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Load a CI report

load-report

Import a CI Playwright JSON or blob report so failure diagnosis, fix suggestions, and flaky-test analysis work on that run as if it were local.

Instructions

Load a Playwright run that happened elsewhere (a CI job's JSON report, or a folder of blob reports from shards) so get-failure, suggest-fix and diagnose-flaky work on it like a local run. CI paths are mapped onto this checkout and traces are found in the unpacked test-results artifact; the run is recorded in the history with its commit.

Input Schema

NameRequiredDescriptionDefault
branchNo
commitNoCommit the run tested (default: read from the report metadata)
recordNoAdd the run to the local history for analyze-history (default true)
reportYesPlaywright JSON report inside the project (e.g. ci-artifacts/results.json), or a folder of blob reports (*.zip) from sharded jobs
timeoutMsNoLimit for merging blob reports (default 120000)
traceDirsNoFolders inside the project where the job's test-results artifact was unpacked (default: the report's folder and its parent)
projectRootNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.1

TDQS

A4.4/5.0
Behavior4/5

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

The annotations already declare that this is a non-read-only, non-destructive, non-idempotent operation. The description adds important behavioral context beyond that: CI paths are mapped onto the checkout, traces are found in the unpacked test-results artifact, and the run is recorded in local history with its commit.

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 compact and front-loaded, moving immediately from the load operation to the supporting behavior. Every clause adds useful information with no repetition or filler.

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 seven-parameter tool with no output schema, the description supplies the key mental model: importing an external run, mapping paths, locating traces, and recording history. It leaves minor gaps around branch/projectRoot and record=false behavior, but the overall context is sufficient.

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 coverage is 71%, so most parameters are already documented. The description adds value by clarifying the report format (JSON file or blob folder), trace directory discovery, and that the commit is recorded with the run, though it does not cover branch or projectRoot.

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 states a specific verb and resource: loading a Playwright run that happened elsewhere, from either a CI JSON report or a folder of blob reports. It also names the downstream tools enabled by it (get-failure, suggest-fix, diagnose-flaky), making its role distinct from run-test.

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?

It clearly explains when to use this tool: when you have a CI job's JSON report or sharded blob reports and want them treated like a local run. It does not explicitly exclude alternatives or say when not to use it, but the context is sufficiently clear.

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