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FluxMCP

by AceDataCloud
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Python
Hybrid

flux_get_tasks_batch

Retrieve status and results for multiple Flux image or video generation tasks in one request, avoiding repeated single-task queries.

Instructions

Query multiple Flux image or video generation tasks at once.

Efficiently check the status of multiple tasks in a single request.
More efficient than calling flux_get_task multiple times.

Use this when:
- You have multiple pending generations to check
- You want to get status of several images at once
- You're tracking a batch of generations

Returns:
    Status and result information for all queried tasks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idsYesList of task IDs to query. Maximum recommended batch size is 50 tasks.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.3
  2. Removedv0.1.2
  3. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It confirms this is a read-style status check returning 'status and result information for all queried tasks', but discloses nothing about permissions, rate limits, or failure behavior for missing/invalid task IDs. Adequate but not rich.

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 core purpose, then a scannable usage list and a short returns note. The bullet list is somewhat redundant ('check status of several images' vs 'tracking a batch'), keeping it just short of a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter read tool with an output schema present, the description covers purpose, usage, and a return-value hint without needing to detail the response shape. Nothing critical to correct invocation is missing.

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

Parameters3/5

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

Schema description coverage is 100%, and the schema itself documents the single task_ids parameter including the recommended max batch of 50. The description adds no parameter-level detail beyond the schema, so the baseline 3 applies.

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 ('Query multiple Flux image or video generation tasks at once') and explicitly differentiates from the sibling flux_get_task by name. An agent can immediately tell it is the batch variant of a task-status query.

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?

Provides an explicit 'Use this when' list of three qualifying scenarios and directly names the alternative ('More efficient than calling flux_get_task multiple times'), so the routing decision is unambiguous.

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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