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Wynncraft

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. Changed4 schema fields changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "Optional Anthropic API key (sk-ant-...) — only needed if \"anthropic\" is in models. Passed straight through to api.anthropic.com.",
+  "type": "string"
+}
    • changedInput schema / properties / context / description
      Previous value: -"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\", \"Polish painter\"). Helps disambiguate common names."New value: +"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\"). Helps disambiguate common names."
    • changedInput schema / properties / entity / description
      Previous value: -"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\", \"the company behind ChatGPT\"."New value: +"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\"."
    • addedInput schema / properties / models
      Added value: +{
+  "description": "Which models to probe. Supported: \"workers-ai\" (free default), \"anthropic\" (requires _apiKey). Omit for just workers-ai.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations indicate read-only and idempotent behavior, which the description does not contradict. Beyond annotations, the description adds key details: default free model, BYO key for Anthropic, return structure (score, confidence, signals, raw_response), and combined view.

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 two sentences with no wasted words. The first sentence front-loads the core purpose and output; the second covers key parameters and return format. Every sentence is essential.

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 moderate complexity (4 params, rich annotations, no output schema), the description fully explains behavior, usage, and return format. It leaves no critical gaps for an agent to invoke 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% with good descriptions. The description adds value by explaining the default model, when the API key is needed, and how context helps disambiguation, exceeding the baseline of 3.

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 brand/product visibility and returns a score. It uses specific verbs ('probe', 'score') and distinguishes itself from siblings like 'ask_pipeworx' and 'compare_entities' by targeting AI visibility audits.

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 explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains default vs. BYO model, but does not explicitly state when not to use or list alternative tools.

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

B3.1/5.0
Disambiguation2/5

The set is split between Wynncraft game data tools and a large Pipeworx data cluster, and within the Pipeworx cluster there is heavy overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (the beta is currently identical), ai_visibility_check and scan_competitor_ai_presence do essentially the same thing at different granularities, and six polymarket_* tools cover overlapping prediction-market functionality. An agent would frequently struggle to choose the right tool.

Naming Consistency3/5

Snake_case with a mostly verb_noun pattern dominates (ask_pipeworx, compare_entities, resolve_entity, validate_claim), and families like polymarket_* and pipeworx_* are internally consistent. However, there is a notable mix of noun-only tools (guild, item_database, leaderboard, player, news) and the server is named Wynncraft while the majority of tools are Pipeworx-branded, which breaks overall coherence.

Tool Count2/5

40 tools is well beyond the borderline range, and the count is inflated by redundancy: multiple ask_pipeworx variants, several overlapping polymarket tools, a memory trio, and subscription management that arguably belong to a separate server. The Wynncraft portion alone would be nicely scoped (~9 tools), but the merged surface feels heavy and unfocused.

Completeness4/5

The Wynncraft side offers solid read coverage of players, guilds, items, leaderboards, news, and online status, which is appropriate for the domain. The Pipeworx side covers lookup, grounded verification, comparison, research, prediction markets, subscriptions, memory, and feedback, leaving few obvious dead ends for the stated meta-purposes.

Resources