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TunnelMind Data API

by ai.tunnelmind

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:

  • Free tier: included in 50 req/day limit. Pro/enterprise: included in plan.

Latency:

  • Typical: <150ms, p99: <400ms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and excels: it discloses tier-based output limits (5 vs 200 domains), cost context (free tier vs Pro/enterprise), return fields, additional fields for higher tiers, and latency expectations (<150ms typical, <400ms p99). This goes well beyond a simple read-only statement.

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 well-structured with clear sections (Returns, Use when, Do NOT use, Inputs, Cost, Latency), but the returns information is duplicated: the opening paragraph lists fields and domain counts, then a later 'Returns' section repeats them. This small redundancy prevents a perfect score.

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 simple one-parameter lookup with no output schema, the description is highly complete. It covers purpose, alternatives, input semantics, tier behavior, cost, latency, and return content, leaving no significant gap in understanding.

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

Parameters5/5

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

Schema description coverage is 0%, so the description compensates fully for the single parameter slug. It explains that it is a URL-safe entity identifier (lowercase, hyphens) and provides concrete examples like alphabet, meta, and oracle-data-cloud, adding meaning beyond the schema's pattern.

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 opens with a specific verb and resource: 'Returns an entity record for a surveillance company or data broker,' listing the exact fields included. It clearly distinguishes itself from siblings like get_domain, list_entities, and search via explicit 'Do NOT use' guidance.

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?

The description provides explicit 'Use this tool when' conditions and equally explicit 'Do NOT use this tool when' exclusions that name alternative tools (get_domain, list_entities, search). This gives the agent clear decision criteria for tool selection.

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

B3.3/5.0
Disambiguation2/5

Many tools overlap in purpose, such as cross_lens_verify, cross_lens_lookup, profile_entity, and preflight_should_i_act, which all return node verdicts with subtle differences. Sigil verification tools and receipt-related tools also have similar names and require deep reading to distinguish.

Naming Consistency3/5

The tool names are mostly readable, but the pattern is mixed: some use verb_noun (get_domain, create_subscription) while others use domain prefixes (sigil_*, ghostroute_*, intel_*). Within each domain, naming is consistent, but the overall style lacks uniformity.

Tool Count1/5

With 90 tools, this server is extremely overloaded. Even for a multi-purpose data API, the sheer number overwhelms and makes navigation difficult, far exceeding the typical well-scoped MCP server. The count is an extreme mismatch for the apparent scope.

Completeness4/5

The tool surface is very comprehensive, covering tracker lookup, cross-lens verification, receipts, compliance, subscriptions, tasks, intel probes, and more. Minor gaps exist, such as no batch cross-lens verification, but core workflows are well covered.

Resources