Galatic.ai

Queued job

get_queued_job

Idempotent

Check one item in the user's Orion generation queue and, when it is at the head of the queue and a render slot is free, start it. Returns { queueId, kind, status, jobId, model, error, errorCode, createdAt }, plus position and retryAfterSeconds while it waits; status is "queued", "started", "failed" or "cancelled". This poll is not side-effect free: starting the item charges Galatic.AI credits for that job. Once status is "started", follow the returned jobId with get_job_status; use cancel_queued_job to drop an item that has not started. queueId comes from an Orion chat request that was queued because all render slots were busy; no other Galatic.AI tool returns one.

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": {
      "queueId": "<queueId>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "get_queued_job"
  }
}

Input Schema

Name	Required	Description
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`queueId`	Yes	Queue item id from an Orion chat request that was queued.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 26, 2026
- addedInput schema / properties / queueId / description
```
Added value: +"Queue item id from an Orion chat request that was queued."
```
First observedOct 24, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false) by disclosing the consequential side effect that a poll can start the job and charge Galatic.AI credits. It also enumerates the returned fields, the four possible status values, and the presence of position/retryAfterSeconds while waiting, which the annotations do not cover. No contradiction with the declared hints.

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?

Front-loaded with the action and its conditional trigger, and every clause carries information (side effect, return shape, follow-up routing). The single dense paragraph packs several distinct concerns together with semicolons, which slightly hurts scanability.

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?

With no output schema, the description fully compensates by naming the returned fields and status values. For a side-effecting poller with billing implications, it covers cost, state machine, and next-step routing, leaving nothing an agent needs before calling it.

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 real meaning about the origin and uniqueness of queueId (produced only by a queued Orion chat request when all render slots were busy). The analytics-only parameters (context, llm_model, conversation_id) are left entirely to their schema text, so it is not fully comprehensive.

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 ('Check one item in the user's Orion generation queue... start it') and names the siblings it is not (get_job_status, cancel_queued_job). An agent can distinguish this poll-and-start tool from job polling or cancellation 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 Guidelines5/5

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

Gives explicit routing: after status is 'started', follow the returned jobId with get_job_status; to drop an item that has not started, use cancel_queued_job. It also explains the precondition for the queueId ('comes from an Orion chat request that was queued because all render slots were busy; no other Galatic.AI tool returns one'), which tells the agent when this tool is even callable.

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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