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thenavidm

ScrapeCreators MCP Server

by thenavidm

Get Age and Gender

creator_tools_get_age_and_gender

Analyze a creator's profile photo to estimate age and gender, returning age range, gender, and confidence score for social profiles.

Instructions

Uses AI to analyze a creator's profile photo and estimate their age and gender. Returns ageRange with low and high bounds, gender, and a confidence score for the gender prediction. The profile photo must contain a clear, visible face for accurate results. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to users social profile
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true), and the description adds genuinely useful context: paid credit consumption, the confirm=true gate, that it is a read-like POST that does not publish to platforms, and the accuracy precondition of a visible face. It goes beyond the structured fields, though it does not discuss failure behavior or credit cost magnitude.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four short sentences, each carrying distinct information (purpose, returns, accuracy precondition, cost/confirm constraint). Front-loaded with purpose and return shape; no filler.

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 steps in to name the return fields (ageRange bounds, gender, confidence score), and it covers cost and confirmation, so an agent has enough to call it correctly. Minor gaps remain around the account parameter and error/credit-cost specifics, but overall it is complete.

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 url, account, and confirm, giving a 3 baseline. The description reinforces that confirm must be true but adds little syntax or accepted-value detail beyond what the schema states.

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?

States a specific verb+resource: uses AI to analyze a creator's profile photo and estimate age and gender, and lists the returned fields. An agent can tell exactly what it does, though it does not explicitly name how it differs from adjacent demographic tools like tiktok_audience_demographics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives actionable prerequisites (requires confirm=true, photo needs a clear visible face, consumes credits), which is real usage guidance. However, it never says when to reach for this tool versus the many sibling profile/demographic tools, so the when-to-use layer is only implied.

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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