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H1b

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

A4.8/5.0
Behavior5/5

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

Annotations indicate read-only, open-world, idempotent, non-destructive. The description adds that the default model is free, but using Anthropic requires a BYO API key (paid separately). It also explains the return structure (per-model score, confidence, signals, raw_response + combined view). No contradiction 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 three sentences, each carrying distinct information: what it does, default model and key behavior, and use cases. No fluff, front-loaded with action.

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 and no output schema, the description fully explains inputs and outputs. It covers return format and usage scenarios, making it self-contained for an AI agent.

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

Parameters5/5

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 meaningful context: default model is Workers AI Llama-3.3-70b (free), _apiKey is passed directly to Anthropic, and context helps disambiguate. This significantly enriches parameter understanding.

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 verbs ('probe', 'score') and a clear resource ('LLMs for what they know about a business/brand/product/topic'). It distinguishes itself from similar tools by focusing on visibility scoring per model, and the sibling list includes 'scan_competitor_ai_presence' which likely differs in scope.

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 explicitly states the tool is useful for 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not provide explicit 'when not to use' or alternative tools, but it gives clear context and usage scenarios.

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

A3.7/5.0
Disambiguation2/5

Several tool families have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,743 tools with only subtle differences in grounding/fan-out, and the six polymarket_* tools overlap heavily in purpose. Even with detailed descriptions, an agent would frequently need to read the full text to pick the right one, and the beta variant is admitted to be currently identical to the stable router.

Naming Consistency4/5

All tool names use snake_case with clear family prefixes (ask_pipeworx, polymarket_*, h1b_*, pipeworx_*, subscribe/unsubscribe), making the set look organized. The minor inconsistency is that some names start with an imperative verb (ask, compare, validate, scan) while others are bare nouns (entity_profile, deep_research, bet_research), so the verb_noun pattern is not universal.

Tool Count2/5

34 tools is well beyond the recommended range for an MCP server, and many are near-duplicates (three ask_pipeworx variants, six prediction-market analyzers, three memory/three subscription tools). The breadth may reflect a genuinely large data catalog, but exposing it all as top-level MCP tools makes the surface heavy and hard to navigate.

Completeness3/5

For the actual data-platform scope, coverage is solid: lookups, research, memory, subscriptions, and validation are all present. However, a direct fetch tool for the advertised pipeworx:// citation URIs is missing (deep_research even conditions citations on resources/read existing), and the server's stated H-1B identity is underrepresented with only three specialized tools.

Resources