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TunnelMind Data API

by ai.tunnelmind

profile_entity

Call this before routing traffic, bidding on inventory, or trusting a counterparty. It fuses ALL THREE TunnelMind lenses for one subject — Scry (attacker intelligence + threat feeds + open ports), Sigil (ad-supply-chain position + trust score + ATAP witness count), and Tracker (DDG/IAB catalog + prevalence + categories) — into a single confidence-scored profile plus a signed P38 receipt.

The cross_lens.hits field tells you if the same infrastructure appears in attack data AND supply-chain data — that's your highest-confidence signal, and the one no siloed competitor can give you. cross_lens.flags surfaces the actionable highlights (cross_lens_overlap:scry+sigil, in_threat_intel:..., high_prevalence_tracker, corroborated_by_N_lenses).

Confidence weighting: each lens contributes a base score; a 1.5× multiplier applies when ≥2 lenses corroborate the same subject; and the Scry contribution is weighted by the attestation tier of the sensors that observed it (silicon_root 1.0 → self_asserted 0.5). Bounded [0,1] and carried into the receipt.

Unlike cross_lens_verify (one node → one verdict) and cross_lens_lookup (one node → raw three-lens view), profile_entity takes the SUBJECT as any combination of ip / domain / entity and returns the richest fused detail for a pre-transaction decision. At least one of ip / domain / entity is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ipNoIPv4 or IPv6 address of the subject (Scry lens).
domainNoDomain of the subject (Sigil + Tracker lenses).
entityNoentity_slug of the subject (Sigil + Tracker lenses).

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It discloses the confidence scoring mechanism, the 1.5× corroboration multiplier, the Scry attestation tier weighting, bounded output [0,1], the cross_lens.hits field semantics, and the signed P38 receipt. This goes far beyond a typical description.

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 longer than average, but every sentence contributes meaningful content and is front-loaded with the most critical context. The structure flows well from invocation to details to alternatives, though it could be trimmed slightly without losing value.

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 there is no output schema and no annotations, the description fully compensates by explaining key return fields (cross_lens.hits, cross_lens.flags), confidence weighting, and examples of flag values. It also covers parameter constraints, making it complete for an AI agent to invoke confidently.

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 100% and each parameter has its own description, but the tool description adds meaning by mapping each parameter to the relevant lens (ip → Scry, domain → Sigil+Tracker, entity → Sigil+Tracker) and explaining the combination flexibility. This exceeds schema-only semantics.

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 clear invocation context ('before routing traffic, bidding on inventory, or trusting a counterparty') and explicitly states it fuses all three lenses (Scry, Sigil, Tracker) into a confidence-scored profile. It distinguishes itself from siblings by naming cross_lens_verify and cross_lens_lookup and contrasting its richer subject handling.

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 when-to-use guidance ('before routing traffic...') and explicit exclusion/alternatives ('Unlike cross_lens_verify... and cross_lens_lookup...'). Also states the input requirement of at least one of ip/domain/entity.

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