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Mast Nasa

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: the default model is free, Anthropic requires a BYO API key with direct payment, and it discloses the return structure. This goes beyond just restating 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 three sentences, front-loaded with the core action and output, and every sentence adds information. It efficiently covers purpose, model options, API key handling, return fields, and use cases without redundancy.

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?

Given there is no output schema, the description appropriately explains the return format (per-model score, confidence, signals, raw_response, plus combined view). It also clarifies cost implications and supported models, making it self-sufficient for an agent to decide and 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 the baseline is 3. The description enriches parameters by explaining that `_apiKey` is passed straight through to Anthropic and that you pay for those calls, and that omitting models uses the free default. It also clarifies the meaning of 'probe' in the context of visibility scoring.

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 a specific action ('Probe one or more LLMs'), the resource (business/brand/product/topic), and the output (visibility score 0-100 per model). It distinguishes this tool from the sibling tools by focusing on LLM knowledge visibility scoring, not general search or research.

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 gives concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to pass `_apiKey` (to probe Anthropic) and that the default model is free. However, it does not explicitly contrast with similar sibling tools like scan_competitor_ai_presence, so it stops short of providing exclusionary guidance.

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

A3.5/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, all of which query the Pipeworx database with different levels of structure. The detailed descriptions help differentiate them, but the overlap is notable.

Naming Consistency4/5

All tool names use snake_case consistently, which is good. However, the naming conventions vary: some are descriptive phrases (e.g., ai_visibility_check), others are verb_noun (e.g., list_subscriptions), and some are compound nouns (e.g., entity_profile). Lack of a single pattern reduces consistency slightly.

Tool Count3/5

34 tools is on the higher side for a single server, but it may be justified given the broad scope of Pipeworx data sources. However, the server name 'Mast Nasa' implies a focus on astronomy, yet only a few tools relate to that domain, making the count feel inflated and unfocused.

Completeness3/5

The tool set is comprehensive for the Pipeworx data platform, covering querying, grounding, entity resolution, comparison, subscriptions, and more. However, for the implied NASA/Mast domain, the surface is severely incomplete with only four astronomy-specific tools, leaving obvious gaps.

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