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F1

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that Anthropic probing requires BYO key and direct payment, plus output structure (score, confidence, signals, raw_response). No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with main action. Could be slightly more concise, but no fluff and each sentence adds value.

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?

No output schema, but description mentions return fields (score, confidence, signals, raw_response, combined view). Covers multi-model probing, payment model, and use cases. Lacks detail on exact behavior of combined view, but sufficient for most agents.

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 descriptions. Description adds value by noting default model, that _apiKey is optional and required only for Anthropic, and summarizes output fields. Helps agent understand parameter usage beyond schema.

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?

Description uses specific verb 'probe' and clearly states it scores visibility (0-100) per model. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on brand/business visibility rather than competitor-specific scanning.

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?

Explicitly mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Does not explicitly state when not to use or name alternatives, but context is clear.

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

A3.6/5.0
Disambiguation2/5

Several tools are near-clones: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ mainly by mode, and deep_research overlaps with all of them. The five polymarket_* tools plus bet_research also blur together, and discover_tools vs suggest_questions both serve a 'what can I do' purpose.

Naming Consistency3/5

All names are readable snake_case, but the convention is mixed: verb-first (get_current_standings, validate_claim, scan_dependency), noun-first (polymarket_edges, pipeworx_trending), and bare verbs (remember, forget, subscribe). The F1 tools follow a clean get_* pattern that doesn't extend to the rest of the set.

Tool Count2/5

35 tools is excessive, especially since the server is named 'F1' but only 4 tools relate to F1. The set could be consolidated substantially: three ask_pipeworx variants, multiple overlapping polymarket scanners, and two tool-discovery helpers all add weight without clear scope.

Completeness2/5

The F1 side is thin: no qualifying results, constructor standings, lap data, circuits, or driver search by name. The Pipeworx half is broad, but it belongs to a different domain, leaving the overall surface feeling incomplete for either purpose.

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