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Riot Games

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

A4.6/5.0
Behavior4/5

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

Annotations (readOnlyHint, idempotentHint, etc.) already signal safety. The description adds transparency about the return shape (per-model score, confidence, raw_response) and free/BYO cost model, which is valuable beyond 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?

Two sentences, front-loaded with core purpose, no redundant language. Every part earns its place.

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?

With no output schema and moderate complexity (4 params, 1 required), the description compensates fully by explaining the return structure, model options, auth, and optional features. It is complete for the tool's complexity.

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%, but the description adds significant meaning: examples for entity, clarifies models and the dependency between _apiKey and anthropic model, and explains context disambiguation. This exceeds baseline 3.

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 verb ('Probe'), resource ('LLMs for visibility'), and the scoring output (0-100 per model). It distinguishes from siblings like 'scan_competitor_ai_presence' by specifying per-model visibility scoring.

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 provides explicit usage context (AI-marketing audits, brand checks, competitive monitoring) and setup details (default free model, optional Anthropic with API key). It lacks explicit 'when not to use' statements but offers 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

B3.1/5.0
Disambiguation2/5

Several tools have overlapping purposes: champion_mastery and summoner_top_mastery both return mastery data, and the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus deep_research all handle question-answering. The inclusion of an entire unrelated Pipeworx research suite under a Riot Games server creates cross-domain ambiguity, making it difficult to know which tool to select.

Naming Consistency3/5

Most tools use snake_case, but the pattern varies: resource_by_key (account_by_puuid), verb_noun (generate_llms_txt, compare_entities), bare verbs (forget, recall, remember), and standalone nouns (match, status). Pipeworx and polymarket tools share consistent prefixes, but the Riot tools and meta-tools break the pattern, resulting in a mixed but still readable convention.

Tool Count2/5

42 tools is far above the typical 3-15 for a focused server. Only 10 are Riot Games-specific; the remaining 28 are unrelated Pipeworx, data-research, Polymarket, and memory tools. The excessive count dilutes the server's purpose and makes it feel bloated.

Completeness3/5

The Riot Games domain is covered reasonably well with accounts, summoners, mastery, matches, and rankings, but misses common endpoints like champion static data and live match info. The extensive non-Riot tools do not fill these gaps and instead add an unrelated, separately complete surface that distracts from the server's apparent purpose.

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