Skip to main content
Glama

Seo Backlinks

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, openWorldHint), the description reveals the need for a user-provided API key for Anthropic, that Anthropic calls are paid directly to Anthropic, and details the return structure including per-model score/confidence/signals/raw_response.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact (4-5 sentences) with no redundancy. Every sentence provides essential information: probe function, default model, optional Anthropic, return fields, and use cases. Front-loaded with the main verb 'Probe...'.

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 fully explains the return structure (per-model score, confidence, signals, raw_response, combined view). Parameters are fully covered in both schema and description. No gaps for an agent to use this tool correctly.

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?

Schema coverage is 100% with good descriptions. The description adds value by explaining the default model behavior and the purpose of `_apiKey` (bring your own key, direct payment to Anthropic), which is not fully detailed in the schema alone.

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 it probes LLMs for knowledge about an entity and scores visibility per model, specifying the default model and optional Anthropic integration. It distinguishes from sibling tools like 'entity_profile' or 'deep_research' by focusing on AI visibility scoring.

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?

Explicitly mentions use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' While it lacks explicit 'when not to use' guidance, the context is clear enough for an agent to decide appropriately.

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

All Seo Backlinks toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

ask_pipeworx_beta explicitly states it currently behaves identically to ask_pipeworx, making them practically indistinguishable, and ask_pipeworx_grounded is the same router with one extra verification step. The five polymarket_* tools also share overlapping 'find/validate edge' territory, and scan_competitor_ai_presence is a direct wrapper over ai_visibility_check, so an agent must read carefully to pick correctly.

Naming Consistency3/5

Prefix families (ask_pipeworx_*, polymarket_*, seo_backlinks_*, pipeworx_*) provide some predictability, and several tools follow verb_noun (compare_entities, resolve_entity, validate_claim). However, conventions mix single verbs (remember, forget, recall), noun phrases (entity_profile, recent_changes), and seo_referring_domains breaks the seo_backlinks_* family pattern, so the overall scheme is readable but inconsistent.

Tool Count2/5

36 tools is over the 25+ 'too many' threshold even for a broad platform, and the mismatch is far worse given the server is named 'Seo Backlinks' — only 5 of 36 tools actually serve that purpose. The other 31 tools (Pipeworx research, Polymarket betting, memory, subscriptions) belong to a different scope entirely, making the set feel bloated and mislabeled.

Completeness3/5

The five genuine backlink tools cover a single-domain audit well (summary, list, anchors, referring domains, history), but they lack standard SEO workflows like multi-domain or competitor backlink comparison, and there is no tool connecting backlink data to the AI-visibility audit tools. The surrounding Pipeworx surface is extensive, but it belongs to a different domain than the server's stated purpose.

Resources