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Apology

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

A4.7/5.0
Behavior5/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) cover safety and idempotency. The description adds beyond this by specifying that the default model is free, requiring an API key for Anthropic, and detailing the return format including per-model score, confidence, signals, and raw response. No contradictions.

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 three sentences with critical information front-loaded. Every sentence adds value: what it does, how it works, return format, and use cases. No fluff or repetition.

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?

Given the tool's complexity (multiple models, optional keys, no output schema), the description provides a complete overview: purpose, parameters, behavior, return format, and use cases. It sufficiently prepares an agent to invoke the 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%, setting a baseline of 3. The description adds value by explaining the _apiKey parameter as "BYO key" with cost implications and the context parameter as helping "disambiguate common names." This enhances understanding beyond the 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?

The description clearly states the tool's purpose: "Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model." It uses a specific verb and resource, and distinguishes itself from sibling tools by focusing on general visibility scoring rather than competitive scanning.

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 explicitly lists use cases: "AI-marketing audits, pre-launch brand checks, competitive monitoring." It provides context for when to use the tool but does not contrast with sibling tools like scan_competitor_ai_presence, missing explicit alternatives or exclusions.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates with beta explicitly identical to the stable version, causing potential misselection. ai_visibility_check and scan_competitor_ai_presence overlap heavily, and resolve_entity/discover_tools/ask_pipeworx all serve lookup purposes. Many tools are distinct, but the boundaries around the core query tools are blurry.

Naming Consistency2/5

Naming is inconsistent: mostly snake_case but mixed verb styles (ask_pipeworx vs pipeworx_feedback vs resolve_entity), brand prefixes applied irregularly, and no uniform convention (e.g., subscribe/unsubscribe/list_subscriptions vs forget/remember/recall vs polymarket_arbitrage/edges/edge_tracker). Some names are descriptive, but the set lacks a predictable pattern.

Tool Count2/5

32 tools is above the 25 threshold for a heavy surface, and the server mixes unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, apology generation). While a large data platform could justify many tools, the random inclusions (apology_generate, generate_llms_txt, scan_dependency) suggest a lack of scoping. Several tools could be consolidated without loss.

Completeness4/5

For the dominant data-research/prediction-market domain, coverage is strong: lookup, grounded verification, research, entity resolution, comparison, arbitrage scanning, fill risk, subscriptions, memory, and discovery are all present. Minor gaps exist (no direct account management beyond subscriptions, no tool to modify stored memories), but agents can mostly achieve their goals. The stray non-domain tools do not hurt completeness of the core platform.

Resources