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TunnelMind Data API

by ai.tunnelmind
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get_entity

Returns an entity record for a surveillance company or data broker, including its industry, estimated annual data value per user (in USD), categories of personal data collected, and the full list of domains it controls. Free tier returns 5 domains, paid returns up to 200.

Use this tool when:

  • You want to understand what corporate entity owns or controls a tracker domain.

  • You need to assess the total surveillance footprint of a company (e.g., Alphabet, Meta, Oracle).

  • You are building a corporate surveillance graph and need domain-to-entity mapping.

Do NOT use this tool when:

  • You have a domain and need its category — use get_domain instead.

  • You want to browse entities by industry — use list_entities instead.

  • You are searching for an entity by name — use search instead.

Inputs:

  • slug (path, required): URL-safe entity identifier (lowercase, hyphens). Examples: alphabet, meta, oracle-data-cloud, the-trade-desk.

Returns:

  • Full EntityRecord with data categories, estimated data cost, and associated domains.

  • domains: array of top-scoring domains (5 for free tier, 200 for paid).

  • Pro/enterprise additionally return website and description fields.

Cost:

  • Anonymous (no key): 10/min, 100/day. Free key: 50/day. Pro/enterprise: included in plan.

Latency:

  • Typical: <150ms, p99: <400ms.

Input Schema

NameRequiredDescriptionDefault
slugYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does so well: it discloses tier-gated behavior (5 vs 200 domains, extra website/description fields for Pro/enterprise), rate limits (10/min, 100/day anonymous; 50/day free), and latency (<150ms, p99 <400ms). It stops short of describing error behavior or what happens with an unknown slug, leaving a minor gap.

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?

Front-loaded with the return summary, then organized into clear Inputs/Returns/Cost/Latency sections that are easy to scan. Every block adds distinct information and nothing is redundant 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?

With no output schema and no annotations, the description fully compensates by enumerating return fields, tier gating, cost, and latency. An agent has everything needed to select and invoke this single-parameter 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 coverage is 0%, so the description must compensate, and it adds real meaning: it explains that slug is a URL-safe entity identifier in lowercase with hyphens and supplies four concrete examples. The schema itself only carries an example and pattern, so the prose adds substantive clarification beyond the raw field definition.

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?

States a specific verb and resource ('Returns an entity record for a surveillance company or data broker') and enumerates the returned fields (industry, estimated data value per user, data categories, domains). It clearly targets a distinct resource from siblings like get_domain or list_entities, so an agent can differentiate without opening the schema.

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

Usage Guidelines5/5

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

Provides explicit 'Use this tool when' and 'Do NOT use this tool when' sections, each naming the alternative tool (get_domain, list_entities, search) and the condition that selects it. This is textbook routing guidance with no inference required.

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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