Veredicto Humano

Pedir el veredicto de una persona real

ask_human_verdict

Pagá $0.10 USDC en Base por el juicio subjetivo/cultural de una persona real (no un LLM) sobre una pregunta — especializado en contexto argentino. Sin pago adjunto devuelve los requisitos de pago x402; con el pago, crea el ticket y devuelve {ticket, resultToken, poll}. La respuesta del humano llega de forma asíncrona (minutos u horas) y vuelve a vos, el agente, por get_human_verdict: no llega por Telegram (Telegram es solo cómo la persona recibe la pregunta).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "<question>"
    },
    "name": "ask_human_verdict"
  }
}

Input Schema

Name	Required	Description
`labels`	No	CSV de 2-6 respuestas categóricas posibles, ej. "natural,traducido"
`context`	No	Contexto extra que ve el humano
`question`	Yes	La pregunta de juicio subjetivo

Output Schema

Name	Required	Description
`poll`	Yes	URL equivalente para pollear por HTTP
`status`	Yes	Siempre "pending": la persona todavía no respondió
`ticket`	Yes	Id del ticket, para get_human_verdict
`expiresAt`	Yes	Cuándo vence el ticket si nadie responde (ISO 8601)
`resultToken`	Yes	Secreto para leer el resultado, guardalo

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 25, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "expiresAt": {
+      "description": "Cuándo vence el ticket si nadie responde (ISO 8601)",
+      "type": "string"
+    },
+    "poll": {
+      "description": "URL equivalente para pollear por HTTP",
+      "format": "uri",
+      "type": "string"
+    },
+    "resultToken": {
+      "description": "Secreto para leer el resultado, guardalo",
+      "type": "string"
+    },
+    "status": {
+      "const": "pending",
+      "description": "Siempre \"pending\": la persona todavía no respondió",
+      "type": "string"
+    },
+    "ticket": {
+      "description": "Id del ticket, para get_human_verdict",
+      "format": "uuid",
+      "type": "string"
+    }
+  },
+  "required": [
+    "status",
+    "ticket",
+    "resultToken",
+    "poll",
+    "expiresAt"
+  ],
+  "type": "object"
+}

First observedSep 14, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description adds substantial behavioral context beyond annotations: it discloses the payment requirement, cost, ticket creation, async latency (minutes or hours), and the exact mechanism for retrieving results. It also clarifies that Telegram is only used for delivering the question to the human, not for returning the answer. No contradiction with annotations.

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 three sentences, front-loaded with the most critical information (cost, human judgment, not an LLM), and every sentence earns its place by covering payment behavior, ticket creation, return values, async retrieval, and the Telegram clarification. No fluff or repetition.

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

Completeness5/5

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

For a paid, async, side-effecting tool, the description covers all important operational context: cost, payment flow, ticket creation, return payload, latency, and retrieval path. An output schema exists, so detailed return-value documentation is unnecessary. The description is complete enough for an agent to use it correctly.

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%, so the schema already documents all three parameters. The description adds context about the question being subjective/cultural and Argentine-focused, but does not specifically add meaning for labels or context beyond what the schema provides. Baseline 3 is appropriate.

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?

The description clearly states a specific action: pay $0.10 USDC to get a real human's subjective/cultural judgment on a question, specialized in Argentine context. It explicitly contrasts with an LLM and mentions the asynchronous retrieval path via get_human_verdict, which distinguishes it from its only sibling.

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?

The description explains both usage states: without payment it returns x402 payment requirements, with payment it creates a ticket. It also tells the agent where the result will arrive (via get_human_verdict) and that it won't arrive via Telegram, effectively routing the agent to the correct retrieval tool.

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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