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prmgint

peon-orchestrator

by prmgint

assign_task

Assign tasks to AI agents asynchronously and get an immediate task ID to track progress via status and result endpoints.

Instructions

Assign a task to an agent asynchronously. Returns task_id immediately. Use task_status and task_result to track progress.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
toolsNo
agent_idYes
max_iterationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.2

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the operation is asynchronous, returns an immediate task_id, and that progress tracking must be done separately. Some details are missing (e.g., error behavior, failure modes, prerequisites), but the key behavioral trait is clearly disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences, with the core operation and return behavior front-loaded. Every sentence contributes useful information, and it ends with a practical follow-up instruction. No fluff or redundancy.

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?

The description covers the essential async contract and points to the right tracking tools, but given no annotations, no output schema, and zero parameter documentation, an agent still lacks enough detail about the optional parameters and edge cases to use the tool fully confidently. The basic call is clear, but richer context would be needed for optional fields.

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

Parameters2/5

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

The schema has 0% description coverage for its four parameters, and the description only loosely implies the meaning of agent_id and task by naming them in prose. The optional 'tools' and 'max_iterations' parameters are left unexplained in both the schema and description, so an agent cannot fully understand what values are expected.

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?

The description states a clear verb and resource ('Assign a task to an agent') and communicates the asynchronous nature. It does not explicitly differentiate from sibling tools like spawn_agent, but the core purpose is specific and unambiguous enough for an agent to grasp the operation.

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?

The description provides a concrete usage pattern: call this to start an async task, receive a task_id, and then use task_status/task_result to monitor. However, it does not explicitly state when to choose this over alternatives such as spawn_agent or broadcast, so some context is left to inference.

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