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TunnelMind Data API

by ai.tunnelmind

list_entities

Returns a paginated list of corporate entities in the TunnelMind surveillance database. Includes data categories, estimated data value, and industry classification. Useful for enumerating the surveillance ecosystem by sector.

Use this tool when:

  • You want to enumerate all entities in a specific industry (e.g., all ad-tech companies).

  • You need a dataset of surveillance entities for analysis or reporting.

  • You are building a comprehensive surveillance landscape map.

Do NOT use this tool when:

  • You need the full profile of a specific entity — use get_entity instead.

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

  • You need domain-level data — use list_domains instead.

Inputs:

  • industry (query, optional): Filter by industry classification. Examples: ad_tech, analytics, data_broker, social, crm.

  • limit (query, optional): Results per page. Max 100 (paid), 20 (free). Default 50.

  • cursor (query, optional): Pagination cursor from previous response's next_cursor.

Returns:

  • Array of entity list items (slug, name, parent_company, industry, data_categories, data_cost_usd).

  • meta.has_more and meta.next_cursor for pagination.

Cost:

  • Free tier: up to 20 results/page, 50 req/day. Pro/enterprise: up to 100 results/page.

Latency:

  • Typical: <150ms, p99: <400ms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNo
industryNo

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and excels: it discloses pagination behavior (next_cursor, has_more), rate limits (free tier limits, request per day), latency expectations, and return fields. This is rich behavioral context beyond a simple read-only hint.

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?

The description is well-structured with clear sections (purpose, when to use/not use, inputs, returns, cost, latency). Each sentence adds value, and the formatting makes it easy for an agent to parse quickly without unnecessary fluff.

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?

Given no annotations or output schema, the description independently covers purpose, usage guidance, parameter details, return format, pagination, cost, and latency. It is complete for an agent to know exactly what this tool does and when to invoke it.

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%, but the description fully explains all three parameters: `industry` with examples, `limit` with tier-dependent max/default, and `cursor` with source from previous response. This compensates completely for the schema's lack of descriptions.

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 a paginated list of corporate entities in the TunnelMind surveillance database.' It clearly states what data is included and explicitly differentiates from siblings by naming `get_entity`, `search`, and `list_domains` in the 'Do NOT use' section.

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' and 'Do NOT use this tool when' sections, listing concrete scenarios and naming the alternative tools for each exclusion case. This gives clear, actionable guidance 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