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MCP Server By TestMu AI

Analyze HyperExecute Job

analyzeHyperExecuteJob
Read-only

Perform a full analysis of a HyperExecute job: fetches job info, sessions, traverses all tasks → scenario stages → console logs, and returns a structured report with test results, per-scenario logs, and pass/fail statistics. Use this for questions like "which tests failed and why" or "show me the console logs for job X".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesThe HyperExecute job ID to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the bar is lower. The description still adds useful behavioral context: it discloses that the tool makes a deep recursive traversal of all tasks and stages and returns an aggregated structured report rather than a single resource, implying heavier cost/latency than the granular siblings.

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

Conciseness5/5

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

Two sentences, no waste. The core capability is front-loaded in the first sentence and the second sentence supplies the routing examples, so the reader gets purpose before use cases.

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

Completeness5/5

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

With no output schema, the description compensates by naming what the report contains (test results, per-scenario logs, pass/fail statistics). Combined with annotations covering safety and a fully documented single required parameter, an agent has everything needed to call this correctly.

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?

There is a single parameter with 100% schema description coverage, so the schema already documents jobId fully. The description adds only marginal meaning (that the id is the job to analyze), which is the expected baseline when the schema does the heavy lifting.

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

Purpose5/5

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

States a specific verb (analyze/fetch/traverse) and resource (HyperExecute job) and enumerates exactly what the deep traversal covers: job info, sessions, tasks → scenario stages → console logs, plus the returned report contents. This scope description makes it distinguishable from the granular per-resource siblings like getHyperExecuteJobInfo or getHyperExecuteStageLogs.

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

Usage Guidelines4/5

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

Explicitly gives use cases with two concrete example questions ('which tests failed and why', 'show me the console logs for job X'), which tells the agent when to pick this aggregate tool. It stops short of naming alternatives such as answerHyperExecuteQuery or the individual getter tools, so there is no when-not guidance.

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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