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match_voice

Extract a creator's voice DNA from reference samples and rewrite a draft in their style. Requires at least 3 reference samples (video URLs or text). Returns voice profile (energy, humor, vocabulary, signature phrases), reusable prompt instructions, and the rewritten draft. Use when the user wants to write in another creator's style or match a specific voice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draftYesThe text to rewrite
reference_samplesYesAt least 3 reference samples — TikTok/YouTube/Instagram URLs or raw text

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
from_paygNoWhether credits came from pay-as-you-go balance
voice_profileNoExtracted voice DNA profile
rewritten_draftNoThe input draft rewritten in the matched voice
credits_remainingNoCredits remaining after this call
from_subscriptionNoWhether credits came from subscription
prompt_instructionsNoReusable prompt to replicate this voice

TDQS

A4.1/5.0
Behavior4/5

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

Annotations are minimal (readOnlyHint=false, no value for destructive or idempotent), and the description adds key behavioral constraints: a minimum of 3 reference samples and the fact that it returns a voice profile, reusable prompts, and the rewritten draft. It doesn't explain side effects or permissions, but the operation is a text transformation, so details are sufficient.

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 two sentences with no wandering. It front-loads the core purpose, then quickly lists requirements and output. Every clause adds information; no redundancy or filler.

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

Completeness5/5

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

For a moderately complex tool (two inputs, output schema present), the description covers the goal, the required inputs, the output structure, and the typical use-case. With an output schema provided, the description doesn't need to explain the return values, and it still manages to state what the result includes. Nothing critical is missing.

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%, and the description adds no extra meaning beyond what the schema provides (minItems on reference_samples, descriptions of draft and reference_samples). It touches on the requirement of 'at least 3 reference samples' but that's already encoded. Baseline 3 applies.

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?

The description clearly states the tool's function: extract a creator's voice DNA and rewrite a draft in their style. It provides a specific verb+resource and so distinct outcome that it will be useful to an agent. While it doesn't explicitly name alternatives or siblings, its specificity (voice DNA, style matching) distinguishes it from casual rewriting 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?

Explicit 'Use when the user wants to write in another creator's style or match a specific voice' gives clear direction. However, it doesn't mention when not to use it or point to an alternative (e.g., viral_remix or analyze_account), so it offers a clear context but no exclusions.

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
Disambiguation4/5

Each tool targets a distinct purpose: analysis, trend research, scoring, creation, and tracking. The lifecycle tools (analyze_account, watch_account, get_changes) are related but clearly delineated by baseline creation vs. comparison. score_hook and predict_virality could be mildly confused since both score content, though descriptions clarify hook-level vs. full-draft scope.

Naming Consistency4/5

Nine of twelve tools follow the verb_noun pattern (analyze_account, find_viral_template, get_changes, list_watches, match_voice, predict_virality, score_hook, search_videos, watch_account). Three deviate: brief_to_blueprint, trend_pulse, and viral_remix use prepositional or noun-based naming, creating minor inconsistency but not confusion.

Tool Count5/5

Twelve tools for a short-form content intelligence platform feels well-scoped. The surface covers research (4 tools), creation (3), validation (2), and tracking (3), with each tool earning its place. This sits comfortably within the ideal 3-15 range for a domain-specific server.

Completeness3/5

The research, creation, and validation workflows are well-covered, but the tracking lifecycle has a notable gap: watch_account and list_watches exist with no unwatch/remove_watch tool, and get_changes implies persistence without a way to manage it. Also missing is a single-video deep-dive tool (analyze_account only covers videos within full account analysis) and direct creator-to-creator comparison.