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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, open-world, non-destructive. The description adds valuable context: default model is free, Anthropic requires a user-supplied key with direct payment, and it returns per-model plus combined views. This goes beyond the structured hints without contradicting them.

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?

Three sentences cover purpose, configuration, output format, and use cases. Front-loaded with the main action, no filler, every sentence earns its place.

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?

Given moderate complexity (4 params, no output schema) and read-only nature, the description is sufficient: it names output fields ('score, confidence, signals, raw_response'), combined view, and supported models. Could mention what 'signals' contains or response size, but not critical for invocations.

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%, so the description need not re-explain params. It adds extra semantics: explains the default model behavior for 'models', clarifies that '_apiKey' is passed straight through for Anthropic, and notes cost implications. This enriches schema descriptions.

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 probes LLMs for knowledge about a business/brand/product/topic and scores visibility per model. The verb 'probe' plus 'score visibility' is specific and resource-oriented, and the mention of per-model output distinguishes it from siblings like scan_competitor_ai_presence.

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 lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies configuration choices (default Workers AI vs. BYO Anthropic key). No direct exclusions or named alternatives, but the context is clear and actionable.

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

A3.8/5.0
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research) has significant boundary blurring—ask_pipeworx_beta explicitly 'currently matches ask_pipeworx exactly'—and the six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have heavily overlapping edge-detection purposes. Only the archive, memory, and subscription families are cleanly delineated.

Naming Consistency3/5

There are consistent family prefixes (ask_*, polymarket_*, pipeworx_*) and clean pluralized lists (list_files, list_subscriptions), but the full set mixes bare verbs (remember, recall, forget, search), nouns (entity_profile), and varying patterns (bet_research vs compare_entities, search vs search_within vs recent_changes). Readable in clusters, but no single naming convention binds the set.

Tool Count2/5

35 tools is heavy, and the count is fattened by five distinct product areas—data routing, prediction markets, archive.org access, memory, and subscriptions—that have little to do with each other or with the server name 'archive'. It sits in the 25+ heavy zone even before honoring the mismatch between its name and the sprawl of its purpose.

Completeness3/5

Individual subdomains are well-covered: the memory trio (remember/recall/forget), subscription lifecycle (subscribe/unsubscribe/list_subscriptions/recent_alerts), and archive lineup (search/get_metadata/list_files/wayback_check) are each complete, and extra machinery like pipeworx_feedback and recent_changes shows domain care. But the unifying domain is incoherent—a server named 'archive' that's also a universal data router and prediction-market toolkit—and the scope ends up both bloated and still full of gaps for any one of the intended users (e.g. no archive-item upload, no prediction-market portfolio management).

Resources