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GroundTruth Field Evidence

Human Do

human_do

Create a task for a human oracle to complete in the real world. Requires x402 payment. Returns a task_id to poll with task_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesWhat you want the human to do
proof_typeYesType of proof
budget_usdtNo0.01
instructionsYesDetailed instructions for the human
target_locationNoOptional target location and allowed capture radius
timeout_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses the x402 payment requirement and the asynchronous task_id polling pattern. However, it omits failure modes, side effects, and what happens after proof is submitted, leaving notable gaps for a paid creation 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 two sentences with no filler. It front-loads the core purpose, then states the payment requirement and the follow-up mechanism. Every sentence earns its place and is easy for an agent to parse quickly.

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

Completeness2/5

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

Given no output schema, no annotations, a nested target_location object, and six parameters, the description is incomplete. It mentions task_id but not the full response shape, payment mechanics, or how location constraints affect the task. An agent would need additional context to invoke this tool with full confidence.

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 description adds no parameter-level meaning beyond the schema. It does not explain intent, proof_type, instructions, budget_usdt, target_location, or timeout_seconds. Schema coverage is 67%, leaving budget_usdt and timeout_seconds undocumented in both schema and description, and the description does not compensate.

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 specific verb and resource: 'Create a task for a human oracle to complete in the real world.' It clearly communicates what the tool does and implicitly separates it from task_status by mentioning the returned task_id is polled there. However, it does not explicitly differentiate from ground_truth_info or review_task.

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 implies when to use the tool: when a real-world human task needs to be completed and x402 payment is acceptable. It does not state when to prefer sibling tools or provide exclusions, so usage guidance is present but only implicit.

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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