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test_component

Run standalone cloud verification on code you already have: give it a component (filePath/exportName/signature) plus acceptance criteria, and it generates a black-box verification test, validates that test against a mechanical stub, then executes your REAL code against it in the cloud (isolate or container, per stack). No code generation happens here — use build_feature for that. Returns a testId immediately; call stream_test to follow progress and get the pass/fail verdict. Call get_usage_guide for detailed usage guidance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe REAL implementation to test. Never shown to the model that generates the verification test.
componentYesMUST match the real code exactly — the generated verification test imports/calls by these exact names.
stackTypeYesRuntime target stack (JavaScript/TypeScript only at launch), e.g. "worker", "typescript", "node", "react".
contextFilesNoOptional ambient read-only files the code under test may import.
acceptanceCriteriaYesWhat the code must do — the verification test is generated from this alone.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses the verification pipeline, that execution happens in the cloud (isolate or container per stack), that validation runs against a mechanical stub first, and that the tool is asynchronous (returns a testId immediately). It doesn't cover permissions, cost/rate limits, or failure handling, so it stops short of 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?

Four compact sentences, front-loaded with the core action and the pipeline, then alternatives and follow-ups. Every sentence is load-bearing, though the parenthetical detail on isolate/container and the multi-clause first sentence make it slightly denser than strictly necessary.

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

Completeness5/5

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

For a complex, async, nested-schema tool with no output schema, the description covers the workflow end to end: what it takes in, what happens internally, that it returns a testId synchronously, and which follow-up tool yields the verdict. It also routes to build_feature and get_usage_guide, leaving no obvious gap for correct invocation.

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 the schema already documents all five parameters in detail (including the exact-match constraint on component and the stub-hidden code field). The description only restates the component shape (filePath/exportName/signature) and acceptance criteria, adding no syntax or constraint detail beyond the schema, so the baseline 3 applies.

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 and resource: 'Run standalone cloud verification on code you already have,' and describes the pipeline (generate black-box test, validate against a stub, execute real code). It explicitly distinguishes itself from siblings by ruling out code generation and naming build_feature as the alternative. An agent can tell it apart from build_* tools without reading either schema.

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?

Gives explicit when-to-use ('code you already have'), when-not-to ('No code generation happens here — use build_feature for that'), and the required follow-up ('call stream_test to follow progress and get the pass/fail verdict'), plus a pointer to get_usage_guide. Nothing is left to inference.

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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