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Smoke-test a running server

smoke_test

Measure response latency and generation speed of a running llama-server by sending batch chat prompts through its OpenAI-compatible API.

Instructions

Send a batch of chat prompts to a running llama-server over its OpenAI-compatible API and report each response with latency and generation speed (tokens/second as measured by llama.cpp).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "port": "<port>"
    },
    "name": "smoke_test"
  }
}

Input Schema

Name	Required	Description
`port`	Yes	Port of a running llama-server.
`prompts`	No	Prompts to send. Defaults to three short sanity prompts.
`maxTokens`	No	max_tokens per completion.
`timeoutMs`	No	Per-request timeout in milliseconds.

Output Schema

Name	Required	Description	Default
`port`	Yes
`prompts`	Yes
`results`	Yes
`meanLatencyMs`	Yes
`meanTokPerSec`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 9, 2026

TDQS

A3.8/5.0

Behavior3/5

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 sends chat prompts and reports latency/speed, but does not clarify side effects (e.g., whether it is read-only or modifies server state), auth requirements, or rate limits. The mention of 'as measured by llama.cpp' adds some context.

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?

The description is a single, well-formed sentence that front-loads the action and resource. Every word contributes to meaning, resulting in zero waste.

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

Completeness4/5

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

Given the presence of an output schema, the description need not detail return values. It adequately covers the tool's purpose, inputs (implied by schema), and reported outputs (latency, speed). Prerequisites (running server) are obvious from context. Nearly complete for a test tool.

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

Parameters3/5

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

Schema coverage is 100%, so each parameter already has a description. The tool description adds limited extra semantic value beyond the schema, only mentioning 'batched chat prompts' and output metrics. Baseline 3 is appropriate.

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 clearly states the tool sends batch chat prompts to a running llama-server via OpenAI-compatible API and reports each response with latency and tokens/second. It uses a specific verb ('smoke-test') and resource ('running server'), distinguishing it from sibling tools like start_server or stop_server.

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

Usage Guidelines3/5

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

The description implies the tool is for testing a running server but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives or exclusions are mentioned, making it minimally adequate.

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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