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find_social_profiles

Extract verified official social media profiles from a business website to support B2B lead qualification, filtering out share widgets and irrelevant external links.

Instructions

    Extract verified official social media profiles from a business website.

    Discovers:
    - LinkedIn (company / personal)
    - Facebook
    - Instagram
    - Twitter / X
    - YouTube
    - Pinterest

    Filters out share/dialog widgets, intent links, and irrelevant external domains.

    Security & Privacy:
    - Prevents SSRF attacks.
    - Respects robots.txt rules.
    - Only extracts publicly linked B2B / official brand profiles.

    Args:
        url: The business website URL (e.g. 'https://acme.example').

    Returns:
        Dict containing detected profiles by platform and total count.
    

Input Schema

NameRequiredDescriptionDefault
urlYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations supplied, the description carries the full behavioral burden and discloses meaningful traits: SSRF prevention, robots.txt compliance, filtering of share widgets and intent links, and scope limited to public B2B/official brand profiles. It stops short of covering rate limits, timeout behavior, or failure modes.

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?

The description is front-loaded with the core purpose and uses short bulleted lists for platforms and security behavior. The Args and Returns sections are somewhat redundant given the input schema and output schema, but the overall structure remains easy to scan.

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 one-parameter extraction tool, the description supplies purpose, platform coverage, filtering behavior, security constraints, and the URL argument example. An output schema exists, so return-value details need not be extensive, and the description is complete enough to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the single url parameter. It does so by defining url as the business website URL and giving an example like 'https://acme.example', adding useful meaning beyond the bare schema.

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 a specific verb and resource: extracting verified official social media profiles from a business website, with an explicit list of platforms. It distinguishes the task from generic website analysis by focusing on social profile discovery, though it does not explicitly contrast itself with sibling tools like analyze_website.

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?

Usage is implied by the stated goal of discovering social profiles on a business site. However, there is no explicit when-to-use guidance, no exclusions, and no routing to alternatives such as analyze_website or find_business_website.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.