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vola-trebla

Release Readiness Triage MCP

by vola-trebla

detect_temporal_failure_patterns

Detect temporal patterns in test failures to determine if failures are time artifacts rather than code regressions, guiding re-runs at different times.

Instructions

Analyzes a history of test failures with timestamps to detect chronometric patterns: failures that cluster at the same UTC hour (hourly jobs), same day of month (billing runs), same weekday (scheduled jobs), or around DST transitions. When a pattern is found, the failure is a time artifact — not a code regression. The agent should schedule a re-run at a different time rather than investigating the source code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
failuresYesHistorical failure records with timestamps
Behavior4/5

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

With no annotations provided, the description carries full responsibility. It conveys that the tool is read-only ('Analyzes') and clarifies the interpretive output: detected patterns imply time artifacts, not regressions. This adds useful context beyond a simple verb, though it does not detail the exact return format or any potential limitations.

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?

The description is two sentences, front-loaded with the core function and enriched with concrete examples and actionable guidance. Every sentence contributes value without redundancy.

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?

Given a single parameter and no output schema, the description covers the input semantics, the type of analysis, and the decision outcome. It is nearly complete, though it could explicitly state what the tool returns (e.g., a list of matched patterns) to fully close the loop for an agent.

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?

The schema already provides a description for the 'failures' parameter and its nested 'timestamp' property, achieving 100% schema coverage. The tool description adds little parameter-specific detail beyond stating 'test failures with timestamps', so it does not significantly augment the schema.

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?

The description clearly states the tool's function: 'Analyzes a history of test failures with timestamps to detect chronometric patterns'. It distinguishes itself from siblings by focusing specifically on temporal patterns (hourly, monthly, weekly, DST), which is not covered by other tools like cross_reference_flakiness or correlate_code_changes.

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

Usage Guidelines5/5

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

The description provides explicit guidance: when a pattern is found, treat the failure as a time artifact and schedule a re-run rather than investigating source code. This directly tells the agent when to use this tool and what action to take, effectively distinguishing it from code-analysis siblings.

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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