JMeter

Calculate Workload Model & Little's Law Sizing

calculate_workload_model

Compute required thread concurrency, pacing delays, ramp-up schedules, and JVM heap recommendations based on target RPS/TPS and SLA response times using Little's Law.

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": {
      "targetRps": "<targetRps>",
      "avgResponseTimeMs": "<avgResponseTimeMs>"
    },
    "name": "calculate_workload_model"
  }
}

Input Schema

Name	Required	Description
`targetRps`	Yes	Target throughput in requests / transactions per second (RPS/TPS).
`thinkTimeMs`	No	Think time / user pause between requests in milliseconds (default: 0).
`safetyFactor`	No	Headroom safety buffer multiplier (default: 1.25 = 25% buffer).
`avgResponseTimeMs`	Yes	Expected average response time in milliseconds.
`testDurationMinutes`	No	Steady-state test duration in minutes (default: 10).

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 21, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description must carry behavioral disclosure. It does communicate that the tool performs a calculation and names the computed outputs, which implies a pure analysis operation with no destructive side effects. Still, it does not state the return format, underlying assumptions, or whether results are estimates.

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, tightly packed sentence with no filler. The primary action and result are front-loaded, and every phrase conveys useful scope information.

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 covers the core purpose and parameter relationships, and there is no output schema to rely on. However, it omits the shape of the returned workload model, the units of some outputs, and explicit guidance on when this tool is preferable to sibling planning tools, leaving moderate ambiguity for an agent.

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?

The input schema already documents all five parameters with 100% coverage, so the baseline is 3. The description adds modest context by linking targetRps and avgResponseTimeMs to Little's Law, but it does not meaningfully elaborate on thinkTimeMs, safetyFactor, or testDurationMinutes 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 uses a specific verb ('Compute') and a concrete resource set: thread concurrency, pacing delays, ramp-up schedules, and JVM heap recommendations. It clearly distinguishes this from the JMeter/documentation-focused sibling tools by stating the exact calculation scope.

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 use case: sizing a load model when target RPS/TPS and SLA response times are known. However, it does not explicitly state when not to use it or compare against adjacent tools like plan_distributed_testing, which may also be relevant for load-test planning.

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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