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Lookup Error & Exception Diagnostic Playbook

lookup_error_playbook

Get 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"). When keywords miss, unmatched text is sent to classifier.dev for zero-shot semantic matching (public keyless calls are not stored).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesError message, exception name, or status (e.g. "bindexception", "heap", "timeout", "401").
classifierApiKeyNoYour own classifier.dev API key for the semantic fallback (anonymous quota is shared); never logged.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / classifierApiKey
      Added value: +{
+  "description": "Your own classifier.dev API key for the semantic fallback (anonymous quota is shared); never logged.",
+  "type": "string"
+}
  2. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does well: it discloses the keyword-miss fallback to classifier.dev semantic matching and a privacy trait ('public keyless calls are not stored'). It stops short of stating read-only nature, quota/rate behavior, or response shape, but the disclosed fallback mechanics are genuinely non-obvious value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two front-loaded sentences that lead with the benefit and then the fallback behavior; nothing is wasted. The example enumeration is slightly long but earns its place by showing accepted input shapes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter lookup with no output schema or annotations, the description covers what it returns (causes, fixes, remediation), the input domain, and the fallback/privacy behavior. Only the precise result format and any quota limits remain unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both query and classifierApiKey. The description reinforces the fallback semantics of classifierApiKey indirectly via the classifier.dev sentence, but adds no syntax or format detail beyond the schema baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Get) and concrete outputs (root causes, OS/JVM config fixes, remediation steps) scoped to JMeter exceptions, with representative examples. It is clear what the tool does, but it never distinguishes itself from the sibling triage_errors, so an agent has no stated basis for choosing between the two.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the JMeter-exception scope and the enumerated examples, and the fallback path is described, but the description never says when to prefer this over triage_errors or lookup_component/lookup_function. No explicit when-not conditions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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