Skip to main content
Glama
Sign In

AgentCompute

by io.github.Ockram4444
OverviewDiscussionsAdmin

Generate an image on the GPU

submit_image_job
Idempotent

Queue a free text-to-image task from a complete scene description. Each task is independent, not an edit of a previous image. Returns a job ID; poll get_studio_job then get_studio_image for the PNG. Configured model and size appear in service status/results. No external AI fallback.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>",
      "consent": "<consent>",
      "idempotency_key": "<idempotency_key>"
    },
    "name": "submit_image_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesComplete image description. English prompts are recommended.
consentYesConfirm permission to process this nonconfidential content.
access_tokenNoAccount credential from My account; optional with Bearer authentication.
idempotency_keyYesStable identifier for this logical submission. Reuse unchanged on retries; use a new value for new work.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / idempotency_key / description
      Added value: +"Stable identifier for this logical submission. Reuse unchanged on retries; use a new value for new work."
  2. Changed1 schema field changed
    • changedInput schema / properties / access_token / description
      Previous value: -"Private token from create_trial_access; optional with Bearer authentication."New value: +"Account credential from My account; optional with Bearer authentication."
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (non-destructive, non-read-only, idempotent, closed-world), and the description adds real behavioral context on top: the task is independent of prior images, it is asynchronous and returns a job ID, and there is 'No external AI fallback,' which signals content never leaves the local service. It does not address failure modes, latency, or quota, which keeps it below the top mark.

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 short sentences, front-loaded with the core action and the workflow, with no filler. The sentence 'Configured model and size appear in service status/results' is slightly cryptic about which field to check, which is the only weak spot.

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?

There is no output schema, so the description carries the return-value burden and does so fully: it states that a job ID comes back and names both the polling and retrieval tools for the PNG. For a four-parameter async submission tool with rich annotations, nothing essential for a correct invocation is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema by stressing that the prompt must be a 'complete scene description' and that each task is independent — i.e., the prompt cannot lean on prior images. It adds nothing about consent or access_token, but those are already well documented in the schema.

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 gives a specific verb and resource ('Queue a free text-to-image task') and immediately scopes it against related operations ('Each task is independent, not an edit of a previous image'). Combined with the named follow-up tools, an agent can tell this apart from the other submit_* siblings by modality without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides a clear usage path — submit, then poll get_studio_job, then get_studio_image — which is exactly the when-to-use information an agent needs for an async job. It stops short of explicitly excluding or comparing against submit_agent_job/submit_chat_job, so the alternative-selection reasoning is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All AgentCompute toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources