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Get human answers

get_answers
Read-only

Human answers (any language, CC0) to one question, or a feed of all answers since a timestamp. Each answer is signed by the person's passkey: it carries proof (the WebAuthn assertion over the answer text and the passkey's public key) and log_index (its seal in the public log), so you can verify it without trusting humancollab.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of answers
sinceNoISO 8601; used when question_id is omitted
question_idNoQuestion id from list_questions; omit for the feed of all answers

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"Maximum number of answers"
    • addedInput schema / properties / question_id / description
      Added value: +"Question id from list_questions; omit for the feed of all answers"
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already mark this as a read-only closed-world operation, and the description adds substantial context beyond that: answers are passkey-signed, carry a WebAuthn proof and a log_index, and can be verified without trusting the server. It does not cover pagination/limit behavior or empty-result semantics, but the trust model is well disclosed.

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?

Two sentences, zero waste, and the primary scope and mode split are front-loaded before the verification detail. The longer second sentence is dense but every clause (proof, log_index, trust) 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?

With no output schema, the description usefully explains the key return fields (proof, log_index) and the verification guarantee. It does not explain the limit parameter or feed pagination, a minor gap for a read tool with full schema coverage.

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 coverage is 100%, with descriptions already explaining that 'since' applies when question_id is omitted and that question_id selects one question versus the feed. The description mirrors that mapping but adds no syntax or format detail beyond the schema, so baseline 3 applies.

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 ('Human answers') and clearly distinguishes its two modes: answers to one question or a feed since a timestamp. An agent can tell it apart from list_questions and the proof-related siblings 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?

The two usage modes are explicit: pass a question to get answers to it, or omit it and use 'since' to get a feed. No when-not-to-use condition or comparison to alternatives like get_inclusion_proof is given, but the operating context is clear.

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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