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Opensky

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 already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds value by clarifying cost implications (free vs. BYO key for Anthropic), that it scores visibility, and returns per-model results. 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 extremely concise: two sentences covering purpose, default behavior, optional parameters, output format, and use cases. Every sentence is essential and front-loaded with the core action.

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?

Given no output schema, the description adequately describes return structure (per-model {score, confidence, signals, raw_response} + combined view). It explains inputs and use cases. Slight gap: no detail on how 'score' is calculated or what 'signals' include, but overall complete for an AI agent to invoke correctly.

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%, so baseline is 3. The description adds meaning beyond schema: it specifies the default model as 'Workers AI Llama-3.3-70b (free)', explains that '_apiKey' enables Anthropic probing, and notes that 'context' helps disambiguate. This extra context raises the score.

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 to score visibility of an entity (business/brand/product/topic) from 0-100 per model. This verb+resource combination is specific and distinguishes it from sibling tools like 'scan_competitor_ai_presence' or 'compare_entities'.

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 explains when to use the tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies default model and optional Anthropic integration. While it does not explicitly state when not to use or name alternatives, the context is clear enough for an AI agent.

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 entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all take natural-language factual questions, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. There is also overlap among get_states, get_aircraft, and airspace_activity, plus a large cluster of prediction-market tools with similar discovery purposes.

Naming Consistency4/5

The set is mostly snake_case and readable, with familiar patterns like get_*, list_*, resolve_*, and compare_*. It is not chaotic, but there are notable deviations: noun-phrase names like entity_profile, recent_changes, ai_visibility_check, and airspace_activity break the verb-first pattern.

Tool Count2/5

35 tools is too many for a single coherent server, especially because they comprise several independent families: aviation, data/research, prediction markets, memory, and subscriptions. Each tool is individually justified, but the bundle should be split into smaller focused MCP servers.

Completeness3/5

The data-research side is fairly complete, with discover, routing, grounded verification, entity resolution, search-within, compare, and follow-up tools, and the subscription and memory lifecycles are covered. However, the OpenSky side is incomplete: get_flights explicitly cannot return its data, and referenced route/arrival/departure tools are missing from the set.

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