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playcal

playcal

by RizgarOzan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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playcal_tests

Record test results and analyze history to spot flaky tests, fail CI only on real failures, and run only test assemblies affected by a change.

Instructions

test result history: spot flaky tests, fail CI only on real failures, run only affected test assemblies

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "action": "<action>"
    },
    "name": "playcal_tests"
  }
}

Input Schema

NameRequiredDescriptionDefault
dirNowhere the history lives (default .playcal/tests).playcal/tests
baseNoaffected: git ref to diff against (default HEAD: uncommitted work)HEAD
filesNoNUnit3 (Unity -testResults) or JUnit XML files; for affected, changed paths
actionYesrecord FILE...: add results to the history. check FILE...: fail only on non-flaky failures, then record. flaky: list tests that passed and failed on one commit. affected [PATH...]: test assemblies a change can break (default: git diff --base)
commitNocommit the run belongs to (default: git rev-parse HEAD)
projectNoaffected: Unity project folder (default .).
no_recordNocheck without adding this run to the history

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden, and it says nothing about side effects, authorization, or exit semantics. Critically, it never discloses that 'check' writes to the history by default (unless no_record is set) or that results are persisted — a mutation trait buried only in the schema's action text.

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

Conciseness3/5

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

There is no wasted wording, but the content is a three-clause fragment with no sentence structure and is arguably undersized for a tool with four distinct actions and no annotations. It is front-loaded with the resource, which helps.

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

Completeness3/5

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

With 100% schema coverage the parameters are covered, but there is no output schema and no annotations, so the description should explain the mutation behavior of record/check, what 'flaky' output looks like, and what CI failure means. Those gaps matter more here than the compact size suggests.

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 description coverage is 100%, so every parameter including the action enum is already documented in structured form; the description adds no syntax, format, or default details beyond what the schema states. Baseline 3 applies.

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

Purpose4/5

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

The description names the resource (test result history) and three concrete capabilities that map onto the action enum: flaky detection, CI gating on real failures, and affected-test selection. It is a fragment rather than a verb+resource sentence and gives no differentiation from the 19 playcal_* siblings, but an agent can still tell what domain this tool operates in.

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

Usage Guidelines3/5

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

Phrases like 'spot flaky tests' and 'fail CI only on real failures' imply when the tool is valuable, but only for three of the four actions; 'record' is never framed as a use case. There are no exclusions, prerequisites, or pointers to alternatives among the many sibling tools.

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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