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build_e2e_test

Destructive

Run the FULL behavioural test suite against a PUBLISHED bundle in the project's OWN already-deployed app (no throwaway project is created) — auth/RBAC/multi-tenant, CRUD round-trips, workflows + event emission, services, aggregates, real-browser UI. WRITE-SAFE: on a DEV project the write suites create + delete only their OWN test records (your real data stays read-only); a LIVE project is auto-restricted to read-only suites so production data is never mutated. This is the deep complement to build_smoke_test ('loads + reads one row'); it proves the app actually WORKS. COSTS A FULL RUN (~2-4 min of real compute) — a PRE-DELIVERY gate, NOT a per-edit check; run it after build_validate + build_doctor pass, on a deployed + seeded project. ASYNC: returns a run_id; poll build_e2e_test_status. Findings are layer-attributed so you know which are yours to fix (app/config) vs. report (platform/sdk). Requires platform enablement.

Input Schema

NameRequiredDescriptionDefault
suitesNoSubset to run (default ['all']). Skipping a core suite caps the verdict.
live_emailNoActually send a test email (default false → audit-only).
project_uuidYesThe project the published bundle belongs to.
version_uuidNoPublished version to test. Default: latest.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removed
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (destructiveHint=true, openWorldHint=true, readOnlyHint=false, idempotentHint=false), it discloses exactly what is at risk: on DEV projects only the suite's own records are created/deleted, and LIVE projects are auto-restricted to read-only suites. It also states compute cost (~2-4 min), the async run_id contract, layer-attributed findings, and the platform-enablement prerequisite.

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?

It is front-loaded with the core action and cost caveat, and the emphasis caps are used sparingly and meaningfully. It is dense and long, but nearly every clause carries decision-relevant information rather than restating the name.

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 an async, expensive, potentially destructive run with no output schema, the description supplies prerequisites, safety behavior, expected duration, the poll sibling for results, and how to triage findings. Nothing an agent needs to invoke it responsibly is missing.

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% and every parameter is documented in the schema, including the suite enum and the live_email default. The description adds contextual meaning (deployed + seeded project, published version) but no parameter-specific syntax or edge cases, so the documented baseline 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?

The description names a specific verb and resource ('Run the FULL behavioural test suite against a PUBLISHED bundle') and enumerates the covered layers (auth/RBAC, CRUD, workflows, services, aggregates, UI). It explicitly distinguishes itself from the sibling build_smoke_test ('loads + reads one row') and from a per-edit check, so an agent can select it without opening a 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?

It gives an explicit when: after build_validate + build_doctor pass, on a deployed + seeded project, as a PRE-DELIVERY gate rather than a per-edit check. It also names the lighter alternative (build_smoke_test) and the condition that selects it, plus the downstream poll tool for the async 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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