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vllm-ops-mcp

vllm-ops-mcp

by jaimenbell
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

check_health

Check vLLM server health via API liveness probe, with optional deep test firing a real chat completion to confirm text generation. Returns status, latency, and error details.

Instructions

Two-stage health probe for the local vLLM server, mirroring check-vllm.cmd. deep=False (default): GET /v1/models liveness check only. deep=True: additionally fires one minimal real /v1/chat/completions call to confirm the server actually generates text, not just reports a model loaded (rate-limited -- this consumes real GPU inference cycles on shared infra). Returns {ok, status: up|degraded|down, tier, model_id, latency_ms, completion_s, error}.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "check_health"
  }
}

Input Schema

NameRequiredDescriptionDefault
deepNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so excellently. It discloses the two-stage behavior, the exact HTTP calls, the side effect of GPU consumption, and the complete return envelope. This is unusually transparent for a health check.

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

Conciseness5/5

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

Every sentence earns its place. The description efficiently covers purpose, parameter semantics, behavioral caveats, and return value without redundancy. It is front-loaded with the main verb and resource, then details.

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

Completeness5/5

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

Given the tool's simplicity (one parameter, output schema present), the description is complete. It explains the two modes, the side effects, and the return structure. No important aspect is left unexplained.

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

Parameters5/5

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

The schema only provides a bare 'deep' boolean with no description. The description fully explains the parameter's meaning, default, and the behavioral difference between true and false, adding substantial meaning beyond the schema.

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

Purpose5/5

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

The description opens with a specific verb+resource: 'Two-stage health probe for the local vLLM server.' It clearly distinguishes this from siblings like list_models or test_completion by framing it as a health check, not a utility for listing or textual generation.

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

Usage Guidelines4/5

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

The description explains when to use deep=False (liveness check) versus deep=True (verifying actual text generation) and warns that deep=True is rate-limited and consumes GPU cycles. However, it does not explicitly name sibling alternatives or state when not to use this tool in favor of another.

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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