Adaga solutions — Data APIs Toolbox

ai_brand_visibility_tracker

Measure whether a selected AI model mentions a brand and cites its official domain in real answers. Use when you need repeatable, answer-level evidence with persistent history and deltas for the same brand, domain, queries and model. Returns mentioned, cited, citationCount, model-provided URLs, the complete answer and an accuracy warning. Use aeo_ai_visibility_checker instead for technical website readiness, or local_presence_monitor for Google reviews and local-business signals. Results are specific to the selected model, prompts and run date; they are not a ranking guarantee or verified commercial truth. Auth: Apify API token required in Authorization: Bearer; a separate OpenRouter apiKey is required for real measurements. Rate limits: 60 calls/min/IP on this facade; no pagination. Pricing: $0.05 per run + $0.05 per measured answer + $0.10 per official-domain citation, plus any OpenRouter provider costs. dryRun returns a technical sample without provider calls or event charges.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brand": "<brand>",
      "domain": "<domain>"
    },
    "name": "ai_brand_visibility_tracker"
  }
}

Input Schema

Name	Required	Description	Default
`brand`	Yes	Exact brand or company name to find in the answer text.	Adaga solutions
`model`	No	Model used for every query. Choose a fixed model for strictly comparable history; openrouter/free selects an available free model.	openrouter/free
`apiKey`	No	Secret key required only for real measurements. Leave empty only when dryRun is true.
`domain`	Yes	Official domain without protocol, for example adagasolutions.lu. Subdomains count as official citations.	adagasolutions.lu
`dryRun`	No	Return a clearly marked technical sample without calling an AI provider or charging events. This is not a visibility result.
`queries`	No	One to ten stable prompts used for the visibility measurement.
`maxTokens`	No	Upper bound per answer. Reasoning models may need at least 1,000 tokens.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 2, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With zero annotations, the description carries the full burden and does: dual auth requirements (Apify token plus a separate OpenRouter apiKey for real measurements), rate limit of 60 calls/min/IP, no pagination, itemized pricing including per-answer and per-citation charges, dryRun semantics (no provider calls, no event charges), and a caveat that results are model/prompt/date-specific and not a ranking guarantee.

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?

Dense but well ordered: purpose, usage, returns, alternatives, caveats, then operational facts (auth, limits, pricing, dryRun). Every sentence carries weight for a paid, credentialed tool, though the apiKey/dryRun constraints are stated twice across description and schema.

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?

For a 7-parameter, credentialed, paid tool with no output schema, the description enumerates the return fields (mentioned, cited, citationCount, model URLs, full answer, accuracy warning) and covers cost, limits, and failure/edge semantics. Nothing an agent needs before calling is missing.

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?

Schema description coverage is 100%, so the baseline is 3; the description mostly restates what the schema already documents (apiKey only for real measurements, dryRun returns a sample). The only genuine addition is the comparability rationale for pinning a fixed model across runs, which is marginal against an already-annotated 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?

States a specific verb and resource: measure whether a selected AI model mentions a brand and cites its official domain in real answers. It also carves out its scope against two named siblings (aeo_ai_visibility_checker for technical readiness, local_presence_monitor for local signals), so an agent can distinguish it without opening a schema.

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?

Explicitly says when to use it (repeatable, answer-level evidence with persistent history and deltas for a stable brand/domain/queries/model set) and when not to (two named alternatives with their domains of use). It additionally routes the agent to dryRun for a no-cost technical sample, covering the cautious-caller case.

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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