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Openholidays

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and the description adds behavioral details: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. 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.

Conciseness4/5

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

The description is a single paragraph that is front-loaded with the key action and is informative without being overly verbose. It could be slightly more structured, but it 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?

No output schema exists, but the description specifies the return fields (ranked list with score, confidence, signal density per entity). For a tool with 4 parameters and no output schema, this is adequately complete.

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%, but the description adds extra semantics: entities first entry is treated as 'subject' for narrative, and context is 'shared context applied to every probe'. This adds value 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 uses a specific verb phrase 'Compare AI visibility across multiple entities side-by-side' and clearly distinguishes itself from the sibling tool ai_visibility_check (which is likely single-entity) and compare_entities (generic).

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 a clear use case ('competitive AI-marketing audits') with an example question, but does not explicitly state when not to use this tool or mention alternatives like ai_visibility_check for single-entity probes.

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

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