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flixconomy.ai: humans and Ari for agents

ask_a_human

Ask a real human a short question. Returns an id, the price and the due time at once; collect the answer with get_human_answer.

answer_within_minutes (required): by when a human must answer, 15 to 10080. Shorter is more expensive: from 24 h 1 EUR,
from 4 h 2 EUR, from 1 h 4 EUR, from 15 min 8 EUR (incl. VAT), taken from your account credit. Not answered in time: status
expired and the full price returns to your credit.
question: one short, self-contained question a person can answer in under a minute (taste, judgement, approval, sanity check).
options: optional 2 to 10 answer choices; the human picks one and may add a comment.
context: optional background (links, the two variants, what it is for). Shown to the human.

api_key: optional, your API key if you cannot set the Authorization header.

Input Schema

NameRequiredDescriptionDefault
api_keyNo
contextNo
optionsNo
questionYes
max_wait_minutesNo
answer_within_minutesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations only declare the generic write/openWorld/idempotent profile; the description goes far beyond by disclosing the credit debit, tiered pricing by deadline, and the expiry/refund behaviour ('full price returns to your credit'). It also states the immediate return payload (id, price, due time), which is exactly the kind of operational context annotations cannot carry.

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?

Front-loads the core action and return contract, then uses a clean per-parameter structure. It is dense but nearly every line carries actionable information; the pricing tier list is verbose but justified because cost is a decision input for the caller.

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

Completeness4/5

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

An output schema exists so return values need not be re-explained, yet the description still summarises the immediate payload usefully. The only real gap is the undocumented max_wait_minutes parameter, so completeness is high but not total.

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?

With 0% schema coverage the description must compensate, and it documents question, options (2-10 choices plus comment), context, api_key, and answer_within_minutes with its 15-10080 range and pricing. However max_wait_minutes is left entirely undocumented, and the description labels answer_within_minutes as required while the schema marks only question as required, a minor mismatch.

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?

States a specific verb and resource ('Ask a real human a short question') and immediately distinguishes itself from its follow-up sibling by naming get_human_answer for collecting the result. An agent can tell what this does and how it differs from get_human_answer without opening any schema.

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?

Gives clear context for use, describing the kinds of questions suited to it (taste, judgement, approval, sanity check) and stating the answer must be collectable via get_human_answer. It does not explicitly contrast with siblings like rent_a_human, so it stops short of the full when-not guidance.

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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