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hire_agent

Hire a specialist AI agent from a marketplace of 13 coding assistants to perform a specific task. Choose an agent ID and provide a detailed task description to get targeted help.

Instructions

Hire a Vending Machine agent to perform a task.

Args: agent_id: Which agent to hire. One of: bug-hunter, test-goblin, devops-dwarf, cloud-sensei, code-gremlin, data-sprite, desk-pilot, embeddings-agent, inbox-zero, mcp-maker, number-crunch, pdf-forge, vibe-writer. task: Detailed task description. Be specific — the agent only sees this text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
agent_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Hire... to perform a task' and notes that the agent only sees the task text, but it does not explain side effects, asynchronous behavior, or what happens after hiring. This is a significant gap for a mutation-like tool.

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 compact and well-structured: a one-sentence purpose followed by a brief parameter breakdown. Every sentence provides useful information without redundancy or fluff.

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?

For a two-parameter tool, the description covers the inputs well and includes the full agent list. However, it leaves out behavioral context such as how to check on the hired task (sibling check_job exists) or whether hiring is asynchronous. Since an output schema exists, return values need not be described, but the operational lifecycle is incomplete.

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

Parameters5/5

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

The description explicitly lists all allowed agent_id values, which is not present in the schema (no enum). It also gives actionable guidance for the task parameter, emphasizing detail and the fact that the agent only sees this text. This fully compensates for the schema's 0% property description coverage.

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 uses a specific verb 'Hire' and identifies the resource as 'Vending Machine agent', making the action clear. It distinguishes from siblings like list_agents and check_job, but it does not explicitly contrast with general tools like 'run', leaving some ambiguity.

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

Usage Guidelines3/5

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

The description states that the tool hires an agent to perform a task, providing clear context for when it is appropriate. However, it gives no guidance on when to prefer this over alternatives, nor does it mention companion tools like check_job for tracking the task. Thus, there is clear context but no exclusions or alternative routes.

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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