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predict_virality

Idempotent

Score a draft script for viral potential with adversarial verification. Returns a virality score, recommendation (ship/rework/no-go), viral DNA breakdown with evidence, attack vectors analysis, and calibration metrics. Use when the user has a finished draft and wants pre-publish verification. Pass either a script string or a video URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nicheNoNiche context
scriptYesThe draft script to predict on (or a video URL — auto-extracts transcript)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
blueprintNoRetention diagnosis with weak seconds identified
from_paygNoWhether credits came from pay-as-you-go balance
viral_dnaNoHook type, structure, emotional triggers breakdown
share_reasonNoWhy viewers would share this content
target_emotionNoPrimary emotion the content targets
virality_scoreNoOverall virality prediction (0-100)
credits_remainingNoCredits remaining after this call
from_subscriptionNoWhether credits came from subscription

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide idempotentHint=true and destructiveHint=false, covering safety. The description adds behavioral context: 'adversarial verification' implies a rigorous analysis, and the list of outputs (attack vectors, calibration metrics) gives insight into the tool's processing. It also notes video URL auto-transcription, an external-access behavior, which is not in annotations. No contradiction with annotations.

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 concise sentences with zero fluff. The first sentence states the action and outputs; the second gives usage condition and input options. The critical information is front-loaded, and every clause earns its place.

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?

This is a complex tool with multiple output components, but the description enumerates all of them. It clarifies the input alternatives (script or URL) and the intended usage scenario. Given that an output schema exists, the description does not need to explain return values, and it covers the essential aspects an agent needs to invoke it correctly, distinguishing it from sibling tools.

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 both parameters. The description adds that the script should be a 'finished draft' for pre-publish verification, which is useful context, but it largely repeats the schema's note about accepting a video URL. No significant new meaning beyond the schema.

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 states a specific verb ('score') and a specific resource ('a draft script'), and lists unique outputs (virality score, recommendation, viral DNA breakdown, attack vectors, calibration metrics) that distinguish it from siblings like score_hook and find_viral_template. The phrase 'adversarial verification' further differentiates its approach.

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 gives a clear usage context: 'Use when the user has a finished draft and wants pre-publish verification.' It also specifies input format ('Pass either a script string or a video URL'). However, it does not explicitly mention when not to use it or alternatives, though siblings exist.

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.