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TunnelMind Data API

by ai.tunnelmind

intel_inject

Fetches a domain's homepage and checks for content patterns that could constitute prompt injection attacks against AI agents that visit and ingest the page. Signals include hidden text, invisible divs, <!-- AI: ignore --> style comments, and known injection patterns.

Use this tool when:

  • You are vetting a domain before feeding its content into an LLM context.

  • You want to assess the prompt injection risk of a URL before browsing it with an agent.

  • You are auditing a set of domains for adversarial AI content.

Do NOT use this tool when:

  • You want tracker surveillance data — use get_domain instead.

  • You want AI training opt-out signals — use intel_optout instead.

  • You want the agent surface (MCP/OpenAPI) — use intel_agent instead.

Inputs:

  • domain (query, required): Domain to scan.

Returns:

  • injection_signals: list of signal types detected (e.g., hidden_text, ai_instruction_comment, invisible_div).

  • risk_level: none, low, medium, or high based on signal count and type.

Cost:

  • Free. No API key required.

Latency:

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoWhen true, return a task handle immediately instead of blocking. Poll get_task for the result.
domainYes
receiptNoWhen true, attach a signed Receipt v1.0 committed to the transparency log. Additive — a signing failure never costs you the observation (ADR-014).

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It covers the operation (fetching homepage, scanning for patterns), return values (injection_signals, risk_level), cost (free, no API key), and latency (2-4s typical, p99 7s). It also explains the risk level scale. This is comprehensive and transparent.

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, Use when, Do NOT use when, Inputs, Returns, Cost, Latency). It is front-loaded with the primary purpose and every sentence adds value without fluff. Despite being moderately long, it remains highly readable and organized.

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 output schema, the description fully explains return values (injection_signals, risk_level) and the risk scale. It covers cost, latency, use cases, and exclusions. It also explicitly states the scope (domain's homepage) and does not overreach. For a tool with this complexity, the description is complete and leaves no significant gaps.

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?

The schema covers async and receipt with descriptions, but domain lacks a description. The tool description adds 'Domain to scan' for domain and provides overall context for using the tool. It doesn't elaborate on the optional parameters, but the schema does, so the description adds meaningful value to the required parameter and compensates for the missing schema description.

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 clearly states the tool's purpose with a specific verb and resource: 'Fetches a domain's homepage and checks for content patterns that could constitute prompt injection attacks against AI agents.' It lists concrete signals like hidden text, invisible divs, and AI-ignore comments, and explicitly distinguishes from sibling tools by naming alternatives for related but different tasks (tracker surveillance, opt-out signals, agent surface).

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 alternative tools (get_domain, intel_optout, intel_agent). This makes the decision boundary crystal clear for an agent.

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