Skip to main content
Glama

Openholidays

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

A4.5/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and open-world. The description adds valuable behavioral context: the default free model, the BYO key for Anthropic, and the per-model output structure. No contradiction.

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 concise (3-4 sentences) and well-structured: core purpose first, then details, then return format, then use cases. Every sentence adds value without redundancy.

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 sufficiently explains the return structure (per-model score, confidence, signals, raw_response + combined view). It covers the main aspects given the tool's moderate complexity, though could mention variability in results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description greatly enhances understanding by explaining the default model, the purpose of each parameter (e.g., _apiKey for Anthropic, context for disambiguation), and typical usage patterns.

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: probing LLMs for knowledge about an entity and scoring visibility. It specifies the verb 'probe' and the resource 'visibility', and distinguishes itself from sibling tools like 'ask_pipeworx' which are general Q&A tools.

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 provides clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains default vs. paid model usage. It lacks explicit when-not-to-use or alternatives, but the context is sufficient.

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

All Openholidays 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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; multiple Polymarket tools) and many serve unrelated domains, making it hard for an agent to distinguish which tool to use for a given task, especially given the server's holiday theme.

Naming Consistency3/5

Most tool names follow a lowercase_with_underscores pattern, but the prefixes vary (ask_pipeworx, pipeworx_*, polymarket_*, etc.) and some names are less descriptive (e.g., process, run, execute-like vague verbs are absent, but still the naming lacks a unified convention across the broad set.

Tool Count2/5

35 tools is excessive for a server named 'Openholidays'. The vast majority of tools (e.g., SEC filings, Polymarket, npm scanning) are unrelated to holidays, making the tool count feel bloated and unfocused.

Completeness3/5

For the holiday domain, the server includes necessary tools (list_countries, list_subdivisions, public_holidays, school_holidays) and is complete. However, the server's actual scope is far broader, and many unrelated tools are present, which dilutes the completeness for its stated purpose.

Resources