Skip to main content
Glama

Ine Pt

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. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already convey read-only, open-world, idempotent, non-destructive. Description adds that it probes multiple LLMs, uses default free model, requires Anthropic API key (paid separately), and returns per-model details. No contradiction with annotations.

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 concise sentences, front-loaded with action and output. Every sentence adds unique value: what it does, default vs. paid option, return structure, use cases. No wasted words.

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?

Without output schema, description adequately explains return format (per-model {score, confidence, signals, raw_response} + combined view). Covers parameter usage and use cases. Could mention rate limits or cost implications more, but overall sufficient for a probing tool.

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 covers all 4 parameters with descriptions (100% coverage). Description adds value by clarifying default model, the role of _apiKey (BYO key, direct billing), and the context parameter's function for disambiguation.

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 verb 'probe' and the resource 'LLMs', specifies the output (visibility score 0-100 per model with detailed fields), and distinguishes from sibling tools by focusing on AI visibility auditing rather than direct queries or research.

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 states use cases like AI-marketing audits, pre-launch brand checks, competitive monitoring. Implicitly distinguishes from siblings by purpose. Could improve by stating when not to use (e.g., for simple factual queries), but the context is clear enough.

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

All Ine Pt 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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions. However, a few tools like 'discover_tools' and 'suggest_questions' both serve exploratory functions and could cause confusion. Similarly, 'ask_pipeworx' and 'deep_research' overlap in scope but are differentiated by depth and account requirements. Overall, an agent can typically pick the right tool, but a few pairs require careful reading.

Naming Consistency3/5

All names use snake_case and are generally readable, but the convention varies: some are verb_noun (e.g., 'resolve_entity'), some are noun_noun (e.g., 'entity_profile'), and a few are just verbs (e.g., 'remember', 'forget'). The 'polymarket_' prefix helps group related tools, but the diversity in patterns slightly reduces predictability.

Tool Count4/5

With 32 tools, the set is slightly large but justified by the wide range of functionality: data querying, prediction markets, memory, subscriptions, and utilities. Each tool serves a distinct purpose, and the count is not excessive given the server's role as a gateway to thousands of data sources. It feels well-scoped for its domain.

Completeness4/5

The tool set covers most essential operations: querying data, entity profiles, comparisons, subscriptions, memory, and onboarding. Minor gaps exist, such as the lack of a generic subscription for all data changes or a way to list all available data packs directly. However, 'discover_tools' partially addresses this. Overall, the surface is comprehensive for the server's purpose.

Resources