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Gemini Crypto

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.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare safe read-only, open-world, idempotent. Description adds behavioral details: free default model, BYO key for Anthropic, return structure per-model with score/confidence/signals. 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?

Three concise sentences, front-loaded with main action and output. No redundant information; every sentence adds essential detail.

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 but description explains return format (per-model {score, confidence, signals, raw_response} + combined view). Adequately covers what agent needs to know, though could mention result structure more explicitly.

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 description adds value with examples for entity, supported model values, clarification for _apiKey, and disambiguation for context. Goes beyond 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 brand visibility and scores it, with specific verb 'probe' and resource 'LLMs'. It distinguishes from siblings by focusing on visibility scoring, unlike 'scan_competitor_ai_presence'.

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?

Provides context for use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). Explains when to use _apiKey for Anthropic, but lacks explicit when-not-to-use or 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

A3.8/5.0
Disambiguation3/5

Many tools serve overlapping research purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, compare_entities), which could confuse an agent. However, descriptions help differentiate them by use case (single vs multi-part, grounded vs standard, etc.). Some overlap remains.

Naming Consistency3/5

Tool names mix patterns: some are verb_noun (ask_pipeworx, bet_research), others are noun_phrase (price_feed, recent_alerts) or adjective_noun (ticker_v2). No strong naming convention, but all use snake_case consistently, making them readable.

Tool Count3/5

40 tools is on the high side for a single server, covering both Gemini exchange data and Pipeworx's broad knowledge tools. While each tool serves a purpose, the scope feels broad, and some tools could be separated into dedicated servers.

Completeness4/5

The Gemini exchange tools cover essential read-only data (order book, candles, ticker, trades), but lack order placement, likely intentionally. The Pipeworx tools provide extensive research capabilities across many domains, leaving few gaps for the stated purposes.

Resources