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Cryptocompare

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds significant behavioral context: return structure (per-model score, confidence, signals, raw_response + combined view), default model details, and API key requirement. 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.

Conciseness5/5

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

The description is concise (4 sentences) and front-loaded with the core function. Every sentence adds value: core action, model options, return format, use cases. No wasted words.

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?

For a tool with 4 parameters, no output schema, and rich annotations, the description covers essential aspects: purpose, parameter usage, return structure, and use cases. It is complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description explains parameter interactions (e.g., _apiKey needed for anthropic, context for disambiguation) but does not add much beyond the schema's existing parameter descriptions.

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 entities and scores visibility, with a specific verb ('Probe', 'score') and resource ('LLMs', 'business/brand/product/topic'). It distinguishes from siblings like 'scan_competitor_ai_presence' by its focus on AI visibility scoring.

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 when to use different models (free default vs. Anthropic with API key). It lacks explicit when-not-to-use or alternatives, but context is 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

B3.1/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx, its beta, and grounded variants, the six polymarket_* tools, and the three price endpoints (price, price_full, price_multi) can be confused despite distinct purposes. Long descriptions provide some disambiguation, but an agent must read carefully to select the correct tool.

Naming Consistency4/5

Tool names mostly follow snake_case with verb_noun or noun_noun patterns (all_coins, compare_entities, top_market_cap), and related families share clear prefixes (histo_*, polymarket_*). Minor deviations exist (bare verbs like remember/forget, brand names like ask_pipeworx), but the overall pattern is readable and consistent.

Tool Count2/5

With 46 tools, the server is far beyond the 3-15 well-scoped range and nearly double the 25-tool threshold for 'too many'. Many tools are unrelated to the server's apparent crypto purpose (generate_llms_txt, scan_dependency, memory helpers), making it feel like a general-purpose utility rather than a focused data service.

Completeness3/5

The crypto data surface is fairly complete (spot, historical, top lists, news, social stats, exchange metadata), and the Pipeworx meta-tools (ask_pipeworx, deep_research, entity_profile) cover a broad range of factual queries. However, there are notable gaps: no direct way to fetch pipeworx:// citation URIs, and no advanced crypto order-book/trade endpoints, leaving some workflows as dead ends.

Resources