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backlinkmcp-server

by io.github.johnsmalls22-rgb

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 3). Answer engines are nondeterministic — more samples tighten the 95% confidence interval on your rates. 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.

TDQS

A4.3/5.0
Behavior5/5

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

No annotations exist, so the description carries full disclosure. It details the panel size, credit costs, mode differences, depth options, nondeterministic behavior (samples), optional history, facts verification, and the diagnostic output. It's transparent about costs and limitations without contradictions.

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 long, but every sentence carries useful information—purpose, modes, cost, panel size, and deliverables. It front-loads the primary purpose and then details specifics. For a tool with 9 parameters, this is appropriately structured, though slightly verbose.

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?

Despite lacking an output schema, the description covers almost everything an agent needs: what the tool does, modes, costs, depth, history, facts, samples, and what the report includes. It doesn't explicitly describe return format, but the rich parameter descriptions and overall context make it sufficient for correct usage. Some advanced details like exact response structure are missing, but it's well above minimal viability.

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 schema has 100% description coverage, so the baseline is 3. The tool description adds context about the overall audit and cost structure but does not enrich individual parameter meanings beyond what the schema already provides. It repeats some details (e.g., modes) but adds little per-parameter value.

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 performs an 'AI Visibility / GEO audit'—it asks AI models a buyer-intent question and reports brand recommendations, share-of-voice, diagnosis, and link recommendations. It is distinctly different from sibling tools like ai_crawler_audit or site_audit, which focus on technical SEO. The purpose is specific and unambiguous.

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?

While it doesn't explicitly name sibling tools to avoid, it clearly describes when to use it (for AI visibility/GEO) and offers mode and depth choices that affect use cases (e.g., parametric vs retrieval, draft vs full). It also notes it's Pro plan only, giving clear context. It doesn't state 'don't use this for X', but the intent is easy to infer from the detailed description.

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

A4/5.0
Disambiguation4/5

Most tools target distinct actions, but backlinks_for_domain and top_linkers_for_domain overlap significantly since both return ranked linking domains with authority and link counts, differing mainly in scope. The descriptions do clarify one is a full payload and the other a quick skim, so an agent can usually choose correctly.

Naming Consistency3/5

All names are lowercase snake_case and readable, but conventions are mixed: several are noun phrases like site_audit and domain_authority_score, some are verb-first like find_link_prospects, and a couple are object-verb like schema_validate and indexnow_submit. The set is understandable but lacks a single consistent naming pattern.

Tool Count5/5

13 tools is appropriate for a backlink and SEO-focused server, fitting comfortably in the well-scoped range. Each area—backlink analysis, AI visibility, site auditing, and indexing—has dedicated tools without the set feeling bloated.

Completeness4/5

The backlink workflow is well covered: scoring, listing, overlapping linkers, prospect discovery, toxic-link flags, and live verification all exist. Minor gaps remain, such as no per-URL or anchor-level backlink data and no rank-tracking or keyword research, but the core stated purpose is served without major dead ends.

Resources