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Ask a Human (Tech & Toast)

Ask the human a question

ask_human

Submit a question. Returns a ticket. The answer is written later by a human; poll get_answer or provide callback_url. Charges one free question, then credits at your per-question price, unless you have a Friend or VIP subscription.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYes
contextNoWhy you are asking; what you already know
questionYes
callback_urlNoWe POST the answer here when ready

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations only give the generic safety profile (openWorld, non-idempotent, non-destructive); the description adds genuinely valuable context: the answer arrives asynchronously from a human, and there is a billing model (one free question, then per-question price, waived for Friend/VIP). It stops short of covering failure modes or timing expectations, which keeps it from a 5.

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?

Three short sentences, front-loaded with the action and return value, then the retrieval mechanism, then cost. Every clause carries information; the billing caveat is dense but earns its place.

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?

No output schema exists, so the description's 'Returns a ticket' plus the polling/callback instruction covers the return flow, and billing is disclosed. A first-time agent has enough to invoke it correctly, though it lacks any note on answer latency or what happens if a ticket goes unanswered.

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 50%, and the description reinforces the callback_url purpose and implies that asking consumes credits (relevant to the api_key). However, the 'context' parameter is never mentioned in the description, leaving half of the optional parameters reliant on the schema alone.

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?

States a specific verb and resource ('Submit a question. Returns a ticket.'), and the async nature distinguishes it from the retrieval sibling. It does not directly say how it differs from nearby tools like check_human_status or about_this_human, so it lands just short of full differentiation.

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 names the follow-up paths ('poll get_answer or provide callback_url'), which is useful for the async workflow, but it never states when to choose ask_human over sibling tools or what kinds of questions it is appropriate for. Usage is implied rather than stated.

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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