical analysis — a real side effect — but says nothing about cost, latency, permission requirements, or whether collection blocks or impacts the monitored service.

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?

Three short sentences with the action front-loaded and no filler. The OpenTelemetry/psutil sentence is implementation detail that slightly exceeds what an agent needs for selection, but it is not wasteful enough to penalize further.

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?

An output schema exists, so return values need no explanation, and the single parameter is fully specified. What remains thin is the side-effect/permission profile for a call that writes persisted metrics, but overall the description is sufficient to 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 coverage is 100% and the single parameter is fully documented with its default and length limit. The description only echoes the 'system or specific service' distinction already expressed by the service_name default, so it adds little 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?

States a specific verb and resource ('Collect ... performance metrics') plus the scope choice ('system or specific service'). It is distinguishable from neighbors like export_metrics and generate_performance_reports by implying in-place collection rather than export or reporting, but it never names a sibling to sharpen the boundary.

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?

Usage is only implied: you gather metrics when you want current performance data. There is no explicit statement of when to prefer this over monitor_system_resources, monitor_server_health, or export_metrics, and no prerequisites or exclusions are given.

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

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

No annotations are provided, so the description carries the full behavioral burden. It describes the intended analysis outcome but does not disclose whether the tool is read-only, what permissions or backend availability are required, how expensive the combined query is, or what happens when one data source fails.

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 short and front-loaded, stating the core correlation purpose in the first sentence. The second sentence is somewhat generic and partially repeats the incident/root-cause framing, but it remains concise overall.

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

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

With an output schema present, the description does not need to explain return values, and the input schema is fully documented. However, the absence of annotations and any usage guidance leaves gaps around when to use this tool versus related log/metric tools and what operational behavior to expect.

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

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

Schema description coverage is 100%, so the input parameters are already well documented as LogQL, PromQL, and a time window. The description adds only the high-level idea of combining Loki and Prometheus, without additional parameter semantics 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 states a specific verb and resource: correlate log entries with metrics for incident analysis, combining Loki and Prometheus. This is clearer than a generic observability tool, but it does not explicitly distinguish itself from siblings such as query_loki_logs or collect_performance_metrics.

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

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

The phrase 'for comprehensive incident analysis' and 'root cause analysis' implies when the tool is useful, but there is no explicit guidance on when to choose it over querying logs or metrics separately. No exclusions or named alternatives 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 behavioral burden. It never says whether export is a pull/read operation or a side-effecting push, whether output is returned inline or written to a file/endpoint, what include_history costs in volume, or what permissions are needed. Only the format enumeration is disclosed, which the schema already covers.

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 short sentences that lead with the core action. The second sentence largely restates the format list already present in the schema, so it earns its place only partially, but there is no filler.

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?

An output schema exists, so return values need not be explained, and the tool is simple with two optional parameters. However, for a tool with zero annotations, the description omits whether the export is read-only, how data is delivered, and how it relates to the metric-collection and reporting siblings, leaving an agent with gaps before calling it.

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% and both parameters (format, include_history) are documented with defaults and allowed values in the schema itself. The description repeats the format names but adds no syntax, sizing, or trade-off detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource ('Export collected metrics') plus the supported output formats. It is reasonably distinguishable from siblings like collect_performance_metrics or generate_performance_reports, though it never explicitly contrasts itself with them.

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?

Usage is only implied through the phrase 'for external monitoring systems' and 'integration with existing monitoring infrastructure.' There is no explicit guidance on when to choose this over collect_performance_metrics or generate_performance_reports, nor any stated 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains that the tool analyzes historical metrics and generates reports, but it does not state whether the operation is read-only, whether reports are persisted or returned, what permissions are required, or whether there are cost/rate-limit considerations.

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 short and front-loaded: the first sentence states the core action, and the second sentence adds useful detail about the analysis scope. There is little waste, though the second sentence partially restates the first and could be tightened.

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?

An output schema exists and the input schema is fully documented, so return values and parameter meanings do not need to be explained. However, with no annotations and many sibling tools, the description should provide more usage routing and operational context to be complete for an agent choosing among metrics collection, reporting, and analysis 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 schema description coverage is 100%, so both parameters (days and service_name) are fully documented in the input schema. The description adds no parameter-specific detail, but the baseline of 3 applies when the schema already provides high 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 states a clear verb and resource: generate performance reports with automated analysis. It further specifies that the reports analyze historical metrics for insights, trends, and optimization recommendations, which is more specific than the tool name alone. However, it does not explicitly distinguish this from siblings like collect_performance_metrics or analyze_mcp_interactions.

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?

Usage is implied: the tool is for producing performance reports and analysis from historical metrics, rather than collecting raw metrics or exporting data. But the description gives no explicit when-to-use guidance, no when-not conditions, and does not name alternative sibling tools such as collect_performance_metrics or analyze_mcp_interactions.

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 for a state-mutating tool. It never states whether add/remove/toggle take effect immediately, whether removal is reversible, what permissions are needed, or what happens if the metric name doesn't exist.

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

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

Front-loaded one-line purpose followed by a compact operation list; every line conveys either an action or a precondition, with no filler.

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?

An output schema exists so return values need not be described, and the operation enumeration covers the call surface. Still, with zero annotations on a mutation tool and an unspecified 'alert' payload schema, an agent cannot confidently construct a valid 'add' call or predict side effects.

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 does important compensating work by defining the legal values of 'operation' (the schema has no enum) and tying 'metric_name'/'alert' to specific operations. But the 'alert' dict — an open additionalProperties object — is left entirely opaque, which is the most consequential parameter for 'add'.

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?

States a specific verb and resource ('Manage metric alert configurations') and then enumerates the four supported operations, which makes it clearly distinguishable from siblings like alert_on_anomalies (event alerting) and the dashboard/datasource management 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 operation list with per-operation requirements ('add' requires 'alert' dict, 'remove'/'toggle' require 'metric_name') tells the agent which operation to pick and what it needs. However, there is no explicit when-to-use-vs-alternatives guidance and no mention of how this differs from alert_on_anomalies.

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 the full burden; it does important work by disclosing that `create` is actually an upsert ("Create or update"), which is non-obvious. However, it never states that `delete` is irreversible or what it destroys, nor permission/auth requirements. Output schema exists, so return-shape disclosure is not required.

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?

One lead sentence plus a bulleted operation list, front-loaded with the general purpose and then scannable per operation. Zero filler; every line maps to a distinct callable behavior.

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 3-parameter multi-operation tool with an output schema, the description covers all parameters and all operations adequately. The remaining gap is that destructive semantics of `delete` and any auth prerequisites go unstated.

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 coverage is 0% and `operation` is an unconstrained string, so the description is the only place the valid operations and their parameter pairings are defined — a substantial addition over the schema. It still omits the expected UID format and the required shape of `dashboard_json`, but the operation-to-parameter mapping is clearly conveyed.

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?

States a specific verb+resource ("Manage Grafana dashboards") and enumerates the four supported operations, which is essential because the `operation` param has no enum in the schema. It does not distinguish itself from the sibling `provision_standard_dashboards`, which could plausibly be confused with the `create` path.

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?

Each operation carries its prerequisite ("requires 'dashboard_json'", "requires 'uid'"), giving clear per-operation invocation context. It stops short of saying when to pick this tool over `provision_standard_dashboards` or `manage_grafana_datasources`, so no exclusions or alternatives are offered.

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 the full burden: it discloses per-operation input requirements and implicitly signals that 'remove' mutates/deletes and 'test' is a connectivity probe. It does not state that removal is irreversible, what permissions are required, or what 'add' actually provisions, leaving real behavioral gaps for a mutation-capable 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?

Front-loads the resource, then uses a tight bulleted list where each line is one operation plus its required parameter. No filler, no repetition of the schema, and it is easy to scan.

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?

An output schema exists, so return values need not be explained, and the description covers all three parameters at the mapping level plus the missing operation enum. The remaining gap is the contents of ds_json and any permission/irreversibility notes, which is modest given the schema richness elsewhere.

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 'operation' property has no enum, so the description's operation list is the only place the valid values appear — a meaningful addition. But it only maps ds_id/ds_json to operations; the required structure and keys of ds_json (the datasource config object) remain completely undefined.

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?

Names a specific resource (Grafana datasources) and enumerates four concrete operations (list/add/remove/test), so the agent knows exactly what capability set is behind it. It is distinguishable from manage_grafana_dashboards by resource, though the description never explicitly contrasts the two.

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?

Usage is implied by the operation names and the per-operation parameter requirements (add needs ds_json, remove/test need ds_id), so an agent can pick the right operation. However, there is no guidance on when to choose this tool over siblings such as manage_grafana_dashboards or provision_standard_dashboards, and no prerequisites or exclusions are stated.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context—real-time checking, OpenTelemetry-based metrics collection, and detailed response analysis—but does not state whether the operation is read-only, whether it requires authentication, how failures are handled, or whether it has side effects. It is adequate but incomplete for a monitoring tool with zero annotation coverage.

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

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

The description is two sentences and front-loads the core operation. The second sentence adds some context but includes slightly fluffy marketing-like phrasing ('comprehensive health monitoring with detailed response analysis') that could be tightened. Nonetheless, it is efficient overall.

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

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

An output schema exists and the input schema is fully documented, so the description need not explain return values or parameter details. However, for a health-check tool with no annotations, the description is missing key contextual elements: when to use it versus similar siblings, whether it is safe/read-only, and how failures are surfaced. It is minimally adequate given the structured data richness but leaves notable gaps.

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

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

Schema description coverage is 100%, so the input schema already documents all three parameters thoroughly, including formats, defaults, and allowed ranges. The description adds no additional parameter meaning or usage nuance beyond what the schema provides, making the baseline 3 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?

States a specific verb and resource: 'Perform real-time health check on an MCP server or web service.' The purpose is clear and concrete. It does not explicitly differentiate itself from sibling tools like check_stack_status, which likely performs a similar health-check function, so it falls short of the 5 criteria.

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 when-to-use guidance, no prerequisites, and no alternatives. It does not explain when an agent should choose monitor_server_health over check_stack_status, monitor_system_resources, or other sibling tools. Usage is only implied by the phrase 'health check.'

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 the full behavioral burden; it does disclose the categories of data gathered and that status is 'real-time,' which tells the agent this is a read-style inspection. It does not say whether the call is side-effect free, whether it is expensive or rate-limited, or how fresh the measurements are, so meaningful gaps remain for a no-annotation 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?

Two short sentences, front-loaded with the action and scope. The second sentence partly restates the first ('provide real-time status' vs. 'collects comprehensive system resource information'), a small redundancy but no wasted structure.

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

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

An output schema exists, so return values needn't be explained, and the inputs are empty. What is missing is the selection context an agent needs among a dozen observability siblings: which scope this covers and when it should be preferred over monitor_server_health or collect_performance_metrics.

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 takes zero parameters and the schema is fully described, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. No misleading parameter claims are made.

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?

States a specific verb and resource: 'Monitor system-wide resources and provide real-time status,' and the second sentence enumerates the collected signals (CPU, memory, disk, network, process). It is clear what the tool does, but it never distinguishes itself from close siblings such as monitor_server_health or collect_performance_metrics.

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

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

There is no statement of when to use this tool versus the several overlapping siblings in the list (monitor_server_health, collect_performance_metrics, check_stack_status). The agent must infer scope from the word 'system-wide' alone, with no exclusions or prerequisites offered.

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 behavioral burden, yet it says nothing about what the card contains, whether it is read-only, or what form the output takes. The word "card" weakly implies a static read-only summary, but that is inference, not disclosure.

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

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

A single short phrase with no filler, but its brevity comes from under-specification rather than tight writing. The key noun phrase is front-loaded but opaque.

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?

With no output schema, no annotations, and no parameter documentation, the description is the only source of information about what the agent gets back, and it does not say. An agent cannot predict the response content or decide when this card is the right one.

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 takes zero parameters, so there is nothing for the description to explain beyond what the empty schema already conveys. Baseline 4 applies.

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?

"Prefab card: SOTA manifest and tool surface summary" restates the tool name with jargon rather than stating a concrete action. "Prefab card" and "SOTA" are undefined, and nothing distinguishes it from sibling card tools like observability_stack_health_card or observability_grafana_dashboards_card.

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?

There is no indication of when to call this versus the two sibling "_card" tools or the many status/monitoring tools. The agent must guess its place in the observability surface.

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 the full burden, and it does declare the read-only trait plus the fact that it produces a prefab card. It says nothing about what happens if Grafana is unreachable, whether it performs a live fetch, or what the returned markup contains. Adequate but thin for a zero-annotation 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?

A single short sentence with the resource and the read-only property front-loaded; nothing is wasted. It is arguably under-specified rather than over-long, but structurally clean.

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?

There is no output schema, so the description is the only source for what the caller gets back; 'prefab card' is not explained and the return shape (rendered UI vs. data) is left to inference. For a trivial read-only card this may be enough, but an agent cannot predict the response structure.

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

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

The tool takes no parameters, so the baseline is 4. Nothing in the description needs to explain arguments, and it correctly implies no user input is required.

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?

States a specific resource (Grafana dashboard list) and the output form (prefab card), which is enough to distinguish it from manage_grafana_dashboards. It does not explicitly contrast with the sibling, but the noun phrase is unambiguous.

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

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

No guidance on when to use this card versus manage_grafana_dashboards, agentic_observability_workflow, or the other observability_*_card tools. The only implicit signal is '(read-only)', which hints at view-only use but does not state alternatives 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. It never says whether the tool is read-only, whether it performs live queries against Prometheus/Loki or renders static content, or what a caller receives back. 'Prefab card' hints at a display artifact but never states it.

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?

A single short sentence with no waste, and the substantive content (PLG stack health) is front-loaded after the label. The 'Prefab card:' prefix is internal jargon that spends tokens without adding meaning for an 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?

For a zero-param tool with no annotations and no output schema, the description should at minimum say what the caller gets back and whether the operation is a safe read. Neither is covered, and the 'Prefab card' framing is unexplained, leaving the agent to guess at the return contract.

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 takes zero parameters, so per the baseline there is nothing for the description to disambiguate. The named scope (Grafana, Prometheus, Loki) is the only semantic content needed and it is present.

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 names a resource (unified PLG stack health across Grafana, Prometheus and Loki) and distinguishes it from sibling cards like observability_grafana_dashboards_card and observability_capabilities_card by scope. However, the verb is only implied by the jargon 'Prefab card' — an agent cannot tell whether invoking this renders a UI component, returns a card spec, or executes a health check.

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?

There is no statement of when to use this card versus check_stack_status, show_status_dashboard, or the other two '_card' siblings. The agent must infer usage from 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 behavioral burden for a write operation. It never says whether the operation is idempotent, whether it overwrites or duplicates existing dashboards of the same name, what permissions or a reachable Grafana/datasource are required, or what happens on partial failure.

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 effect is stated in the first sentence and the numbered list is compact and scannable, with no filler prose. The list is slightly redundant (the names are self-describing) but it usefully confirms scope.

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

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

An output schema exists, so return-value explanation is not required, and the effect list is complete. Still, for an unannotated mutating tool the description omits prerequisites and idempotency/overwrite behavior, which is the kind of context an agent needs before running setup against a live Grafana instance.

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 takes zero parameters and the description correctly presents it as a no-argument bootstrap action, so there is no parameter meaning an agent could miss. Per the zero-parameter baseline this lands at 4 rather than 5, since the enumerated dashboard names do not function as 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?

Specific verb (provision/set up) plus resource (Grafana dashboards), and it enumerates exactly which three dashboards are created, so the agent knows the concrete effect. It does not, however, distinguish itself from the sibling manage_grafana_dashboards, which sounds like it could also create dashboards.

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?

There is no explicit when-to-use guidance and no routing away from the close siblings manage_grafana_dashboards or manage_grafana_datasources. Usage is only implied by the word 'standard', leaving the agent to guess whether this is the bootstrap path or a duplicate of the generic management 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 the full burden. It is silent on whether the call is read-only, what the default time window is when start_time/end_time are null, whether results are truncated at the limit, and whether there are rate or timeout constraints — all critical for a log query 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?

It is short and front-loaded with the core verb, but the second sentence is largely promotional ("powerful log querying capabilities") and its correlational framing earns little. Roughly half the text is filler that could be spent on behavior.

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?

An output schema exists and parameter docs are complete, so return-value explanation is rightly omitted. However, for a LogQL query tool the description leaves undefined the default time range, result-limit behavior, and read-only nature — gaps the agent must guess at when invoking.

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 parameter baseline is 3. The description adds the term LogQL and implies filter construction, but supplies no extra meaning about limit truncation, relative time syntax, or default range 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 first sentence gives a specific verb and resource ("Query logs from Loki") and the schema confirms the dialect is LogQL. It does not differentiate this tool from adjacent siblings such as analyze_log_patterns, correlate_logs_and_metrics, or send_logs_to_loki, so an agent has to infer the boundary rather than read it.

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?

There is no statement of when to use this tool versus the pattern-analysis or correlation siblings, nor any prerequisites (datasource selection, tenant, permissions). The closing phrase about "correlation between logs, metrics, and traces" gestures at a use case but never names an alternative or an exclusion.

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 burden; it does disclose that this is a manual injection into an external aggregation system and implies an append-style write. It does not cover auth/permission requirements, whether ingestion is asynchronous or how failures surface, and it omits the label cap that only exists in the schema. The presence of an output schema reduces the need to describe return values.

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 the core action front-loaded in the first. There is minor redundancy between "centralized log aggregation" and "Loki log aggregation system" in the second sentence, but nothing that wastes meaningful 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 simple 3-parameter tool with full schema coverage and an output schema, the description covers the essentials of what it does. It leaves the main gaps around write semantics (sync vs async ingestion, permission needs) and when to prefer query_loki_logs or correlate_logs_and_metrics.

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% and all three parameters (level, labels, log_message) are documented in the schema, including the max-10 label rule, so the description can legitimately defer. It adds no format or convention guidance (e.g. label naming, level selection) beyond the structured fields, so the baseline 3 applies.

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?

States a specific verb+resource ("Send custom log entries to Loki") plus the outcome ("centralized log aggregation"), which distinguishes it from the read-side sibling query_loki_logs. It does not explicitly name that sibling, so differentiation is done only implicitly via the verb.

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 second sentence gives an implied use case ("manual log injection ... enabling correlation between custom events and system metrics"), which tells the agent roughly why to reach for this tool. However, it never states when NOT to use it, nor names query_loki_logs / correlate_logs_and_metrics as the alternative for the correlation goal.

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 the full burden; it earns credit for disclosing the Degraded Mode fallback when Docker or stack services are unreachable, which is genuinely useful behavioral context. However, it does not state that it is read-only, that it requires a Prefab-capable UI client, or what is returned to a non-visual consumer.

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?

Three short sentences, front-loaded with the core action and followed by content scope and resilience behavior; no wasted preamble. The word 'premium' is mild marketing filler but does not impede comprehension.

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

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

For a parameterless, annotation-free, output-schema-free rendering tool, the description covers what it shows and how it behaves when the backend is down. The main residual gap is whether the caller receives an image, a payload, or a client-side render, but overall it is sufficient to call 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?

The tool takes zero parameters and the schema is trivially complete, so there is nothing for the description to clarify. Baseline 4 applies for a parameterless tool.

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?

States a specific verb (Display) and resource (status dashboard via Prefab UI) and names the content it surfaces: stack health, metrics, active alerts. It is distinguishable from data-returning siblings like check_stack_status, though it does not explicitly contrast itself with the similarly named observability_*_card 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?

Usage is only implied by the phrase 'visual overview' – an agent can infer this is for human-facing rendering rather than programmatic status checks, but there is no explicit when-to-use or when-not-to-use guidance versus check_stack_status or the card siblings.

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

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

With no annotations, the description carries the full burden. It discloses the core behavior (creates OpenTelemetry spans for tracing) but does not mention side effects, permissions, idempotency, or whether it is safe to call repeatedly. Enough to understand the action, but not full 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?

Two sentences, front-loaded with the core action, and no extraneous information. Every sentence earns its place.

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

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

Given an output schema exists and schema descriptions are complete, the description covers the essential purpose and behavior for a tracing tool. Minor gaps remain around safety and expected call patterns, but the definition is largely complete for 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?

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds no parameter-level meaning beyond what the schema provides; baseline 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 states a specific verb ('Record a trace') and resource ('MCP call monitoring and distributed tracing'), and clarifies that it creates OpenTelemetry spans. It does not explicitly name siblings it differs from, but the tracing focus distinguishes it from most observability siblings.

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

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

The description implies usage context (MCP call monitoring and distributed tracing) but does not explicitly state when to use this tool versus alternatives like analyze_mcp_interactions or send_logs_to_loki. No exclusions or prerequisites are given.

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