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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

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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