Galatic.ai

Cancel queued job

cancel_queued_job

Cancel one Orion queue item that has not started yet and get { queueId, status "cancelled" }. Nothing was charged for it, so there is nothing to refund. An item already started, failed or cancelled is refused (409); an unknown or other user's queueId is 404. queueId comes from an Orion chat request queued while render slots were busy; check it first with get_queued_job, which can also start it.

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": "cancel_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.
`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 27, 2026
- addedInput schema / properties / queueId / description
```
Added value: +"Queue item id from an Orion chat request."
```
First observedOct 24, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Goes well beyond the annotations: discloses that nothing was charged and no refund is involved, and enumerates failure semantics (409 for wrong state, 404 for unknown or other-user queueId). Doesn't cover concurrency or whether the cancellation is reversible by re-queuing, which keeps it below a 5.

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-loads the mutation, the return value, and the no-charge fact before the error cases and the sibling pointer, with no filler. It is a single dense run-on block, so scanning the error semantics takes a second pass.

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?

No output schema exists, but the description supplies the return shape ({ queueId, status "cancelled" }) plus the two error codes and the sourcing of the required queueId. An agent has everything needed to call this correctly or to decide not to.

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; the description adds real value by explaining where queueId originates (an Orion chat request queued while render slots were busy) rather than just restating its type. The other three params (context, llm_model, conversation_id) are left entirely to their schema descriptions.

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 with an explicit scope guard ('one Orion queue item that has not started yet') and names the sibling it complements (get_queued_job). An agent can distinguish this from get_queued_job without opening either 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?

Explicitly instructs to check with get_queued_job first and notes that sibling can also start the item, plus lists which item states make this call invalid (already started, failed, cancelled → 409). It stops short of naming a preferred alternative when the item is already started, so it isn't fully closed-loop.

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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