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Tarot Draw

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

The description complements annotations (readOnlyHint, idempotentHint, etc.) by disclosing that the default model is free and that providing an API key enables Anthropic probing (with cost implications). It also describes the return structure (per-model {score, confidence, signals, raw_response} + combined view), adding behavioral context beyond the 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?

The description is three sentences long, front-loaded with the core purpose, and each sentence adds essential information (purpose, configuration, returns and use cases). No redundancy or fluff.

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?

The tool has no output schema, but the description explains the return format. With 4 well-documented parameters, clear use cases, and no nested objects or enums, the description is sufficiently complete for an agent to understand inputs, outputs, and invocation context.

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 description coverage is 100%, so baseline is 3. The description adds value by explaining the default model for 'models' (free Workers AI Llama-3.3-70b) and clarifying that '_apiKey' is passed directly to Anthropic. This provides semantic context not fully captured in 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 probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100) per model. It uses specific verbs and resources ('probe', 'score visibility') and distinguishes from siblings like 'scan_competitor_ai_presence' by highlighting the multi-model probing and scoring aspect.

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 context on when to use the tool ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to provide the optional '_apiKey' parameter. It does not explicitly compare with sibling tools like 'scan_competitor_ai_presence', but the use cases are clear.

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

A3.7/5.0
Disambiguation2/5

Multiple tool families have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, suggest_questions and discover_tools are near-duplicates, and ai_visibility_check is a subset of scan_competitor_ai_presence. The five polymarket_* tools are heavily overlapping in purpose and rely on long descriptions to distinguish them, which an agent must read carefully to avoid misselection.

Naming Consistency3/5

All names are lowercase snake_case and there are helpful prefixes (polymarket_*, ask_pipeworx_*, pipeworx_*), but the verb/noun ordering is inconsistent: verb-first names (generate_llms_txt, resolve_entity, scan_dependency) sit alongside noun-first names (bet_research, entity_profile, recent_changes) and bare verbs (forget, recall, remember). Sub-families are internally consistent, but the set as a whole follows no single convention.

Tool Count2/5

32 tools is over the 'too many' threshold, and the scope is a grab-bag rather than a focused server: data research, prediction-market analysis, npm dependency checks, llms.txt generation, memory utilities, subscriptions, and exactly one tarot tool. The server is named 'Tarot Draw' yet 31 of 32 tools serve a completely different purpose, making the count wildly mismatched to the apparent identity.

Completeness2/5

For the inferred Pipeworx data/prediction-market domain the coverage is genuinely deep — ask/grounded/deep research, entity resolution, profiles, comparisons, validation, subscriptions, alerts, edge tracking, and arbitrage all exist. But for the stated purpose ('Tarot Draw'), the surface is one draw tool with no deck details, spreads, reading history, or reversal support, and the data tools' domain is so diffuse that an agent cannot rely on the set forming a coherent workflow.

Resources