get_jsr223_recipeFetch production-ready, performant Groovy scripts for JMeter JSR223 samplers, preprocessors, and postprocessors (e.g., JWT parsing & expiration, HMAC-SHA256 signing, dynamic header injection, nested JSON array extraction, custom CSV failure logging).
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Filter by keyword or topic (e.g. "jwt", "hmac", "header", "json", "csv", "logging"). If omitted, returns all recipes. |
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 behavioral disclosure. It only restates the tool's purpose and does not mention read-only behavior, return format, limitation, version compatibility, or any other side-effect context.
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 one well-structured sentence followed by a parenthetical list of concrete examples. It is front-loaded and contains no filler or redundancy.
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?
This is a simple tool with one optional parameter and no output schema, so the description does most of the work. It covers the main scope well, but a short note about what a returned recipe looks like would make it fully complete.
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 input schema already provides 100% coverage of the query parameter with examples. The description adds some contextual color by mentioning topics like JWT, HMAC, and JSON extraction, but it does not meaningfully expand parameter semantics beyond the schema description.
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 opens with a specific verb and resource: 'Fetch production-ready, performant Groovy scripts for JMeter JSR223 samplers, preprocessors, and postprocessors.' The example use cases clearly distinguish it from sibling tools like search_jmeter_docs or lint_jmx_snippet.
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 gives clear context by naming the tool's scope and the kinds of tasks it supports. It does not explicitly state when not to use it or mention alternatives, so it misses the fullest possible guidance.
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.