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TunnelMind Data API

by ai.tunnelmind

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Close the loop: after you acted on a TunnelMind verdict, tell us how it went. Reports aggregate per node into an advisory second opinion that any caller can read back via GET /v1/feedback/{node}.

Advisory only. In v0 a negative aggregate does NOT silently lower the fused trust score — it's a human-weighable signal beside the verdict, not an automatic reweight.

Use this tool when:

  • You acted on a verdict and want to record the real-world outcome (honored, defrauded, no issue) to help future callers.

Inputs:

  • node (body, required): the subject — ip, domain, asn, or entity slug.

  • outcome (body, required): one of positive, negative, neutral.

  • receipt_id (body, optional): the verdict receipt this outcome refers to.

  • note (body, optional): free-text context, max 500 chars.

Returns the updated advisory aggregate { node, counts, total, score, signal }.

Cost:

  • Free. Requires an API key (authenticated callers only).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeYes
noteNo
outcomeYes
receipt_idNo

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: aggregates per node, returns a read-back endpoint, is advisory only, is free, and requires an API key. It explicitly states what the tool does NOT do (silently lower the fused trust score), which is valuable and not contradicted by any structured data.

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?

Well-organized with clear sections for usage, inputs, return value, and cost. Every sentence provides necessary information without fluff. The description is 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?

Given the lack of annotations and output schema, the description is exceptionally complete. It covers purpose, usage, inputs, return shape, cost, and authentication, leaving no significant gaps for an agent to infer.

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?

Despite 0% schema description coverage, the description meaningfully explains each parameter: node is the subject, outcome is an enum, receipt_id is an optional verdict reference, and note is free-text with a max length. This adds semantics beyond the raw schema.

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 function: recording real-world outcomes after acting on a TunnelMind verdict. It uses a specific verb ('Close the loop', 'tell us how it went') and distinguishes itself from the read-only sibling get_feedback by explaining it aggregates feedback into an advisory opinion.

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 an explicit 'Use this tool when' section with a concrete scenario. It also clarifies a key non-use condition: the feedback is advisory only and does not automatically reweight the fused trust score, preventing misuse.

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