Skip to main content
Glama

TunnelMind Data API

signal_dark_pool_risk

Reconciles every sell path a publisher declares (sells_through) against each SSP's own sellers.json (exchange_seat) and keeps three classes strictly separate: corroborated (seat present), contradicted (SSP crawled but seller_id absent — real risk), and unchecked (SSP not yet crawled — excluded from risk, lowers confidence). Combined with publisher-side ads.txt opacity. Two-sided corroboration is the cross-lens moat — it catches unauthorized resale a one-sided ads.txt read cannot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesPublisher hostname.

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It discloses meaningful behavioral detail: how the three classes are derived, that 'unchecked' items are excluded from risk and lower confidence, and that this is a cross-lens verification. It does not mention auth/rate limits or output format, but the core analytical behavior is transparent.

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?

Three dense sentences effectively front-load the main action and key distinctions. The only minor waste is the 'moat' metaphor; otherwise, every sentence earns its place. Could be slightly tighter or use structured lists, but it is well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool's complex risk classification logic is well explained, including the role of each class and confidence impact. However, with no output schema, the description does not explicitly state the response shape/fields, though the semantics are clear enough for an agent to anticipate a structured risk assessment.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter `domain` is already fully described in the schema as 'Publisher hostname', and schema coverage is 100%. The description adds no additional parameter-level detail (e.g., format or examples), so baseline 3 is appropriate.

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 uses a specific verb ('reconciles') and clearly identifies the resource (publisher's `sells_through` declarations against SSP `sellers.json`). It is unambiguous about what the tool does and the three output classes, which distinguishes it from sibling signal/risk tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description strongly implies the tool is for detecting unauthorized resale via two-sided corroboration and explicitly contrasts this with 'one-sided ads.txt read'. It does not name a specific sibling alternative, but provides enough context to know when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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