TunnelMind Data API

intel_stack

Fetches up to 32KB of the domain's HTML and response headers from the edge, then fingerprints the content for known CMS platforms, JavaScript frameworks, CDN providers, and analytics tools. Detection is based on meta generator tags, script src patterns, response headers, and cookie names.

Use this tool when:

You need to know what CMS (WordPress, Drupal, Shopify) a site runs.
You are assessing a domain's infrastructure before a security review.
You want to identify analytics or marketing tools a site embeds.

Do NOT use this tool when:

You want HTTP headers and security posture — use intel_http instead.
You want tracker database classification — use get_domain instead.
You need robots.txt AI policy — use intel_robots instead.

Inputs:

domain (query, required): Domain to fingerprint.

Returns:

cms: detected content management system, or null.
frameworks: JavaScript/backend frameworks detected.
cdn: CDN provider detected, or null.
analytics: analytics and tracking tools detected.
meta_generators: raw meta generator tag values.

Cost:

Free. No API key required.

Latency:

Typical: 2-4s (HTML fetch), p99: 7s.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	When true, return a task handle immediately instead of blocking. Poll get_task for the result.
`domain`	Yes
`receipt`	No	When true, attach a signed Receipt v1.0 committed to the transparency log. Additive — a signing failure never costs you the observation (ADR-014).

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses the 32KB limit, detection signals (meta tags, script src, headers, cookies), cost (free, no API key), and latency (typical/p99). It does not explicitly state read-only/non-destructive, but the fetching and fingerprinting behavior implies it. This is strong disclosure for a read-like tool.

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: main purpose, usage conditions, input list, return fields, cost, and latency. Every section adds essential information without unnecessary verbosity. Front-loaded with the core purpose.

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?

The description covers the tool's purpose, inputs, return values, cost, latency, and exclusions. It even details the detection methods. Given no output schema, the 'Returns' section is especially valuable for setting expectations. This is a comprehensively specified tool description.

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 67%, leaving 'domain' without a schema description. The description compensates by listing 'domain (query, required): Domain to fingerprint.' It does not describe async or receipt beyond what the schema already provides, but the key required parameter gets semantic clarity.

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 explicitly states the tool fetches HTML and response headers then fingerprints for CMS, frameworks, CDN, and analytics. It uses a specific verb ('fetches', 'fingerprints') and names the resource (domain's HTML/headers), clearly distinguishing its role from sibling tools like intel_http.

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, naming specific alternatives (intel_http, get_domain, intel_robots). This gives clear decision 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

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