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OriginalVoices MCP Server

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by ovlabs

ask_twins

Ask questions to AI twins representing a specific target audience to gather research insights. Define the audience demographics and submit open-ended questions.

Instructions

Ask questions to a specific audience using Original Voices AI twins. Use this for ad-hoc audience queries with a free-text audience description.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audienceYesThe target audience to query. Typically includes demographics such as age range, gender, and location. Examples: 'Women aged 35-55 in the US and UK', 'Gen Z men (18-25) in urban areas'.
questionsYesThe questions to ask the Digital Twins. Open-ended questions work best.
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions using 'Original Voices AI twins' but does not explain the result format, response time, or any side effects. For a tool that returns results (no output schema), more behavioral detail is needed.

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, front-loaded with the primary purpose, and contains no redundant information. Every word contributes to understanding the tool's function.

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

Completeness3/5

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

For a tool with two simple parameters and no output schema, the description explains what the tool does but omits details about what the response contains or how to interpret results. It is minimally adequate but lacks completeness for an agent to fully understand usage.

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?

The input schema already describes both parameters (audience and questions) with clear examples. The description adds a mention of 'free-text audience description' but does not significantly enhance understanding beyond the schema. With 100% schema coverage, a baseline of 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 the tool's action ('Ask questions to a specific audience using Original Voices AI twins') and specifies it is for 'ad-hoc audience queries with a free-text audience description,' distinguishing it from sibling tools like ask_audience and ask_project which likely target different use cases.

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 says 'Use this for ad-hoc audience queries with a free-text audience description,' providing clear context for when to use this tool. However, it does not explicitly mention when not to use it or compare to siblings, which would earn a higher score.

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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