Inference AIops
OfficialServer Quality Checklist
Latest release: v0.9.0
- Disambiguation2/5
Several tools are near-duplicates differentiated only by engine-specificity: request_metrics vs engine_request_metrics, queue_depth vs engine_queue_depth, and diagnose_latency_spike vs diagnose_engine_latency. model_list and engine_inventory also overlap in listing served models, making tool selection ambiguous.
Naming Consistency3/5Names are consistently snake_case but mix object-first patterns (model_list, deployment_status, engine_health) with action-first verbs (model_deploy, scale_replicas_up, diagnose_latency_spike). Phrasal names like scale_to_zero and undo_apply add further inconsistency, though the naming is still readable and not chaotic.
Tool Count2/539 tools is excessive for the domain and the count is inflated by redundant vLLM-specific/engine-agnostic pairs and three refuse-with-teaching stubs (replica_restart, routing_policy_update, drain_replica). Many tools could be consolidated, so the set feels bloated rather than well-scoped.
Completeness3/5The surface covers model lifecycle, scaling, autoscaling, monitoring, diagnostics, cost, and undo, which is broad for an inference AIops server. However, notable gaps include no in-place deployment config update, no log/event retrieval, no request tracing, and several advertised actions are stubs that refuse to execute.
Average 4.1/5 across 39 of 39 tools scored. Lowest: 3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 24 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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This repository includes a glama.json configuration file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
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 only states it is a READ operation, but lacks details on permissions, destructiveness, rate limits, or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the READ indicator and key metrics. The parameter is listed succinctly. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool returns metrics, the description lacks details like return format, aggregation, time range, or whether metrics are per-request. With no output schema, this is insufficient for an agent to understand the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'target' is described as 'Inference target name from config; omit for the default.' This adds context beyond the schema's type/optionality, clarifying its source and default behavior. Schema coverage is 0%, so this is valuable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[READ]' and specifies the metrics: 'TTFT / TPOT / e2e latency + generation-token totals', making the purpose clear. However, it does not distinguish from sibling tools like 'request_metrics' or 'diagnose_engine_latency'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over alternatives. The description does not mention prerequisites, context, or when to avoid using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It states '[READ]' implying a safe read operation, but does not disclose any behavioral traits such as side effects, required permissions, rate limits, or invocation frequency concerns. The return format is hinted but not fully specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, using two sentences plus an Args section. It front-loads the key purpose and parameter info. However, the structure could be improved by listing the metrics more explicitly, but overall it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and no output schema, the description adequately covers the metrics returned and the parameter. However, it lacks differentiation from similar sibling tools and does not specify the format of the target name or the exact structure of the output data. It is minimally complete but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds clear meaning to the single 'target' parameter: 'Inference target name from config; omit for the default.' This explains the parameter's purpose and default behavior beyond the basic type information in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it reads vLLM latency metrics (TTFT, TPOT, e2e) and generation-token totals, specifying the resource and metric types. It uses a [READ] prefix indicating it is a read operation. However, it does not explicitly differentiate from the sibling tool 'engine_request_metrics' which may overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'engine_request_metrics' or 'diagnose_engine_latency'. It only mentions omitting the target parameter for default behavior, but no context on appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description labels the tool as [READ] which implies non-destructive behavior, but no annotations exist. It does not disclose any behavioral traits such as performance impact, authorization needs, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a single line with a [READ] tag, a one-sentence purpose, and a bullet for the parameter. Every element is essential, and it is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with no output schema, the description covers purpose and parameters adequately. However, it does not describe the return format (e.g., two numbers, dictionary) which would help an agent interpret the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'target' is explained: it accepts an inference target name or omit for default. This adds meaning beyond the schema, which has 0% description coverage. The explanation is clear and sufficient for the tool's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads running vs waiting requests as a backpressure signal for any engine. It distinguishes from queue_depth by specifying 'any engine', but does not explicitly differentiate from sibling tools like engine_health.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 vs alternatives (e.g., queue_depth, engine_health). The description implies it's for monitoring backpressure but lacks explicit usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description marks the tool as '[READ]' hinting at read-only behavior, but with no annotations provided, the description carries full burden. It fails to disclose pagination, error handling, or any side effects. For a list operation, it lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (two lines), with the purpose front-loaded via the '[READ]' marker and a clear list of returned fields. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema, the description adequately states what it returns but omits format, ordering, or filtering details. It is minimally complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining the 'target' parameter: 'Inference target name from config; omit for the default.' This adds real context beyond the schema's type definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists 'All Ray Serve deployments' with specific fields (status, replica count, target). It uses a specific verb ('list' implied by '[READ]') and resource ('deployments'), and the 'All' qualifier distinguishes it from tools that focus on single deployments or replicas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Only basic parameter usage is given ('omit for the default'). No guidance on when to use this tool versus sibling tools like deployment_status or replica_list, nor any prerequisites or context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully communicate behavioral traits. While it states the tool is a read operation, it does not explicitly confirm it is non-destructive or safe, nor does it mention any potential side effects, rate limits, or output characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct and front-loaded with the key action (READ) and purpose, followed by a single parameter explanation. Every sentence is essential, with no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and a single optional parameter. The description lacks details about return values (e.g., a boolean or status code) and does not confirm non-destructiveness, though the simplicity of the tool reduces the need for extensive context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds meaning by explaining the 'target' parameter as the inference target name from config with an option to omit for default. This provides context beyond the bare schema, partially compensating for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a read operation for liveness of the serving engine, specifying supported backends (vLLM, SGLang, TGI) and the tool's function via a health probe. It distinguishes itself from sibling tools like engine_queue_depth or diagnose_engine_latency.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 only explains the target parameter but does not mention conditions that would make this tool preferable over other health-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The '[READ]' prefix indicates the tool is non-destructive, which is a key behavioral trait. However, with no annotations present, the description carries the full burden of disclosure. It does not mention authentication requirements, rate limits, or whether the data is live or cached. For a simple read tool, this is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence for purpose and one line for the parameter. No redundant words, and the most critical information (read nature, per-node output) is front-loaded. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional parameter, no output schema), the description provides sufficient context: it states the tool returns per-node GPU count, utilization percentage, and memory usage. It does not describe the format or structure of the output, but the listed fields give a clear picture. Adding a note about whether the output includes all nodes or a specific node would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (the 'target' parameter has no description in the schema). The description compensates by explaining that 'target' is an optional inference target name, defaulting to the default. This adds meaningful context beyond the schema's type-only definition. A slight improvement would be to mention possible values or where to find the config.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a READ operation providing per-node GPU count, utilization percentage, and memory usage. It is specific about the resource (GPU utilization) and distinguishes itself from many sibling tools that deal with models, queues, or engine health. However, it could be more precise about whether 'utilisation %' refers to per-GPU or per-node average.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'kv_cache_stats', 'queue_depth', or 'engine_inventory'. The description does not mention prerequisites, such as requiring a running inference target, or when to omit the 'target' parameter. The '[READ]' prefix hints at non-destructive use but does not provide clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description labels the tool as [READ], indicating a read-only operation. However, with no annotations, it lacks details on potential side effects, permissions required, or performance impact. The read hint is helpful but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise, two sentences with no redundancy. Front-loaded with [READ] for quick identification. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the tool is simple, the description omits return format and does not differentiate from similar status tools. Given no output schema, more detail on what data is returned would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the 'target' parameter as an inference target name from config, with omission for default. This adds meaning beyond the schema (which has no description), but does not specify default behavior or valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it reads Serve controller health and app/deployment counts, specifying verb and resource. It distinguishes from sibling tools like ray_job_cancel or replica_restart by focusing on status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Does not mention context, prerequisites, or exclusions, leaving the agent to 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.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds value by labeling it as read-only and noting 'best-effort' for permission. However, it omits error behavior, rate limits, or authorization requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using a one-line summary and a clear parameter list. No unnecessary words, every sentence provides value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with only 2 parameters and no output schema, the description covers key aspects but lacks details on return structure, error cases, or usage examples.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description explains model_id's source (model_list) and target's role (default inference), adding practical meaning beyond the schema's type and required flag.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it's a read operation for a single model's config, listing fields like max length, root/parent, and permission. It differentiates from siblings like model_list by focusing on one model's detailed config.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating model_id is from model_list, but lacks explicit guidance on when to use this vs other diagnostics tools or alternatives. No when-not advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The '[READ]' prefix indicates a read-only operation, which is helpful since no annotations are provided. However, it does not disclose other behavioral traits such as whether it requires authentication, whether it's expensive to call, or what happens if no models are served. The description partially compensates for missing annotations but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with only two lines: a clear '[READ]' indicator and a one-line parameter explanation. Every sentence serves a purpose, and it is front-loaded with the key action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks information about the return format (expected schema of output) and does not provide context for when to use this tool over siblings like model_info or engine_inventory. Given the absence of an output schema and the number of sibling tools, the description is incomplete for an agent to make fully informed decisions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the 'target' parameter as 'Inference target name from config; omit for the default', adding meaningful context beyond the schema's type-only definition (string or null). This helps an agent understand how to use the parameter correctly, especially since schema description coverage is 0%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists all served vLLM models and LoRA adapters, using a '[READ]' prefix for clarity. It distinguishes itself from sibling tools like model_info (which likely returns details for a specific model) and model_deploy/undeploy (mutations).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 (e.g., model_info for details or model_is_sleeping for status). The agent is left to infer usage from the tool name and the '[READ]' hint, but no explicit when/when-not advice is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It includes a '[READ]' prefix indicating read-only behavior, but it does not elaborate on permissions, side effects, or other behavioral traits. The description is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a single line for purpose and one line for parameter explanation. It is front-loaded and efficient. Minor room for structure improvement but very effective for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description partially covers return values by listing CPU/GPU capacity, headroom, and pending placement groups. However, it lacks broader context such as assumptions, required resources, or limitations. Adequate but not exhaustive given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0% per the metric, but the description adds meaning beyond the schema by explaining that 'target' is an inference target name from config and that omitting it uses a default. This clarifies usage despite the schema showing only a string/null type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads cluster-wide CPU/GPU capacity, headroom, and pending placement groups. The verb 'read' is explicit, and the resource scope (cluster-wide) is specified. This purpose is distinct from sibling tools like gpu_utilization or ray_dashboard_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 specify prerequisites, when-not-to-use, or scenarios where sibling tools are more appropriate. The only usage hint is about the target parameter being optional.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates it is a read operation via '[READ]', which implies no side effects, but does not detail permissions, error conditions, or the exact response format. With no annotations, some behavioral aspects are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences and a list of arguments. It is front-loaded with the purpose and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description covers the tool's purpose and parameters, it does not describe the output format or potential errors. Since there is no output schema, this leaves the agent guessing about the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains each parameter: application, deployment, and target, adding meaning beyond the bare schema property names. However, it could include more details like acceptable values or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads autoscale bounds (min/max replicas, target ongoing requests). It uses the verb 'read' and specifies the resource, distinguishing it from sibling tools like autoscale_config_update or scaling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives such as autoscale_config_update or scaling tools. The description lacks context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The '[READ]' tag indicates read-only behavior, but with no annotations, the description should also mention that it is non-destructive and safe to call. It does not disclose other behavioral traits like error states, performance impact, or caching, but is minimally transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two-line description with a front-loaded tag, concise purpose statement, and parameter documentation. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose and parameter, but lacks explicit mention of return format (e.g., numbers, structure). For a simple tool with no output schema, it is adequate but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description documents the 'target' parameter as an optional inference target name, adding meaning beyond the schema. The default behavior ('omit for the default') is explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it reads queue depth for an inference target, using the verb 'Running vs waiting requests' and the tag '[READ]'. It distinguishes from siblings like engine_queue_depth by implying it's a generic queue depth signal for the inference target.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like engine_queue_depth, request_metrics, or model_is_sleeping. No explicit context for when it is appropriate or inappropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description indicates a read-only root cause analysis via '[READ][RCA]', but does not disclose default behavior when target is omitted or output format. It carries the burden well but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, front-loads the purpose with brackets, and every sentence serves a purpose with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one optional param and no output schema. The description explains what it does but omits details on return values. Given simplicity, it is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage. The description adds value by explaining the target parameter as 'Inference target name from config; omit for the default', which is not obvious from the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the verb 'explain' and the resource 'under-used GPU' with concrete causes (batching/idle/overprovision). It distinguishes from sibling tools like 'gpu_utilization' (raw metrics) and 'diagnose_latency_spike'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when GPU is under-used and mentions optional target, but lacks explicit guidance on when not to use or alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It states the tool stops a job but does not disclose side effects (e.g., cleanup behavior), success/failure indicators, or whether the operation is synchronous. The [risk=medium] tag is present but insufficient without further detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one line for action and two lines for parameters. The [WRITE][risk=medium] prefix front-loads important metadata. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters, no output schema, and no annotations, the description covers the basics: action and parameter meanings. However, it lacks any mention of return value, error conditions, or post-conditions, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds meaning: job_id is sourced from ray_job_list, and target is an optional inference target name. This provides context beyond the raw schema, but could be improved by specifying expected formats or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Stop a submitted/running Ray job') with specific verb and resource. The [WRITE][risk=medium] prefix further distinguishes it as a mutation tool, and the sibling list includes ray_job_list (a different operation), so no confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description hints at usage by noting that job_id comes from ray_job_list, implying a workflow. However, it does not explicitly state when to use this tool versus alternatives or when not to use it. A clearer usage condition would improve this dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The [READ] tag indicates a read-only operation, and the description lists the states returned. However, without annotations, the description carries full burden; it fails to disclose error behavior, authentication requirements, rate limits, or whether the output is a list or single object. More context on behavior beyond the basic purpose is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two sentences for purpose and three args) and front-loaded with the [READ] tag and core function. Every sentence is informative without redundancy, achieving high efficiency for an agent parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description is adequate for a simple read operation but lacks details on return format, pagination, or error cases. It covers the basic what and how, but not enough for a fully informed agent decision, especially among many sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds essential meaning: 'application' is the serve application name, 'deployment' is the deployment name, and 'target' is an optional inference target defaulting to null. This clarifies the parameter semantics beyond the bare schema, though it could include format or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists replica states (running/starting/draining) for a specific deployment, using a [READ] prefix. It identifies the resource ('replica states') and the action ('list'), and the examples of states provide concrete output expectations. This distinguishes it from sibling tools like deployment_status or scale actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying required parameters (application, deployment) and optional target, but does not explicitly state when to use this tool over alternatives like serve_deployment_list or scale_replicas_up. No 'use when' or 'avoid' guidance is provided, limiting its utility for selection among many siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description bears full responsibility. It discloses that the operation is a write with medium risk and is reversible, but does not detail permissions, side effects, or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (~60 words) and front-loaded with the main action. Every sentence earns its place, and the list format is clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple write tool, the description covers the main action, parameter semantics, and key behavioral traits (live, reversible). However, it omits output description and error handling, which are minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description provides meaningful parameter descriptions for all six parameters (e.g., 'New floor (omit to leave unchanged)'), adding value beyond the schema's type and title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Live-tune autoscale bounds without a redeploy (reversible).' It uses a specific verb and resource, and distinguishes it from siblings like autoscale_config_get and scale_replicas_up/down.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for quick adjustments via 'without a redeploy' and mentions 'reversible,' but lacks explicit when-to-use vs. alternatives like manual scaling tools. No exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It indicates a read operation with '[READ]' and lists return metrics, but does not explicitly guarantee no side effects or mention any behavioral traits beyond reading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences, with the purpose front-loaded in the first sentence and parameter explanation in the second. Every word adds value, meeting the standard of efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one optional parameter, no output schema), the description covers the purpose and parameter adequately. It lists three return metrics but lacks details on the return format, which is acceptable for a read-only tool with minimal complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the parameter 'target' is undefined in the schema. The description explains it as 'Inference target name from config; omit for the default,' adding essential meaning beyond the schema's bare type definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[READ]' and explicitly lists three specific metrics: KV-cache utilisation, prefix-cache hit rate, and preemption count. This clearly identifies the tool's purpose and resource, distinguishing it from sibling monitoring tools like gpu_utilization or request_metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as a read operation for cache stats but fails to provide explicit guidance on when to use this tool versus alternatives like request_metrics or engine_health. No exclusion criteria or when-not-to-use advice is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full transparency burden. The '[WRITE][risk=medium]' tag and 'hot-load' phrase indicate a write operation with medium risk and live loading, but it does not detail failure behavior, permissions, or effects on existing adapters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a header line with risk tag, blank line, then parameter documentation. Every sentence provides information without redundancy, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no output schema, the description covers the operation and parameters adequately. However, it does not mention the outcome (e.g., success/error) or side effects, leaving some gaps for an agent considering invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 defines each parameter: lora_name (adapter name), lora_path (local path or HF repo id), target (inference target, omit for default). This adds meaning beyond the schema's type-only definitions, though format specifics and examples are missing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Hot-load a LoRA adapter onto the running engine.' Using a specific verb ('load') and resource ('LoRA adapter'), it distinguishes from the sibling 'lora_unload', making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context with 'hot-load' and 'onto the running engine', but does not explicitly state when to use this tool versus alternatives like 'lora_unload', nor does it provide conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description declares the tool as a read operation ('[READ]') and explains it correlates signals into a ranked cause and suggests a knob to turn. Since no annotations are provided, the description carries the full burden. It covers the main behavioral aspect (non-destructive analysis) but lacks details on edge cases or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (about 3 sentences) and front-loaded with the core purpose. It includes a brief explanation and argument description without unnecessary fluff. Could be slightly better structured, but effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a diagnostic tool with only one optional parameter and no output schema or annotations, the description provides sufficient context: what it does, when to use which variant, and the argument's meaning. It is complete enough for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema shows one optional parameter 'target' with only type and default. The description adds meaningful context: 'Inference target name from config; omit for the default.' This clarifies the purpose and usage of the parameter beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[READ][RCA] Rank the probable cause of a latency spike for any serving engine.' It clearly specifies the action (rank), the resource (probable cause of a latency spike), and the scope (any serving engine). It also distinguishes itself from the sibling 'diagnose_latency_spike (vLLM)' by noting it is engine-agnostic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly names an alternative: 'the engine-agnostic counterpart to diagnose_latency_spike (vLLM).' This helps the agent choose between these two tools. However, it does not provide explicit when-not conditions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden, and it does well: it discloses the refusal behavior, explains the underlying limitation (no REST endpoint), and states that it will return a teaching error. It does not describe the exact error format or side effects, but since it is a refusal stub, that is less critical. The disclosure is honest and helps an agent avoid expecting a real update.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description begins with a useful [WRITE][risk=medium] tag and a one-line summary, followed by a necessary explanatory note and clear args section. It is a little longer than strictly needed, but every sentence contributes context, especially the note about the REST limitation. The structure is logical and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's unusual nature (a stub that refuses), the description thoroughly explains why it exists and what will happen. It covers all parameters, states the limitation, and provides an alternative. It does not mention what the 'teaching error' message contains or whether any state changes occur, but for this kind of tool, that is acceptable. Overall it is complete enough for an agent to decide not to call it unless explicitly instructed to inform the user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides no descriptions (0% coverage), so the description fully compensates. It explains each of the four parameters: application (Serve application name), deployment (deployment name), policy (with a list of allowed values: prefix_aware / round_robin / session_affinity), and target (with instruction to omit for default). This adds meaningful guidance beyond the schema and is complete for all parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the nominal purpose ('Switch a deployment's request-routing policy') and then immediately qualifies it by explaining this tool actually refuses with a teaching error because no REST endpoint exists. This distinguishes it from sibling deployment tools and makes its actual behavior explicit. The verb+resource is present, though the contrast between the nominal action and real behavior could be confusing, so 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.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says the policy is code-level and cannot be set over the Dashboard REST API, and it recommends changing the source and redeploying. This is a clear 'when not to use' directive and gives an alternative action. It does not explicitly say 'use this tool when you want to inform the user that this cannot be done via API,' but the implication is strong and adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
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 clearly marks the tool as READ, describes the multiplication and degradation behavior, and explains parameter effects. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a summary and includes a structured docstring. Each sentence adds value, though it could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations or output schema, the description sufficiently explains the tool's purpose, calculation, degradation case, and all parameters. An agent can select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description fully explains all three parameters (gpu_hourly_cost, num_gpus, target) with context beyond the schema names, compensating well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it attributes a $/1M-token unit cost from live vLLM throughput, using a specific verb and resource that distinguishes it from sibling tools focused on models and deployments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the calculation and degradation condition but does not explicitly state when to use this tool versus alternatives. More guidance on context or exclusions would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully covers behavioral traits: it is a write operation with high risk, can drop unfinished requests, and supports dry_run preview. It lacks details on authorization needs or rollback, but the core behaviors are well disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the key header and risk note, then lists parameters efficiently. The docstring format is clear but adds some length; still, every sentence adds value. A slightly more compact version would achieve a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no output schema, and no annotations, the description covers the main behavioral aspects and parameter meanings. It could include information about return values or error states for full completeness, but it is sufficient for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain parameters. The docstring provides clear, concise definitions for each parameter (application, deployment, dry_run, target), adding meaning beyond the schema titles. However, the parameter 'target' could be more specific about its default behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[WRITE][risk=high] Force a deployment to re-apply new config.' which clearly specifies the action (force re-apply) and the resource (deployment config). It distinguishes itself from siblings like 'deployment_status' (read) and 'scale_replicas_up' (scaling) by focusing on config re-application.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It states the tool can drop unfinished requests and offers a dry_run preview, providing clear situational guidance. However, it does not explicitly mention when to avoid using this tool or compare it to alternative siblings like 'serve_deployment_list' or 'undo_apply', which could help in decision making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description labels the tool as '[READ]', indicating idempotent and safe behavior. However, with no annotations provided, it could be more explicit about potential errors, permissions, or limitations. It does not contradict any annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a single-line header followed by a bulleted list of parameters. Every sentence adds value, with no redundant or verbose text. The structure makes it easy for an AI to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no output schema), the description covers all parameter meanings and specifies the return values (status and replica counts). It lacks details on output format or error cases, but for invocation and selection, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description carries full burden for explaining parameters. It adds valuable context: application is a name from serve_deployment_list, deployment is within the app, and target is optional from config. This compensates completely for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a read operation ('[READ]') and specifies that it retrieves 'status + current/target replica count' for a single deployment. This directly distinguishes it from sibling tools like replica_restart or deployment_redeploy, which involve mutations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit context for each parameter, explaining that application comes from serve_deployment_list and target is optional from config. While it doesn't explicitly state when to use this tool over alternatives, the read nature and focus on a single deployment imply its use case clearly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description uses tags [READ][RCA] to indicate it is read-only and for root cause analysis. It explains it correlates queue depth, KV-cache pressure, etc., giving insight into its analytical nature. No annotations are provided, but the description adequately covers its behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with a strong summary tag. Every sentence adds value, including the purpose, methodology, and parameter explanation. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the tool's correlation and output (ranked cause list), but lacks details on the output format (e.g., structure, scoring). With no output schema, the agent might need more specifics about what is returned. Otherwise, it covers the input and use case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the single optional parameter 'target' as 'Inference target name from config; omit for the default.' This adds meaning beyond the schema, which has 0% coverage. It tells the agent where the value comes from and the effect of omission.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Rank the probable cause of a TTFT/latency spike + the knob to turn.' It specifies the resource (latency spike) and action (rank causes), and distinguishes from siblings by noting 'call this first on why is inference slow.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'call this first on why is inference slow.' It implies this is the primary diagnostic tool for latency issues. However, it does not explicitly state when not to use it or mention alternative tools like diagnose_engine_latency.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It explicitly declares read-only with '[READ]' and describes per-engine behavior differences. However, missing details on permissions, side effects, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: a front-loaded purpose line, engine-specific details, and clear parameter documentation. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity, no output schema, and no annotations, the description covers purpose, engine-specific behavior, and parameter meaning. Missing return format or error handling, but adequate for the tool's scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one optional parameter 'target'. Schema coverage is 0%, but description explains it's an inference target name from config and that omitting uses default. This adds meaningful context beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it's a READ operation that returns running-model identity and engine server info, and explains engine-specific behaviors (vLLM, SGLang, TGI). This distinguishes it from sibling tools like model_list, which are model-level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for engine-level inventory via engine-specific details, but lacks explicit guidance on when to use this tool versus alternatives (e.g., model_list or engine_health). No when-not-to-use or alternative mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden, disclosing risk=high, irreversibility, and the dry_run preview option, which is thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with clear 'Args' section, though slightly verbose with the risk prefix.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers parameters and key behaviors but omits return values or success/failure indicators, which would be helpful for a destructive action.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description explains each parameter's source and behavior (application from serve_deployment_list, dry_run preview, target from config), adding significant value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Tear down') and resource ('whole Serve application'), clearly distinguishing it from siblings like model_deploy and deployment_redeploy.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some guidance (irreversibility, dry_run to preview) but does not explicitly state when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
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 explicitly marks the operation as [WRITE][risk=high], mentions reversibility, and warns that fewer replicas can strand in-flight requests. This provides good behavioral context, though it could include more on authentication or rollback specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a header line, a warning line, and an Args block. It front-loads key information (risk, reversibility, preview). It could integrate inline parameter docs but is well-structured and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains parameters and provides risk warnings, but lacks information about return values or prerequisites (e.g., required permissions). Given no output schema, this is a gap, but overall it is adequate for a focused write tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description includes an 'Args' section that clearly explains each parameter's meaning (e.g., 'dry_run: If True, preview without scaling'). This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Lower a deployment's replica count on prod'. It specifies it's a WRITE operation with high risk and mentions reversibility. The sibling tools include scale_replicas_up and scale_to_zero, making the differentiation implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises when to use the tool (to lower replica count) and provides a usage hint: 'pass dry_run=True to preview'. However, it does not explicitly state when not to use it or name alternatives like scale_to_zero for setting to zero.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that it stops requests, frees memory, and details the two sleep levels and their wake behavior. It also notes the dev mode requirement. Missing info on active request handling, but covers key effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a header, body, and args section. Every sentence adds value and there is no redundancy. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given 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 covers the purpose, behavior, parameters, and a critical environment condition. Missing return value and error scenarios, but sufficient for an AI agent to understand and use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains all three parameters: level (1 or 2 with effects), dry_run (preview), target (inference target). This adds meaningful context beyond the schema's types and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it suspends the engine via Sleep Mode, stopping request serving. It distinguishes from sibling tools like model_wake (wakes) and model_is_sleeping (check status).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains when to use (free GPU memory between bursts) and the prerequisite (server started with VLLM_SERVER_DEV_MODE=1). It mentions the complementary model_wake, but does not explicitly contrast with alternatives like scale_to_zero.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: it is a read operation (labeled [READ]), returns a defined structure with pagination details (truncated flag), and explains the truncated field behavior. It doesn't cover rate limits or authentication, but for a read-only list tool this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Concise and well-structured: a one-line summary, then the return format, followed by parameter explanations. Every sentence adds value, and the layout is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete for a list tool with 2 parameters and no output schema. It covers the return format, pagination, and parameter defaults. No missing critical information like authentication or scoping given the tool's simple nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully carries the burden. It explains both parameters: target (inference target name, default null) and limit (max rows, default 100), adding meaning beyond the schema's type and default annotations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists submitted Ray jobs with specific fields (id, status, entrypoint, start time). The verb 'list' and resource 'Ray jobs' are explicit, and the READ label further clarifies intent. It distinguishes from sibling tools like ray_job_cancel or serve_deployment_list by specifying the resource and action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use compared to alternatives. The description does provide guidance on pagination ('re-run with a higher limit' when truncated is true), which is helpful but does not cover all usage scenarios or exclude sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the write nature, high risk, and that traffic fails. Dry_run option is mentioned. Could add reversible nature (implied by load/unload pair) and error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with risk and action. Parameter descriptions are clear and compact. Every sentence adds value. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, risk, dry run, parameters. Lacks explanation of return value (no output schema) and reversible nature. Minor missing context about exact failure behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains each parameter in detail: lora_name source, dry_run effect, target optionality. Adds meaning beyond schema types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Hot-unload a LoRA adapter') and the resource, with explicit risk highlighter. It distinguishes from siblings like lora_load by being the inverse operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on safe usage via dry_run=True. However, lacks explicit when-not-to-use or comparison to alternatives like model_undeploy. The risk=high tag implies caution but could be more detailed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosure. It clearly marks the operation as WRITE with medium risk, explains the merge behavior (existing apps preserved), and notes that replica counts come from the app's config. This provides meaningful behavioral context beyond simple action identification.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-organized: a clear action line, a short behavioral context paragraph, and a bulleted argument list. Every sentence contributes value, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema or annotations, but the description covers the key behavioral aspects (merge, preservation, replica handling) and all parameters. It lacks explicit success/failure or verification steps, but for a deploy operation, it provides sufficient guidance to invoke correctly. Minor gaps exist around prerequisites and return behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate fully. It does: each parameter is explained with its role (application name to create/replace, import path format with example, target with default behavior). This exceeds the schema's minimal titles and adds practical meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Deploy') and a specific resource ('Serve application from an import path'), immediately distinguishing it from sibling tools like model_undeploy or deployment_redeploy. It also clarifies the application is created or replaced, making the action unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates when to use this tool (to deploy a Serve app) and provides a follow-up action (set replica counts with serve scale), implying that scaling is not handled here. However, it does not explicitly name alternative tools or state when not to use it, so it misses some exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It explicitly marks as [READ] and details return value meanings and the dev-mode condition, which are key behavioral traits. No mention of side effects or auth, but adequate for a simple status check.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence serves a purpose, no redundancy. Front-loaded with [READ] for quick scanning. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional param, no output schema), the description fully covers return values, special server mode, and parameter usage. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description adds meaning: 'target: Inference target name from config; omit for the default.' This explains purpose and default behavior beyond the schema type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it is a READ operation to check if the vLLM engine is suspended in Sleep Mode, with specific verb 'is_sleeping' and resource 'model'. Distinguishes from siblings like model_sleep and model_wake by focusing on status retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context on when to use: returns sleep status, explains null means unknown, and notes Sleep Mode exists only on dev-mode servers. Lacks explicit comparison to other health-check siblings but sufficiently guides usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses this is a WRITE operation with medium risk, explains it dispatches an inverse tool (audited separately), and covers dry_run preview and single-use tokens. It lacks detail on error handling but is otherwise transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (5 sentences plus parameter bullets), front-loaded with a summary in brackets, and each sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, behavior, and all parameters adequately for a tool with no output schema. Minor omission is error handling or invalid input behavior, but overall it is complete for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully documents all three parameters (undo_id, dry_run, target) with clear explanations, providing essential meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool applies a recorded undo by dispatching its inverse tool, and it distinguishes from the sibling undo_list (which lists undos) by focusing on application.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It mentions dry_run for preview and that a token can only be applied once, but does not explicitly state when not to use or list alternatives beyond the implicit pairing with undo_list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses WRITE operation with medium risk, resumes serving, notes undo limitation, and dev mode condition. Some edge cases (e.g., already awake) are unaddressed, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is front-loaded with practical info, using brackets [WRITE][risk=medium] and structured paragraphs. Could be slightly more concise but avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description does not explain return values. However, it covers core functionality, undo limitations, and conditional availability. Missing details on edge cases like already-wake state, but adequate for a wake action.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must explain params. It does so clearly: 'dry_run: If True, preview without waking' and 'target: Inference target name from config; omit for the default.' Fully compensates for lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Resume serving after Sleep Mode (weights return to GPU)' with a specific verb-resource combo, and distinguishes itself from sibling model_sleep by labeling itself as the inverse.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use (inverse of model_sleep), when not to rely on undo, and suggests an alternative (call model_sleep with specific level). Also mentions conditional existence on servers with VLLM_SERVER_DEV_MODE=1, guiding proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses the write operation, high risk, reversibility (prior count stored), and side effects (cold-start latency, ingress stranding). The dry_run option is also mentioned for safe preview. This provides strong behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: a one-line purpose, a risk note, a sentence of trade-offs, and a bulleted arg list. It is well-structured and front-loaded with key information, no redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a write operation with no output schema, the description covers input behavior and reversibility. However, it does not explain what the tool returns or how to interpret the result (e.g., confirmation, prior count). This minor gap prevents a perfect 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides clear, concise descriptions for all 4 parameters: application, deployment, dry_run, and target. It compensates for the 0% schema coverage by explaining each parameter's role. However, the target parameter description is a bit vague ('omit for the default'), so not a 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Park a deployment at 0 replicas (reversible → prior count).' The verb 'park' and resource 'deployment' are specific, and it distinguishes from siblings like scale_replicas_up/down by focusing on scaling to zero with reversibility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use: 'Stops the cost bleed' and notes trade-offs: cold-start latency and ingress stranding. It suggests using dry_run for preview. However, it does not explicitly exclude scenarios or compare to alternatives like scale_replicas_down, so not a perfect 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses read-only nature, ordering, return structure, and detailed behavior of truncated detection and effectVerified field. No annotations provided, so description carries full burden. Could mention permissions or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with summary, return details, truncated explanation, effectVerified note, and Args. Every sentence adds value; no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers return structure, truncated detection, and effectVerified. Lacks explicit description of other undo entry fields (original tool, inverse tool, note), but overall complete for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds meaning beyond schema: limit default and cap, target marked as unused for CLI uniformity. Schema coverage is 0%, so description compensates well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool lists not-yet-applied undo tokens, using specific verb 'list' and resource 'undo tokens'. Distinguishes from sibling tools like undo_apply.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes when to use: to see available undo tokens before applying with undo_apply. Provides guidance on handling truncated results and interpreting effectVerified field.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Includes [WRITE][risk=medium] tag, explains reversibility, dry_run preview behavior (reads current count and fails early if deployment doesn't exist). No annotations provided, so description carries full burden and does it well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-organized with a concise header, a bullet for dry_run behavior, and an Args list. A few extra words could be trimmed, but overall efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all parameters, behavioral traits (reversible, dry_run, early failure), and usage context. With no output schema or annotations, this description fully enables correct agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but the description's Args section adds full meaning for all five parameters, including that num_replicas must be higher, dry_run purpose, and target optional with default behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action ('raise a deployment's replica count') and the resource, and the reversibility note helps distinguish from sibling tools like scale_replicas_down and scale_to_zero.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on using dry_run=True for preview and explains the behavior. Implicitly differentiates from siblings by focusing on scaling up, but does not explicitly mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description fully discloses behavior: it refuses, explains why, and notes dry_run reports unavailability. The [WRITE][risk=high] tag adds risk context. This is complete transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a bit verbose but well-structured: a clear warning banner, a concise explanation of unavailability, an alternative, and an args list. Every sentence earns its place, though it could be tightened slightly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a deliberate stub/refusal tool, the description covers all necessary context: why it exists, what it does, what the alternative is, and how parameters behave. No output schema is needed since the tool always refuses except dry_run.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explains all five parameters with meaningful purpose: application (name), deployment (name), replica_id (from replica_list), dry_run (preview), and target (config default). This compensates fully for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool drains one replica but immediately clarifies it's NOT available over Ray's REST API and refuses with a teaching error. It distinguishes itself from siblings by naming the alternative (scale down) and explaining the limitation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when-not-to-use and alternative: 'To retire surplus replicas, scale the deployment down — the controller drains them gracefully.' Also mentions that draining is Python-API only, guiding the agent away from this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it refuses, never actually restarts, dry_run reports the same unavailability, and it avoids false positives. It also flags the risk level with '[WRITE][risk=high]' while explaining the action is not actually performed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the key message ('NOT available'), followed by a concise explanation and alternatives. Every sentence adds value, and the parameter list is compact yet informative. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that intentionally refuses all operations, the description is complete: it covers the purpose, the underlying technical limitation, the behavioral result (including dry_run), and all parameters. Despite lacking an output schema, the tool's behavior is fully specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates by explaining each parameter in context: replica_id comes from replica_list, dry_run reports availability without attempting, and target can be omitted for the default. This goes beyond the schema's bare property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource pair ('Restart one Serve replica') and immediately clarifies that it is NOT available over REST, explicitly stating the tool refuses with a teaching error. This clearly distinguishes it from sibling tools like drain_replica and scale_replicas_up/down.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states that the tool is unavailable over REST, explains why (controller respawns replicas), and gives explicit alternatives: 'scale the deployment down then up, or redeploy.' This provides clear when-not-to-use guidance and alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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