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write_test_report

Generate machine-readable JUnit and Allure test reports for the current session, enabling qa-mcp to integrate into existing CI pipelines.

Instructions

Write machine-readable test reports for the scenarios run THIS MCP-server session so qa-mcp slots into an existing CI pipeline. formats (default both): "junit" -> <out_dir>/junit.xml (CI test-result panels); "allure" -> <out_dir>/allure-results/*.json (renderable by the Allure CLI, with per-step status/timing and any screenshot attachments). Uses the same session log as get_test_results; pass clear=true to reset it after writing. Returns the output paths + scenario count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clearNo
formatsNo
out_dirYes
suite_nameNoqa-mcp

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden, and it does well: it discloses that files are written to specific paths, the exact output locations for each format, that it shares the same session log as get_test_results, that clear=true resets that log after writing, and the return payload. It leaves overwrite/replace semantics for existing files unstated, keeping it from a 5.

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?

Front-loaded with the core action and CI rationale, then parameters, then return value. It is dense but every clause adds information; no filler sentences. Slightly packed but appropriately sized for a configurable file-writing tool.

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?

Output schema exists so return values are covered, yet the description still summarizes them usefully. With no annotations, the description supplies the needed behavioral context (path formats, session-log sharing, clear behavior), though overwrite semantics for pre-existing output files remain unaddressed.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it explains formats thoroughly (values junit/allure with their output paths and defaults), clear, and out_dir via the <out_dir> placeholders. suite_name is never mentioned, leaving one of four parameters undocumented.

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 (write) plus resource (test reports) and scope (scenarios run THIS MCP-server session), and differentiates itself from the read-side sibling get_test_results by naming it. An agent can identify the tool's role without opening the schema.

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?

Explains the purpose context (slotting into an existing CI pipeline) and links to the session log shared with get_test_results, plus when to pass clear=true. It does not state explicit when-not conditions or other alternatives, so it stops short of full routing guidance.

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