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Glama

parallel_task

Delegate the same task to multiple AI assistants in parallel, then collect each response for comparison or consensus.

Instructions

Delegate the same task to multiple assistants in parallel.

Args: assistant_ids: JSON list of assistant ids. task: The task/instruction for the assistants. context: JSON object of additional context. timeout: Per-task timeout in seconds. depth: Current delegation depth.

Returns: JSON: {"ok": true, "tasks": [...]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
depthNo
contextNo{}
timeoutNo
assistant_idsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.1/5.0
Behavior2/5

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 does not state what happens if one assistant fails, whether calls are truly concurrent, auth/rate-limit considerations, or side effects. The mention of 'depth: Current delegation depth' hints at recursion limits but is not explained as a safety constraint.

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 parallel-delegation purpose is front-loaded in the first sentence, followed by a compact Args/Returns block with no filler. The structure is mechanical but efficient, and nothing is wasted.

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

Completeness3/5

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

An output schema exists, so the Returns line is largely redundant and the description need not detail return values. But with zero annotations and no usage guidance for a multi-target orchestration tool, the definition stops short of what an agent needs to pick it over its siblings.

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 description coverage is 0%, so the description must compensate, and it does: it clarifies that assistant_ids is a JSON-encoded list and context a JSON-encoded object even though both are typed as plain strings, and gives units for timeout ('seconds'). 'depth' and 'task' are thinner restatements, but the type-encoding clarification is real value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (delegate) and resource (the same task to multiple assistants in parallel), which is clear enough to distinguish it from a single-target delegate_task. However, it never names or contrasts itself against close siblings like debate_task or pipeline_task, which also fan out to multiple assistants.

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

Usage Guidelines2/5

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

There is no when-to-use guidance. Given siblings such as delegate_task, debate_task, and pipeline_task, the agent is left to infer that 'parallel' means independent identical tasks rather than a debate or staged pipeline. No prerequisites, exclusions, or alternatives are mentioned.

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