Skip to main content
Glama

dnd5e

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable context about cost ('free' Workers AI default) and authentication ('BYO key' for Anthropic, with direct payment). It also reveals the return structure (per-model score, confidence, signals, raw_response). This goes beyond what annotations provide.

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 a compact, three-sentence paragraph where each sentence serves a distinct purpose: what the tool does, configuration details, and use cases. No redundant wording; it is front-loaded with the core purpose.

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?

For a tool with 4 parameters, full schema coverage, and annotations, the description adequately covers purpose, configuration, output format, and practical use cases. Minor details like rate limits or error handling are not mentioned, but they are not essential for correct invocation given the tool's simplicity.

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?

The schema has 100% description coverage for all parameters, so the baseline is 3. The description enriches this by explaining defaults (e.g., 'Default model is Workers AI Llama-3.3-70b'), the _apiKey pass-through behavior, and provides an example entity. This adds meaning 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 function: probing LLMs for knowledge about an entity and scoring visibility on a 0-100 scale. It specifies the resource (LLMs) and the action (probe and score), and this distinguishes it from siblings like scan_competitor_ai_presence, which likely focuses on a different aspect of AI visibility.

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 concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives clear context for when to use the tool. It does not explicitly list alternatives or exclusions, but the context is sufficient for selection.

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

All dnd5e 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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as the three ask_pipeworx variants, the memory tools (remember/recall/forget), and the subscription management tools. Additionally, the D&D tools are mixed with a large set of unrelated tools, causing confusion between domains.

Naming Consistency2/5

Naming conventions are inconsistent: some tools follow verb_noun pattern (e.g., get_class, list_spells), while others use descriptive noun phrases (e.g., ai_visibility_check, polymarket_arbitrage). There is no clear, predictable pattern across the set.

Tool Count2/5

With 35 tools, the count is high for a server ostensibly focused on D&D 5e. The inclusion of many unrelated tools from the Pipeworx ecosystem makes the set feel bloated and unfocused for the intended domain.

Completeness1/5

For the D&D 5e domain, only 4 tools exist (get_class, get_monster, get_spell, list_spells), which is severely incomplete. The remaining tools cover other domains, but they do not serve the server's primary purpose, leaving obvious gaps in functionality.

Resources