JMeter

Convert cURL or HAR to JMeter JMX Test Plan

convert_curl_or_har_to_jmx

Convert one or more cURL commands or HAR (HTTP Archive 1.2) JSON traces into a valid, production-ready Apache JMeter .jmx test plan XML with HTTP Request Defaults, Header Managers, Cookie Managers, timeouts, and assertions. Supports GET, POST, PUT, DELETE, PATCH, and RFC 9838 QUERY methods.

Input Schema

TableJSON Schema

Name	Required	Description
`input`	Yes	cURL command string (single, multiline, or batch) or HAR 1.2 JSON text (max 1MB).
`threads`	No	Thread concurrency / virtual users (default: 1).
`loopCount`	No	Loop count (-1 for infinite, default: 1).
`testPlanName`	No	Name of the JMeter Test Plan (default: "cURL Converted Plan").
`rampUpSeconds`	No	Ramp-up time in seconds (default: 1).
`durationSeconds`	No	Test duration in seconds (0 = disabled, default: 0).
`parameterizeAuth`	No	Extract Bearer token into ${AUTH_TOKEN} variable (default: true).
`parameterizeHost`	No	Extract common host into HTTP Request Defaults and ${BASE_URL} (default: true).
`includeAssertions`	No	Add HTTP 200/201/204 Response Code assertions (default: true).
`filterStaticAssets`	No	Filter out images/css/fonts when parsing HAR (default: true).
`includeCookieManager`	No	Include HTTP Cookie Manager (default: true).

TDQS

A4.2/5.0

Behavior4/5

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

There are no annotations, so the description carries the behavioral burden. It discloses the generated artifacts, components such as defaults and managers, timeouts, assertions, and supported methods. It does not mention potential error behavior or whether the output is returned inline or written to a file, but it gives a solid picture of what the tool does.

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?

Two sentences deliver the input, output, generated components, and supported methods without filler. The action is front-loaded and every phrase adds relevant information.

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?

For a conversion tool with 11 parameters and no output schema, the description covers the input and output shape well. It could mention how the result is returned and what happens on invalid input, but the core contract an agent needs is clear.

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 the parameters are already fully documented. The description adds broad context about input format and HTTP method support, but does not explain individual parameters beyond what the schema already states, so the baseline of 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 names a specific action (convert), a concrete input (cURL/HAR 1.2), and a concrete output (JMeter .jmx test plan XML). It differentiates this tool from the other JMeter siblings that handle snippets, properties, planning, and linting.

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 clearly establishes when this tool is appropriate: when the agent needs to turn raw cURL commands or HAR traces into a JMeter plan. It lacks an explicit exclusions or mention of alternatives, but the boundary is easy to infer.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool addresses a clearly distinct need: conversion, validation, workload math, distributed planning, OS tuning, docs search/page retrieval, error lookup, property lookup, and script recipes. Even the doc-related tools are complementary: search returns links, while get_jmeter_page fetches full content.

Naming Consistency5/5

All tool names follow a consistent lowercase snake_case verb_noun pattern: calculate_, convert_, get_, lint_, lookup_, plan_, search_, tune_. The verbs clearly signal the action and the nouns clearly signal the target, making the set predictable.

Tool Count5/5

Ten tools is well-scoped for a JMeter-specific MCP server. Each tool targets a meaningful area: test plan generation, validation, workload modeling, distributed execution, OS tuning, documentation, and troubleshooting. None feel redundant or superfluous.

Completeness4/5

The surface covers the main JMeter workflow: converting HTTP input to JMX, linting it, fetching scripting recipes, planning distributed runs, tuning the OS, and researching docs/errors. A minor gap is the lack of a general raw JMX builder or meaningful coverage for non-HTTP protocols, but most practical JMeter use cases are addressed.

Resources

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