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Shodan Internetdb

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent hints. The description adds valuable behavioral context: workers-ai is free, Anthropic requires BYO API key and direct billing, and it details the return format (per-model score, confidence, signals, raw_response plus combined view). No contradictions with 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 well-structured with a clear main action, parameter clarifications, and use cases. Every sentence adds value without redundancy. It is appropriately sized and front-loaded.

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 no output schema, the description adequately covers the return structure and key behaviors. It explains model options and API key handling. However, it does not detail the scoring algorithm or what 'signals' are, which could be helpful for an AI agent.

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% with parameter descriptions. The description goes beyond by clarifying default model, stating that _apiKey is only needed for Anthropic, and explaining how 'context' helps disambiguate entities. This adds meaningful nuance to the parameter usage.

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 uses specific verb+resource ('probe LLMs, score visibility') and clearly states the tool's purpose for AI-marketing audits, pre-launch checks, and competitive monitoring. It distinguishes from sibling tools by its unique focus on visibility scoring across models.

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 lists use cases (audits, pre-launch, monitoring) and explains when to use the default vs. Anthropic model. However, it does not explicitly state when not to use this tool or compare it to alternatives like deep_research or scan_competitor_ai_presence.

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

A3.9/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx_grounded and deep_research both answer grounded research questions, and bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. Long descriptions help, but the overlap creates real misselection risk, especially between the ask_pipeworx variants.

Naming Consistency4/5

Most tools follow a clear lowercase snake_case verb_noun or domain_verb pattern (lookup_ip, resolve_entity, validate_claim, list_subscriptions, generate_llms_txt). There are minor deviations like noun-first names (entity_profile, polymarket_edges, pipeworx_trending) and product-branded verbs (ask_pipeworx), but the overall style is consistent enough to predict behavior.

Tool Count2/5

32 tools is well over the 25-tool threshold where a tool set becomes hard to navigate, and the server named 'Shodan Internetdb' carries only one Shodan-related tool among dozens of Pipeworx, Polymarket, memory, and utility tools. The count reflects scope sprawl rather than a focused, coherent surface.

Completeness3/5

The broader inferred domain (structured data lookup, entity research, prediction markets, memory, subscriptions) is covered surprisingly well, with lifecycle tools for subscriptions and memory. However, there are notable gaps: no tool to fetch a pipeworx:// citation URI directly, no equivalent scan coverage for non-NPM ecosystems despite mentioning them, and the Shodan surface is minimal relative to the server name.

Resources