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AceDataCloud

SeedreamMCP

by AceDataCloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Seedream task results in batch

seedream_get_tasks_batch
Read-onlyIdempotent

Check multiple Seedream image tasks in one request to track batch generation statuses and image details, reducing repeated single-task queries.

Instructions

Query multiple Seedream image tasks at once.

Efficiently check the status of multiple tasks in a single request.
More efficient than calling seedream_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 image information for all queried tasks.

Input Schema

NameRequiredDescriptionDefault
task_idsYesList of task IDs to query. Allows querying multiple tasks at once.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.5
  2. Removedv0.1.3
  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?

The annotations already declare the operation as read-only, idempotent, open-world, and non-destructive, so the safety profile is well covered. The description adds that results include status and image information for all queried tasks, but does not disclose batch-size limits, partial-failure behavior, or how unknown task IDs are handled.

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?

The purpose is front-loaded and the bullet structure is easy to scan. There is mild redundancy between the opening sentence, the efficiency sentence, and the bullet items, and the 'Returns' section duplicates information available via the output schema, but none of it is confusing.

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 batch read with an output schema and rich annotations, the description covers purpose, usage, alternative, and return content adequately. It stops short of addressing practical edge cases such as maximum batch size or mixed valid/invalid task IDs.

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 already documents task_ids as a list of task IDs supporting multiple tasks. The description adds no syntax, format, or ordering constraints beyond what the schema provides, so the baseline of 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 Seedream image tasks') and explicitly positions itself against the sibling single-task tool by saying it is 'More efficient than calling seedream_get_task multiple times.' An agent can distinguish this from seedream_get_task 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 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 covering pending generations, checking several images at once, and tracking a batch. It also names the alternative (seedream_get_task) and the condition under which this tool is preferable, leaving no routing ambiguity.

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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