lookup_jmeter_propertySearch or lookup curated JMeter properties (e.g. "httpclient4.idletimeout", "jmeter.save.saveservice.*", "remote_hosts", "summariser"). Returns category, defaults, and recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Property name or keyword to search (e.g. "ssl", "timeout", "jtl", "influxdb"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It states that it returns category, defaults, and recommendations, which is useful. However, it does not disclose whether the lookup is read-only (likely safe), whether it requires network access, or any limitations (e.g., only curated properties, not exhaustive). The description adds some value but could be more transparent about the scope of the curated set.
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 concise, two sentences, and front-loaded with the purpose. It includes examples and states the return content. No wasted words.
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?
Given the tool's simplicity (one parameter, no output schema), the description is fairly complete. It explains what it does, what it returns, and provides examples. It could mention that the lookup is for curated properties only, but that is implied by 'curated'. The lack of output schema means the description should hint at return structure, which it does (category, defaults, recommendations). Overall, adequate for the complexity.
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 coverage is 100%, so the parameter 'query' is well-documented in the schema. The description adds examples of property names and keywords, which reinforces the schema. However, it does not add significant new meaning beyond the schema. Baseline 3 is appropriate.
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's purpose: to search or lookup curated JMeter properties, with examples of property names. It distinguishes itself from siblings like search_jmeter_docs (which likely searches documentation) and get_jmeter_page (which likely retrieves a specific page). However, it could be more explicit about the distinction from search_jmeter_docs.
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 usage: when you need to find JMeter properties and their recommendations. It does not explicitly state when not to use it or mention alternatives. Given the sibling tools, it could have noted that for general JMeter documentation, use search_jmeter_docs, but it doesn't. The context is clear but lacks explicit exclusions.
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.