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ask_human

Present content to a human and ask them to choose between options. Use this for subjective judgment, approval, preference, or tie-breaks. Avoid using it for deterministic checks or reversible low-stakes choices. The tool waits briefly for a result, then returns pending if the human has not responded yet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesShort title for the request (max 200 chars)
run_idNoOptional workflow run identifier
choicesYes2-4 choices for the human to pick from
contentNoContent for the human to review (text, markdown, HTML, or image URL). Max 50KB.
metadataNoOptional metadata passed through to webhook
trace_idNoOptional trace identifier
agent_nameYesYour agent/tool name (max 100 chars)
confidenceNoAgent confidence between 0 and 1
callback_urlNoOptional HTTPS webhook URL for async notification
content_typeNoHow to render the content. Default: text
decision_reasonNoWhy the agent is escalating this to a human (max 500 chars)
timeout_secondsNoRequest expiry in seconds (default 3600, max 86400)
recommended_optionNoOptional choice id the agent currently recommends
consequence_of_wrong_choiceNoWhy a wrong choice matters (max 500 chars)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose a key behavior: 'The tool waits briefly for a result, then returns pending if the human has not responded yet.' This adds valuable context about asynchronous behavior. However, it does not explain how to retrieve the eventual result or what happens on timeout, leaving some gaps.

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 only two sentences, highly concise, and front-loaded with the main purpose. It then efficiently covers usage guidance and key behavior without wasted words.

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?

Given the tool's complexity (14 parameters, nested objects, no output schema), the description gives a solid high-level overview. It covers the primary action, use cases, and the pending behavior. However, it lacks details on return values or how to handle pending results, which would be more helpful given the absence of an output schema.

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 parameters clearly. The description adds general context about choosing between options but does not elaborate on individual parameter meanings beyond what the schema provides. This matches the baseline for high schema coverage.

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 the tool's purpose: 'Present content to a human and ask them to choose between options.' This is a specific verb+resource definition and distinguishes it from sibling tools like get_service_status and provision_api_key, which are not human-in-the-loop decision tools.

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?

The description explicitly provides when-to-use guidance: 'Use this for subjective judgment, approval, preference, or tie-breaks.' It also gives a clear when-not-to-use: 'Avoid using it for deterministic checks or reversible low-stakes choices.' However, it does not name alternative tools or specific alternatives, so it falls just short of a 5.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: ask_human for human interaction, get_service_status for service availability, and provision_api_key for key creation. No overlapping functionality.

Naming Consistency5/5

All tools use a consistent verb_noun pattern in snake_case: ask_human, get_service_status, provision_api_key. The naming is clear and predictable.

Tool Count4/5

With 3 tools, the server is minimal but covers the core workflow of human interaction. However, additional tools for managing API keys or retrieving ask results would be beneficial.

Completeness2/5

The tool set lacks essential features: no way to retrieve a pending ask result, no key management (list/revoke), and no tool to cancel or update an ask. Agents will likely fail to complete workflows involving delayed human responses.