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backlinkmcp-server

by io.github.johnsmalls22-rgb
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AI visibility report (Pro, spends credits)

ai_visibility_report

AI Visibility / GEO audit. Asks a panel of AI models a buyer-intent question ('what's the best ?') and reports whether YOUR brand gets recommended, who the models prefer instead (share-of-voice across the panel), WHY the leader wins — diagnosed against BacklinkMCP's own authority/link data (referring-domain gap) — and which domains to earn links from to catch up. Two modes: parametric (default — the models' baked-in brand memory) or retrieval (live web-search models = what the AI answers right now, SEO-driven). Pro plan only (runs a live multi-model AI panel): 8 models × 3 prompts × up to 4 competitors per report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo'parametric' (default) = the models' baked-in brand memory (cheap). 'retrieval' = live web-search models (what the AI answers now, SEO-driven; costs more credits).
depthNo'draft' = cheap iteration pass (2 models × 1 prompt) for trying prompts/competitors. 'full' (default) = the whole panel. Draft costs 1 credit (2 for retrieval).
factsNoOptional ground-truth facts about YOUR product (max 5), e.g. ['pricing starts at $29/mo','has a free tier']. When supplied, the panel is also asked what it knows about your brand and each fact is verified — separating 'AI doesn't know you' from 'AI knows you but gets it wrong' (opposite fixes).
domainYesYOUR root domain — the brand you're measuring AI visibility for, e.g. 'yoursite.com'.
historyNotrue = return the permanent archive of past runs for this domain (mention rate, rank, leader over time) instead of running a new panel. Costs no credits. Optionally combine with `mode` to filter parametric vs retrieval history.
promptsNoBuyer-intent queries to ask the panel, e.g. ['best project management tool for a startup'].
samplesNoRepeat draws per (prompt×model) cell (default 1, max 10). Answer engines are nondeterministic — more samples tighten the 95% confidence interval on your rates. Use samples>=5 for a defensible inclusion rate; 1 is a smoke test, not a measurement. Credits multiply by samples.
categoryNoOptional shortcut used only if `prompts` is omitted — turned into one prompt 'What is the best <category>?', e.g. 'CRM for small business'.
competitorsNoCompetitor root domains to track share-of-voice against and diagnose the winner, e.g. ['rival1.com','rival2.com']. Supplying the winner as a domain unlocks the link-gap diagnosis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / samples / description
      Previous value: -"Repeat draws per (prompt×model) cell (default 1, max 3). Answer engines are nondeterministic — more samples tighten the 95% confidence interval on your rates. Credits multiply by samples."New value: +"Repeat draws per (prompt×model) cell (default 1, max 10). Answer engines are nondeterministic — more samples tighten the 95% confidence interval on your rates. Use samples>=5 for a defensible inclusion rate; 1 is a smoke test, not a measurement. Credits multiply by samples."
    • changedInput schema / properties / samples / maximum
      Previous value: -3New value: +10
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

This is highly transparent: it discloses that a live multi-model panel runs ('8 models × 3 prompts × up to 4 competitors'), that credits scale with samples, that 'Answer engines are nondeterministic', and that history mode 'Costs no credits.' These details go well beyond the annotations and meaningfully set agent expectations.

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 dense and front-loaded: the first sentence captures the core audit purpose, and the following sentences explain modes and cost constraints. Some mode details are repeated in the schema, but the description remains efficient and avoids fluff.

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?

Despite having no output schema, the description clearly enumerates what the report contains: whether the brand gets recommended, share-of-voice, why the leader wins, referring-domain gap, and which domains to target. It also covers mode trade-offs, credit implications, nondeterminism, and sampling guidance, so an agent has enough context to invoke it correctly.

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?

All 9 parameters already have rich schema descriptions, including enums, examples, credit costs, and defaults, so the description does not need to compensate. The tool description adds useful context like the Pro limitation and the referring-domain-gap diagnosis, but schema coverage is 100% and carries the parameter-semantics burden.

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 states a precise purpose: an 'AI Visibility / GEO audit' that 'asks a panel of AI models a buyer-intent question' and reports recommendation rates, share-of-voice, the leader's win reasons, and link targets. This clearly distinguishes it from sibling tools like backlinks_for_domain or core_web_vitals, which address different SEO concerns.

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 gives clear context on when to use each mode ('parametric ... baked-in brand memory' vs 'retrieval ... live web-search models'), and flags that the tool is 'Pro plan only' and spends credits. It does not explicitly name sibling alternatives or state when not to use it, but the use case is well-scoped enough for an agent to route correctly.

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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