JMeter

Linux Kernel & OS Tuning for Load Injectors

tune_linux_os

Generate production sysctl.conf, limits.conf, systemd overrides, and Docker/K8s configs tuned for high-concurrency JMeter load testing (fixing ulimit nofile, BindException port exhaustion, somaxconn backlog, and JVM swappiness).

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": "tune_linux_os"
  }
}

Input Schema

Name	Required	Description
`role`	No	Machine role: "injector" (JMeter client) or "target_sut" (default: "injector").
`ramGb`	No	Host machine RAM in GB for TCP buffer sizing (default: 16).
`concurrency`	No	Target concurrent connections/threads (default: 10000).
`trafficType`	No	Traffic profile (default: "http_churn").
`targetDistro`	No	Linux distro or container target (default: "ubuntu_debian").

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 22, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the entire burden. It clearly states the artifacts that will be generated and the issues addressed, but it does not disclose whether the tool writes files to the system or simply returns configuration text, nor does it mention permission requirements or output format. This leaves some ambiguity about side effects.

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

Conciseness5/5

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

The description is a single, front-loaded sentence that names the action and resources immediately, then adds a compact parenthetical of concrete problems solved. There is no filler or repetition; every phrase contributes to understanding the tool's purpose.

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

Completeness3/5

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

The description lists the output artifacts and the target scenario, but since there is no output schema, it does not clarify whether the tool returns file contents, a structured bundle, or applies changes directly. Given five parameters and no annotations, this output-format gap is notable but the core functionality is still inferable from the description.

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 description coverage is 100% and all parameter meanings are already documented in the schema with defaults and enum options. The description adds no parameter-specific guidance beyond the high-level outcome, which is acceptable but does not enhance understanding of how parameters influence the generated configs.

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 uses the specific verb 'Generate' and names the precise resources (sysctl.conf, limits.conf, systemd overrides, Docker/K8s configs) plus the tuning goal (high-concurrency JMeter load testing) and concrete problems it solves (ulimit, BindException, somaxconn, swappiness). This clearly separates it from all sibling tools, none of which focus on OS/kernel configuration 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 implicitly communicates when to use it: when tuning a host for high-concurrency JMeter load testing with specific pain points like ulimit/no-file limits and port exhaustion. It provides clear context but does not explicitly state exclusions or mention alternative tools, though no sibling tool appears to be a direct alternative.

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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