TunnelMind Data API

list_domains

Returns a paginated list of domains from the tracker database. Results are ordered alphabetically by domain name and support cursor-based pagination for full traversal. Filtering by category and minimum score allows targeted data extraction.

Use this tool when:

You want to enumerate all known ad-tech or analytics domains above a risk threshold.
You need a dataset of tracker domains for offline analysis.
You are paginating through a category to build a block list.

Do NOT use this tool when:

You need data for a specific domain — use get_domain instead.
You are searching by keyword — use search instead.
You want domains belonging to a specific company — use get_entity instead.

Inputs:

category (query, optional): Filter by surveillance category. One of: ad_tech, analytics, social, fingerprinting, content, cdn, other.
min_score (query, optional): Integer 0-100. Exclude domains scoring below this value.
limit (query, optional): Number of results per page. Max 100 (paid), 20 (free). Default 50.
cursor (query, optional): Pagination cursor from the previous response's next_cursor field.

Returns:

Array of domain list items (domain, category, score, prevalence, entity summary).
meta.has_more: true if more pages exist.
meta.next_cursor: pass as cursor to get the next page.
meta.count: number of results in this page.

Cost:

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

Latency:

Typical: <200ms, p99: <500ms.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`cursor`	No
`category`	No
`min_score`	No

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations provided, the description fully discloses behavior: pagination mechanism (cursor-based), ordering, filtering semantics, return structure including meta fields, cost/free-tier limits, and latency expectations. No hidden side effects or prerequisites are omitted.

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-organized with clear sections (overview, when to use, inputs, returns, cost, latency). It is slightly longer than necessary but every sentence adds value, and the structure aids comprehension. A minor trim could make it more concise, but it earns a strong 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?

Given 4 parameters, no output schema, and no annotations, the description covers all necessary context: usage scenarios, parameter details, return format, pagination, rate limits, and performance. The agent has everything needed to call the tool correctly.

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 explains every parameter in detail: category enum values, min_score semantics (exclude below), limit max/default, and cursor usage. This fully compensates 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 domains from the tracker database.' It clearly states ordering and filtering capabilities, and distinguishes from siblings by explicitly naming alternatives like get_domain, search, and get_entity 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?

Provides explicit 'Use this tool when' and 'Do NOT use this tool when' lists with concrete scenarios and named alternatives. This gives the agent clear decision criteria for selection versus other tools.

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

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