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Bitfinex

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. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: default model is free, optional Anthropic probing requires BYO key with direct payment, and returns per-model structure. This goes beyond the annotations without contradicting them.

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?

Four sentences, front-loaded with the core action, followed by key details and use cases. No filler or redundancy. Every sentence earns its place.

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?

The description effectively covers the tool's essential context: purpose, default behavior, cost implications, return format, and typical use cases. Since there's no output schema, the description's mention of per-model fields and combined view is important and well-handled. Sibling overlap or advanced nuances aren't addressed, but the overall completeness is strong for a tool of this complexity.

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 schema already documents all parameters. The description reinforces the default model behavior and mention of `_apiKey` pass-through, but adds little beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 a business/brand/product/topic and scoring visibility. It specifies a concrete action and resource, but doesn't explicitly differentiate from similar siblings like scan_competitor_ai_presence, so it earns a 4 rather than 5.

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 clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default vs optional model choice. It doesn't explicitly say when not to use it, but the context is sufficient for an agent to decide. No exclusions or alternatives mentioned, so not a 5.

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

C2.8/5.0
Disambiguation2/5

Many tools overlap or are near-duplicates: ask_pipeworx and ask_pipeworx_beta are currently identical, and there are multiple prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) with fine-grained but confusing distinctions. The mix of Bitfinex market data tools with an unrelated Pipeworx research suite makes tool selection genuinely ambiguous.

Naming Consistency2/5

All names use lowercase snake_case, but the semantic pattern is inconsistent: bare nouns (ticker, candles, trades, stats), verb_noun phrases (validate_claim, compare_entities, generate_llms_txt), and large prefixed families (ask_pipeworx*, polymarket_*) coexist. This mixed convention gives no reliable cue to a tool's function.

Tool Count2/5

42 tools is excessive for a server named 'Bitfinex'. Only about a dozen tools actually relate to the crypto exchange (ticker, candles, trades, book, liquidations, etc.); the rest are a grab bag of Pipeworx research, prediction markets, memory, and subscription features. The count bloats the surface and obscures the server's purpose.

Completeness2/5

The set has no coherent scope. For a Bitfinex server, there are no account/trading tools, only market data. For the buried Pipeworx functionality, the surface is extensive but unrelated to Bitfinex. The overall result is an incomplete hodgepodge with no clear lifecycle or workflow for a single domain.

Resources