Scenario

Overview

Scenario (scenario.com) generates AI images, video, 3D, and audio across 500+ models plus custom training, all through the core loop below.

Setup

Endpoint:

https://mcp.scenario.com/mcp

(Streamable HTTP). Prefer OAuth: no credentials pass through the conversation. Client config, API-key setup for headless use, and per-client re-authentication: references/setup.md. Never ask an agent to collect, encode, or echo a secret. A connection that authenticated once and fails later is re-authenticated per client before anything else is debugged: a server-side authentication fix lands only through a fresh handshake, and

diagnostics_run

names the failing layer (

auth

tenant-scope

api-unreachable

, or

healthy

) with trace ids for support.

The default toolset is wider than the core loop below:

asset_get

job_get

jobs_list

and

models_list

are in it too and are called directly, so treat the table as the loop rather than the whole list.

?toolsets=full

exposes everything. The catalog tools are the ones outside it (collections, tagging, analysis, training, members, keys):

scenario_tools_search

with the tool name or plain keywords as

query

(it takes only

query

and

limit

) returns the schema and lane, and the matching

scenario_tool_execute_read

write

delete

runs it with

{name, parameters}

, scope ids inside

parameters

when the target's

inputSchema

declares them, which nearly every catalog tool does (

plan_generation

, which takes only

description

, is the exception), unlike a direct tool's top-level

team_id

project_id

. The lane is the result's own

permission

, not what the verb sounds like:

asset_download

and

asset_analyze

are both write-class.

Scope first

Resolve scope first, then pass

team_id

and

project_id

on every later call.

teams_list

returns the teams with their projects;

projects_list

requires a

team_id

, so it cannot come first. Confirm the pair with the user: a guess writes into someone else's project. A non-interactive run takes the pair from its task instructions; when they name none, stop and list the choices.

The server fills scope in only for read-only tools with one candidate remaining; anything else fails rather than guesses, and the error names which half is wrong (see Errors and recovery). Scope errors are the most common failure here, surfacing mid-session on the first call that drops the pair once a second team or project is in play.

Quick reference

Step	Tool	Notes
Resolve scope	`teams_list` , then `projects_list`	Once per session; pass the ids on every call
Find a model	`search` or `recommend`	Free; `recommend` for a capability, `search` for a name
Get the schema	`model_schema_get`	Always before `model_run` ; check `runs_as` and caps
Generate	`model_run`	Schema-conformant `parameters` ; `dry_run` for cost
Wait	`jobs_wait`	Whenever `model_run` returns a `job_id` without assets; never loop `job_get`
View / save	`asset_display` / `asset_download`	Never paste raw asset URLs; `format` converts images only
Inspect an asset	`asset_get`	Free; dimensions, duration, `firstFrame` / `lastFrame` ids
Upload inputs	`upload_asset` + `upload_asset_complete`	Local files become asset_ids
Refine a prompt	`prompt_spark`	Advisory rewrite; needs `model_id`
Quota / debugging	`usage` , `diagnostics_run`	CU consumption; `diagnose` MCP prompt

A multi-step request ("product video with voiceover", "concept to 3D") goes to

plan_generation

(catalog-only, read lane): plain words in

description

, ordered steps out, each naming a tool and optional model hint; it runs nothing. Single-step:

recommend

Worked example

Generating a stylized game prop image:

```
recommend
```
with the user's own words as
```
prompt
```
when the need is a capability;
```
search
```
with
```
target="models"
```
,
```
query="flux"
```
,
```
public=true
```
when you have a name.
```
search
```
ranks by keyword and its
```
filters
```
hold no capability key, so a capability-worded query can rank the wrong output type first. For the user's own trained models, omit
```
public
```
on
```
search
```
(
```
search
```
has no private flag); on
```
recommend
```
the flag is
```
include_private_models: true
```
. Re-discover ids each time: availability differs per team.
```
model_schema_get
```
on the pick: exact field names, types, required flags, defaults, and caps such as the prompt's
```
max_length
```
(an overrun is a 400, never a trim). File fields take asset ids even when named
```
...Url
```
, and
```
cost_impact: true
```
flags what moves the price.
If the schema carries
```
runs_as
```
(
```
"lora"
```
or
```
"composition"
```
), never send that model's own id to
```
model_run
```
. Its
```
run_with.required_arguments
```
holds the real call:
```
model_id
```
there is the base model, and its
```
parameters
```
(the
```
loras
```
or
```
modelId
```
wiring) merge into inputs from the same schema. Sending
```
required_arguments
```
alone discards your prompt.
Optional:
```
prompt_spark
```
rewrites a thin prompt into an on-model one; pass the discovered id (a LoRA's own, not its base) and the draft
```
prompt
```
. Skip deliberate prompts.
```
model_run
```
with
```
model_id
```
and schema-conformant
```
parameters
```
. If cost matters (the default assumption unless the user says otherwise), price first with
```
dry_run: true
```
, a top-level argument beside
```
model_id
```
: no job is created and the response's
```
creativeUnitsCost
```
is the exact payload's price; a
```
recommend
```
cost quote assumes defaults, and the schema's
```
cost_impact
```
fields move the real number. Then run: asset_ids come back, or a
```
job_id
```
for
```
jobs_wait
```
. The
```
status
```
beside it is
```
in_progress
```
when the server's wait budget ran out; with
```
wait=false
```
it is the backend's live word at creation (
```
queued
```
,
```
in-progress
```
,
```
warming-up
```
), a spelling that is not a different state.
```
jobs_wait
```
with
```
job_ids=[...]
```
(up to 32); each completed row carries
```
assetIds
```
and
```
cuCost
```
, so no
```
job_get
```
follow-up; on timeout re-call with the returned
```
pending_job_ids
```
as
```
job_ids
```
. Failed jobs are reimbursed, except xAI generations stopped by moderation.
```
asset_display
```
shows the asset inline; its
```
format
```
picks the rendering:
```
display
```
(the default) returns an inline image plus links and viewer data;
```
viewer
```
returns a lighter payload for a host that renders the interactive widget;
```
json
```
and
```
markdown
```
return metadata and links.
```
display
```
does not disable a host's widget. For PNG files, use
```
asset_download
```
with
```
format: "png"
```
, one call per asset.
```
asset_download
```
returns a file URL (save with
```
curl -L
```
, it may redirect).
```
format
```
is an image conversion (
```
png
```
,
```
webp
```
,
```
jpg
```
, and
```
gif
```
to keep an animated GIF animated: the
```
png
```
default flattens it to one frame) and nothing else; omit it for video, 3D, and audio. When
```
curl
```
reports
```
host_not_allowed
```
or
```
CONNECT tunnel failed, response 403
```
while MCP calls work, check the host's proxy or sandbox egress policy. A CDN 403 alone does not establish the cause; request a fresh download URL before diagnosing it: Sandbox network access names the setting that lifts it, an organization-level allowlist on some hosts that the user may not be able to change. Meanwhile hand over the
```
app_url
```
that
```
asset_display
```
returns, where the user downloads directly and, for a mesh, picks the 3D export format the app offers, none of which
```
asset_download
```
converts to. The signed URL also opens in a browser but expires, so it is a last resort, never the deliverable.

Local inputs go up with

upload_asset

: always

file_name

content_type

, and

kind

(

image

audio

video

3d

), plus exactly one of

file_size

data

, since the call fails without either. Prefer

file_size

, the file's exact byte count read from disk, and omit

data

: the reply carries presigned part URLs and

instructions

; PUT each part's raw bytes to its URL with no added headers (a checksum header makes the store answer 403), check every PUT returned 200, then

upload_asset_complete

with the

upload_id

. Inline base64

data

only under 100KB (that path returns the asset directly, with no complete step); a larger file is rejected naming the cap. Scope rides on both; they take no other fields: no parts list, no etags. The server decodes the file at completion, so

Upload upl_… failed: Corrupt JPEG data

premature end of data segment

bad Huffman code

libpng read error

means the uploaded image could not be decoded: check for a corrupt source, a truncated or re-encoded body, a missing part, or a mismatched

file_size

. Confirm the local file opens, its content type matches, and its byte count is correct, then start over with a fresh

upload_asset

and raw PUTs; never re-complete the same

upload_id

Unhandled image format

at upload lists what

kind: "image"

accepts: convert locally first. A host with no shell cannot PUT parts at all; the user uploads in the web app at app.scenario.com and the agent continues from the asset id.

Filing is part of delivering, not a tidy-up: run the catalog tools above with arguments under

parameters

(never

arguments

, which the executor drops silently, surfacing as a scope error that is not one).

collection_create

takes a name and the scope pair only;

asset_ids

sent there is ignored without an error, so adding is always a second call,

collection_add_assets

with

collection_id

and

asset_ids

, and re-adding a filed asset is a hard 400 naming the duplicate: drop it and continue. Confirm membership with

assets_get_bulk

and read each record's

collectionIds

;

search

filters={"collection_ids": [...]}

lags on a fresh write.

collection_add_assets

takes at most 49 ids per call (past it, 400

You can not add more than 49 assets at once

), so chunk a larger set.

asset_add_tags

is additive, one

asset_id

per call, so a set is one call per asset, and a tag the asset already carries returns 400

Duplicated tag

: read

tags

off

asset_get

first and send only the missing ones; skip the write when none are missing.

For reusable templates or reference content, follow the shared asset lifecycle: resolve existing assets, stage new versions, publish only through a supported operation, and verify public access before recording public IDs. Uploading and filing alone are not publication.

Errors and recovery

Error	Recovery
`context_missing`	Nothing resolved: `teams_list` , then `projects_list`
`context_ambiguous`	Several fit: present the options; the user picks (non-interactive: task instructions name the pair, else stop and list)
403 Forbidden	Usually wrong scope, not missing: re-check the id pair
403 naming a plan	Surface the upgrade or switch models; retrying never clears it. `recommend` pre-flags these as `requires_plan_upgrade` (never run one) unless its response says plan gating is `_degraded` ; then this row is the backstop
429 with `details.actionName` = `parallel-custom-jobs`	Per-team generation concurrency ceiling: keep at most `actionLimit` jobs in flight, use `wait=false` for launches and `jobs_wait` to retire existing jobs before launching more. An immediate retry repeats the error
429 naming a quota, balance, or seat limit	Read the reason and `details` together, including `actionLimit` and `limitScope` when present. Generic plan-limit wording alone does not distinguish concurrency from consumption or feature access. A CU limit does not prove the balance is zero; the requested run may exceed what remains. Stop the affected batch, use `usage` for consumption, and report the stated remedy. A per-user cap goes to the team admin; a seat-cap error can also block uploads. Do not change billing or membership automatically
429 with an explicit retry delay	Respect the returned delay before retrying; this is not evidence that the user needs an upgrade
Other 429, including an unfamiliar `actionName`	Do not classify every other action as a count quota, or infer a training quota's behavior from its name. Report the error and run `diagnostics_run` in the same scope before choosing recovery
`jobs_wait` timeout ( `in_progress` )	Not an error: re-call with the returned `pending_job_ids` , never a second `model_run` or a cancel; it takes no timeout argument
Transport error on `model_run` (no HTTP status)	The request may still have landed: `jobs_list` before any re-run, or a lost response becomes a double charge; a `dry_run` call creates no job and always retries safely
400 `Cannot cancel this type of job`	A launched job is committed spend: `job_cancel` rejects most generation jobs, so plan batches with no abort path
400 `Invalid target format`	`format` converts images only: omit it for video, 3D, audio
`Either 'file_size' … or 'data' is required` on `upload_asset`	Read the byte count from disk and send it as `file_size` (omit `data` ); inline `data` is for files under 100KB only
`Upload upl_… failed: Corrupt JPEG data` (or `libpng read error` )	The uploaded image could not be decoded: check the source file, content type, byte count, and part transfers before a fresh upload; re-completing does not repair corrupt bytes
404 on `asset_get`	The id does not exist and no retry makes it appear: re-read `assetIds` off the `jobs_wait` or `job_get` row, since a guessed or mistyped id is the usual cause
400 `At least one of query, filter, image, or images must be provided`	`search` never lists bare: a "newest first" asset listing is `target="assets"` , `filter="createdAt EXISTS"` with `sort_by=["createdAt:desc"]`
`recommend` client-side timeout	It ranks on live data and commonly runs 30 seconds, sometimes over a minute: wait or re-call it; do not fall back to `search` for a capability

Common mistakes

A bare value where the schema says
```
array: true
```
: silently dropped, the run ignoring your reference or LoRA.
```
asset_get
```
on the output echoes what the run consumed (
```
metadata.referenceImages
```
,
```
parentId
```
), the cheapest proof it was not.
Taking
```
recommend
```
's
```
ranked[0]
```
blindly: read
```
next_step.type
```
first. On
```
ask_user
```
, present the options; the user's pick wins (non-interactive: task instructions, else
```
proceed
```
). On
```
proceed
```
, prefer
```
specialty.model_id
```
, else the first
```
ranked
```
entry its own text does not mark deprecated, reading
```
tradeoff
```
and
```
explanation
```
as well as
```
caveats
```
, since the flag lands in any of them: the ranking is by measured performance, not by lifecycle, so a deprecated member can top it.
Debugging blind: the
```
diagnose
```
MCP prompt (or
```
diagnostics_run
```
with the scope pair the failure happened under and any
```
mcpt_
```
ids seen as
```
observed_trace_ids
```
) returns trace ids;
```
usage
```
answers credit questions.
Guessing a file field's name: 400
```
Input image is required
```
(or
```
images
```
,
```
referenceImages
```
,
```
startImage
```
,
```
frontImage
```
,
```
video
```
,
```
model
```
, each a different model's name for the same idea) means the payload never carried the field the schema's
```
required
```
list names, so copy that name verbatim; a URL in a file field reads as missing too, since file fields take asset ids only.
Feeding an image field a video, audio, or 3D asset: 400
```
Unhandled image format
```
lists the image types it accepts.
```
asset_get
```
reports the asset's
```
mimeType
```
; pull a still with the clip's
```
firstFrame
```
when the model wants an image.
```
filters.kind
```
on
```
search
```
with
```
target="models"
```
: a 400
```
"kind" is not a filterable field
```
, since
```
kind
```
describes assets. Narrow models with
```
filters.type
```
(the architecture) or
```
tags
```
, or go through
```
recommend
```
for a capability.
Filtering
```
models_list
```
by
```
type
```
without
```
privacy: "public"
```
: a 400 says so. A private listing narrows by
```
status
```
or
```
modality
```
, or goes through
```
search
```
(private by default).

scenario

NPX Install

Tags

SKILL.md Content