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create_bucket

Create a new Qencode Media Storage bucket.

Call this ONLY on an explicit request to create a bucket or to keep a
result long-term. Do NOT call it just because a transcoding request lacks a
`destination`, or because the user says they have no bucket / nowhere to
save the output — that is the default temp-storage case: omit `destination`
(24-hour temp storage) and disclose it, do not provision an account-level
bucket the user did not ask for.

Args:
    name: 6–63 chars, lowercase letters / digits / hyphens
        (`^[a-z0-9][a-z0-9-]{4,61}[a-z0-9]$`) — no underscores or uppercase.
        A name that breaks this pattern is rejected (`invalid_bucket_name`).
    region: one of us-west, eu-central.

Returns `{bucket, region, status}`:
  - `status: "created"` — a new bucket was provisioned.
  - `status: "exists"` — you already own a bucket with this name (no-op).
A name already taken by another account fails with `bucket_conflict`.

The bucket's CDN endpoint is provisioned asynchronously, so a new bucket is
usually usable within a few seconds but may not appear in `list_buckets`
immediately — poll `list_buckets` if you need to confirm it before using it.
During that same async window the bucket reports `public: false` and then
flips to `public: true` within a few seconds up to ~a minute as CDN
provisioning completes; the `public` value read right after creation is not
stable (see qencode://docs/storage).

This tool does not make the bucket public; visibility is otherwise managed in
the Qencode portal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
regionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
bucketYes
regionYes
statusYes

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=false, idempotentHint=false), the description discloses key behavioral traits: asynchronous CDN provisioning causing delayed visibility, unstable `public` value immediately after creation, status values `created`/`exists`, and the `bucket_conflict` error for name collisions. This is exactly the kind of context that helps an agent anticipate side effects.

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 well-structured with clear sections (usage rule, args, returns, async note, visibility caveat) and front-loads the main purpose. It is somewhat verbose, but every sentence carries useful information; no filler. A minor trim of the async details could improve conciseness, but it remains appropriately sized for the tool's complexity.

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?

Given the tool's complexity (async provisioning, idempotent no-op, naming validation, public flag instability), the description is complete. It explains return values, edge cases, and operational guidance (poll list_buckets, expect public flag flip). The output schema is also referenced with the return format, so the description fully covers what an agent needs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides only parameter names and types, with 0% coverage in the schema. The description compensates fully by specifying the name regex (lowercase letters/digits/hyphens, 6-63 chars), the allowed regions (us-west, eu-central), and the error behavior for invalid names. This is critical meaning beyond what the schema provides.

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 opens with 'Create a new Qencode Media Storage bucket' — a specific verb and resource. It clearly differentiates from siblings by explicitly stating this tool is only for creating a bucket or long-term retention, not for default temp-storage scenarios, and references list_buckets as the tool to poll for confirmation.

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?

The description gives explicit when-to-use and when-not-to-use guidance: 'Call this ONLY on an explicit request' and 'Do NOT call it just because a transcoding request lacks a destination'. It also names the alternative behavior (omit destination for 24-hour temp storage) and points to list_buckets for confirming creation.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools are clearly distinct (list_buckets vs list_objects, search vs fetch docs). Minor overlap exists between transcode_video and start_encode2_raw (both submit jobs) and between get_job_status and get_job_status_detailed, but the descriptions explicitly state when to use which, making misselection unlikely.

Naming Consistency4/5

Names overwhelmingly follow verb_noun (create_bucket, list_buckets, get_download_url, transcode_video). A few deviations like start_encode2_raw, wait_for_job, and download_url_to_bucket break the pure pattern, but the convention is still easily predictable.

Tool Count5/5

13 tools is well-scoped for a video encoding platform: bucket management, transcoding submission/status/wait, result retrieval, and docs search/read. Each tool serves a clear purpose without redundancy or bloat.

Completeness4/5

The set covers the main lifecycle: create bucket, ingest via copy, transcode (two entry points), poll status, fetch result, and generate download URLs. Missing cancel/delete operations for jobs and buckets are notable but not critical for core workflows, and the docs tools help fill knowledge gaps.