lint_jmx_snippetValidate a JMeter test plan XML string or snippet against best practices and performance anti-patterns (e.g., active GUI listeners, legacy BeanShell, uncompiled JSR223, missing timeouts, zero ramp-up, Thread.sleep in scripts).
| Name | Required | Description | Default |
|---|---|---|---|
| jmxContent | Yes | JMX XML string or test plan snippet to analyze. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavior. It mentions validation but does not state whether it modifies anything, returns a report, or requires specific permissions. It also does not describe potential side effects or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently lists the tool's purpose and key checks. It is well-structured and free of unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain what the tool returns, such as a list of violations or a pass/fail status. Given that no output schema is specified, this omission is a significant gap in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description already covers the parameter adequately, and the tool description adds minimal extra semantics beyond clarifying it is a JMeter test plan. Since schema coverage is 100%, the baseline is 3 and the description does not enhance it significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates JMeter test plan XML against best practices and anti-patterns, listing specific examples. It is distinct from sibling tools that focus on calculation, retrieval, or tuning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when you have a JMX string to check for issues, but it does not explicitly state when to prefer this over sibling tools or when not to use it. The examples provide context but no direct guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.