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Setlist Fm

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

A4.2/5.0
Behavior4/5

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

Discloses that probing is read-only (aligns with readOnlyHint) and returns per-model score/confidence/signals/raw_response plus combined view. No contradictions with annotations; adds value beyond them by explaining response structure and cost implications for Anthropic.

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?

Description is compact (3 sentences) and well-structured: action, model details, response format, use cases. No redundant information.

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?

Covers all key aspects: what it does, models, parameters, response structure, and use cases. No output schema, but description adequately explains return values. Annotations provide safety context, making the description nearly complete for this complexity level.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. Description adds minimal extra meaning (default model, example entity), but does not significantly enhance understanding beyond schema definitions.

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 clearly states the tool probes LLMs for entity knowledge and scores visibility (0-100), using specific verbs and resource. It distinguishes from siblings by focusing on multi-model probing with scoring, unlike related tools like 'scan_competitor_ai_presence'.

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 describes when to use the default model vs Anthropic with API key, and lists use cases (AI-marketing audits, pre-launch checks). Lacks explicit 'when not to use' or differentiation from sibling tools, but provides clear context.

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

The tool set mixes two completely different domains: setlist.fm (12 tools) and Pipeworx data services (30+ tools). An agent cannot easily distinguish which tools belong to the server's primary purpose, leading to confusion and misselection.

Naming Consistency2/5

Setlist.fm tools use consistent verb_noun patterns (artist, artist_search, artist_setlists), but the majority of tools follow no unified convention: some use snake_case (ai_visibility_check), others use mixed case (ask_pipeworx), creating an inconsistent naming landscape.

Tool Count2/5

43 tools is excessive for a setlist.fm API. Only about 12 are relevant; the remaining 31 are unrelated and bloat the tool surface, making it hard to navigate and maintain.

Completeness4/5

For the setlist.fm domain, the tools cover search, retrieval, and user data comprehensively (artists, setlists, venues, cities, countries, users). Minor gaps exist (e.g., no update/delete operations), but core workflows are supported.

Resources