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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. 'Fetch' clearly signals a read-only retrieval operation with no side effects, and the examples convey what kind of content will be returned. It does not detail return formatting or verification criteria, but for a simple fetch tool this is adequate.
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, front-loaded sentence that states the action and resource first, then adds illustrative examples. Every part contributes useful context, and there is no redundant or filler wording.
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?
For a simple tool with one optional parameter and no output schema, the description covers the purpose, scope, and relevant examples. It could explicitly state the return format or that recipes are usable code snippets, but the overall context is sufficient for an agent to invoke this correctly.
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 documents the single optional 'query' parameter with 100% coverage, including examples and the behavior when omitted. The description restates some example topics but adds no new parameter semantics beyond what the schema provides, so the baseline of 3 applies.
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 states a specific verb ('Fetch') and resource ('production-ready, performant Groovy scripts for JMeter JSR223 samplers, preprocessors, and postprocessors'), with concrete examples like JWT parsing and HMAC signing. It clearly distinguishes this tool from siblings such as search_jmeter_docs, lint_jmx_snippet, and lookup_jmeter_property.
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 provides clear context for when to use the tool: when a JMeter JSR223 recipe is needed for common scripting tasks. It does not explicitly name alternatives or say when not to use it, but the intended use case is unambiguous enough for an agent to select it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.