Fetch vLLM TTFT, TPOT, and end-to-end latency plus generation-token totals for an inference target to monitor GPU serving performance and identify bottlenecks.
[READ] vLLM TTFT / TPOT / e2e latency + generation-token totals.
Args: target: Inference target name from config; omit for the default.
| Name | Required | Description | Default |
|---|---|---|---|
| target | No |
Changes observed during successful MCP inspections.
v0.6.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already declares the safety profile, and the '[READ]' prefix merely restates it. The description adds no behavioral context such as the time window the metrics cover, aggregation granularity, or whether data is live versus historical, so it clears the lower annotation-assisted bar but adds little.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short lines with the metric list front-loaded and no filler. The 'Args:' block is slightly over-formatted for a single parameter but still earns its place by documenting the target.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a metrics tool with no output schema and only a readOnlyHint annotation, the definition is minimally sufficient: it says what metrics are returned and how target selects the source. It omits the observation window, units, and refresh behavior, which matter for interpreting latency metrics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the schema only carries a title and null default, so the description must compensate. It does so by explaining that 'target' is an inference target name drawn from config and that omitting it selects the default, which is meaningful semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the concrete outputs (TTFT / TPOT / e2e latency plus generation-token totals) and marks the tool as a read operation, so an agent knows exactly what data comes back. It does not, however, distinguish itself from the sibling engine_request_metrics, which sounds like an overlapping metrics endpoint, so the purpose is clear but not differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The only guidance is about the target argument (omit for default), which is parameter usage rather than when-to-use guidance. There is no statement of when to reach for this tool versus engine_request_metrics, queue_depth, or the diagnose_* siblings, leaving selection entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.