lookup_error_playbookGet immediate root causes, OS/JVM config fixes, and remediation steps for common JMeter exceptions (e.g. "BindException", "SocketTimeoutException", "OutOfMemoryError", "NoHttpResponseException", "SSLHandshakeException", "401/403 after recording").
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Error message, exception name, or status (e.g. "bindexception", "heap", "timeout", "401"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It communicates that this is a lookup-oriented, informational tool returning remediation content, and it scopes the result set to 'common JMeter exceptions.' However, it does not state fallback behavior when the query has no matching exception, how matches are determined, or explicitly confirm that the operation has no side effects.
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, focused sentence that front-loads the main deliverable (root causes, config fixes, remediation steps) and uses parentheses for concrete examples. It earns its place, though it is sentence-long and the title overlaps slightly with the description's focus.
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 that this is a one-parameter lookup tool with no output schema and no annotations, the description sufficiently tells the agent what content to expect and what inputs are relevant. It could be more complete by stating behavior when no exception is matched, but the simplicity of the tool does not demand much more detail.
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 already describes the single query parameter with useful examples (bindexception, heap, timeout, 401), so schema coverage is complete. The description adds no additional parameter syntax or format details beyond the schema, but no gap exists, so baseline scoring 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 uses a specific action and resource: it says to 'Get immediate root causes, OS/JVM config fixes, and remediation steps' for JMeter exceptions. The examples clarify the exact exception types and the 401/403 after recording case, clearly distinguishing this diagnostic playbook from documentation or property lookup tools.
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 usage context: users should invoke this tool when they need root causes and fixes for JMeter exceptions and status errors. It does not explicitly name excluded alternatives like search_jmeter_docs or lookup_jmeter_property, so it stops short of full exclusion guidance, but the implied use case is clear.
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.