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qase-tms

Qase MCP Server

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by qase-tms

qase_regression_run

Launch a regression test run from a suite, test plan, or explicit case IDs in one step. Skips pre-querying cases by resolving them automatically, so you can start a cycle without assembling the run manually.

Instructions

Build and start a test run from a suite, a test plan, or an explicit list of case IDs, in one step. Use it to launch a regression cycle without first querying for cases and then creating a run around them — give it the source and it resolves the cases itself. For a run you assemble by hand, use qase_run_upsert and pass the case IDs. For a pipeline that has already finished and just needs its results filed, use qase_ci_report instead: this tool opens a run, it does not close one. Cost: two API calls behind one tool call — resolving the source, then creating the run — roughly 1s, growing with the number of cases the source resolves to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesProject code (2-10 uppercase letters, numbers, or underscores)
titleYesRun title
plan_idNoCreate run from an existing test plan
suite_idsNoInclude cases from these suites
descriptionNo
milestone_idNo
include_casesNoExplicit case IDs to include
environment_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
runNoFull run entity
run_idYesCreated run ID
cases_addedYesNumber of cases added to the run

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv2.0.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish that the operation is not read-only, not idempotent, and not destructive. The description adds useful context beyond annotations: it resolves the source into cases, makes two API calls, and opens a run without closing one, including a rough latency estimate.

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?

The description is longer than average but every sentence earns its place: purpose, alternative routing, and behavioral/cost notes. The core purpose is front-loaded, and there is no filler or repetition.

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 tool with 8 parameters and an output schema, the description covers the main invocation decision, the source-resolution behavior, and sibling alternatives. It is slightly incomplete only in not explaining the optional milestone/environment/description parameters or whether source selectors are mutually exclusive, but the schema and output schema mitigate the gap.

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 coverage is 63%, so the description needs to add moderate value. It clarifies the semantics of the three source selectors (plan_id, suite_ids, include_cases) by explaining the tool resolves cases itself, but optional parameters such as milestone_id, environment_id, and description remain undocumented in both schema and description.

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 starts with a specific verb phrase, 'Build and start a test run from a suite, a test plan, or an explicit list of case IDs,' naming the resource and supported sources. It also explicitly differentiates the tool from qase_run_upsert and qase_ci_report, so an agent can select it without opening sibling schemas.

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?

It gives clear when-to-use guidance: launch a regression cycle from a source without first querying cases. It names alternatives and the conditions that select them — qase_run_upsert for hand-assembled runs, qase_ci_report for already-finished pipelines — and clarifies that this tool opens but does not close a run.

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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