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Youtube

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destruction. The description adds context about free vs. paid model probing but does not cover rate limits, response size, or error behavior. 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?

Four sentences, each with distinct purpose: what it does, default/key note, return format, use cases. No fluff, front-loaded with core purpose.

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?

Without an output schema, the description adequately details return fields (score, confidence, signals, raw_response, combined view) and scoring range. Lacks error handling or edge-case notes, but sufficient for typical use.

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 baseline is 3. The description adds value via examples (e.g., 'Pipeworx' for entity) and payment nuance for _apiKey, going beyond the schema's descriptions.

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 and score') and resource ('LLM knowledge of an entity'). It explicitly distinguishes from siblings by focusing on multi-model visibility scoring, unlike single-query tools like ask_pipeworx or research tools like deep_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 identifies use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains model choices (free default vs. BYO key). However, it lacks explicit exclusions or comparisons to sibling tools like scan_competitor_ai_presence.

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
Disambiguation3/5

There is meaningful overlap among the ask_pipeworx, deep_research, validate_claim, and polymarket_* tools, but the descriptions do draw fairly clear boundaries between them. The five yt_* tools are distinct and easy to tell apart, though the unrelated Pipeworx cluster makes the overall set feel muddier than it should.

Naming Consistency3/5

Most tools use readable snake_case, and there are coherent prefixes like yt_ and polymarket_, but the set mixes bare verbs (remember, recall, forget, subscribe), noun-style names (entity_profile, deep_research), and API-like names (ask_pipeworx, generate_llms_txt). The pattern is not chaotic, but it is inconsistent across the set.

Tool Count2/5

36 tools is well above the typical well-scoped range, and the vast majority are unrelated to the server's stated 'Youtube' identity. The actual YouTube surface is only five tools, while 31 tools belong to a different Pipeworx/Polymarket domain.

Completeness2/5

For a YouTube-focused server, the yt_* tools cover search, channel info, video details, and comments, but miss obvious surfaces like playlists, transcripts, subscriptions, uploads, and video updates. The large non-YouTube tool collection does not fill these gaps; it only makes the server feel mis-scoped.

Resources