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start_trust_signals_scan

Run an async 34-signal trust analysis on a domain to assess enterprise readiness, third-party validation, social proof, brand authority, and risk reversal. Returns scanId for polling.

Instructions

Start an async trust-signals analysis on any domain — 34 signals across enterprise readiness, third-party validation, social proof, brand authority, and risk reversal. That set is the trust-signals SCAN taxonomy and is a different thing from the homepage trust signals the monitored Tech & Trust dimension tracks (see get_tech_trust_dashboard), which are 26 signals in five different categories. Never quote a count from one as if it described the other. AND ONE CATEGORY NAME COLLIDES: socialProof is a field on both, spelled identically, and both have exactly five members — so neither the name nor the count reveals that they differ. Here the five are customer logos, hero-only logos, customer count, case studies and testimonials. In the monitored dimension they are customer logos, customer count claim, case studies, money-back guarantee and free trial. A socialProof of 4 from this scan and 3 from the dashboard is not a change and not a discrepancy; the two were never measuring the same set. If you hold both numbers, report them separately or not at all. Returns scanId immediately; poll with get_trust_signals_scan. Typical completion: 30-90 seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to scan, e.g. example.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv4.0.1
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Addedv1.2.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=false and openWorldHint=true; the description adds the substantive behavior the agent needs: the scan is async, returns scanId immediately, must be polled with a named sibling, and typically completes in 30-90 seconds. It also discloses the lookalike-data hazard (identical `socialProof` field name, identical member count, different members) that would otherwise cause a wrong conclusion.

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?

Front-loaded with the core action and return behavior, then spends much of its length on the taxonomy-collision warning. The warning is load-bearing, but it reiterates the point several times ('Never quote a count...', 'not a change and not a discrepancy', 'report them separately or not at all'), making it longer than needed.

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?

No output schema exists, and the description compensates by stating exactly what is returned (scanId), how to retrieve results, and how long completion takes. Combined with the taxonomy caveat, an agent has everything needed to launch and correctly interpret this scan.

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?

Only one parameter with 100% schema coverage, so the schema already documents `domain` and its example format. The description adds that it accepts 'any domain' and is the scan target, but no extra syntax or constraints beyond the schema — baseline 4 for a single fully-documented parameter.

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?

Opens with a specific verb+resource+scope: 'Start an async trust-signals analysis on any domain,' and quantifies the resource (34 signals across five named categories). It explicitly distinguishes itself from the monitored Tech & Trust dimension and names the sibling (get_tech_trust_dashboard) that covers that different set, so an agent can route correctly without opening schemas.

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?

Gives explicit when-to-use context, names the alternative tool that tracks a superficially similar set, and warns about when NOT to conflate the two outputs ('report them separately or not at all'). It also tells the agent to poll with `get_trust_signals_scan`, closing the async workflow loop.

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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