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Librivox

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. Changed4 schema fields changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "Optional Anthropic API key (sk-ant-...) — only needed if \"anthropic\" is in models. Passed straight through to api.anthropic.com.",
+  "type": "string"
+}
    • changedInput schema / properties / context / description
      Previous value: -"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\", \"Polish painter\"). Helps disambiguate common names."New value: +"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\"). Helps disambiguate common names."
    • changedInput schema / properties / entity / description
      Previous value: -"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\", \"the company behind ChatGPT\"."New value: +"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\"."
    • addedInput schema / properties / models
      Added value: +{
+  "description": "Which models to probe. Supported: \"workers-ai\" (free default), \"anthropic\" (requires _apiKey). Omit for just workers-ai.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds valuable behavior beyond that: the default model (Workers AI free), the cost implication of using Anthropic ('you pay Anthropic directly'), and the exact return payload structure. It also notes the scoring range and combined view, providing full transparency for a probing tool.

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 long, front-loading the core purpose, then briefly covering default behavior, output shape, and use cases. Every sentence carries information with no redundant filler.

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?

Given there is no output schema, the description fully covers the return format (per-model {score, confidence, signals, raw_response} + combined view). It also explains the default model, API key requirement, and common use cases. For a read-only tool with strong annotations, this is complete.

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 coverage is 100% with each of the 4 parameters clearly described (entity, models, _apiKey, context). The description adds marginal value by echoing the default model behavior and the BYO-key model, but the schema already covers these details, so the description does not significantly compensate beyond the baseline.

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 opens with a specific verb 'Probe' and resource 'one or more LLMs', and explicitly states the output is a visibility score (0-100) per model. It clearly distinguishes from sibling tools like ask_pipeworx by framing the tool as a cross-LLM brand visibility checker, with use cases like AI-marketing audits.

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 clear context for when to use it ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and when to pass the `_apiKey` to also probe Anthropic. It does not explicitly name alternative tools or exclusion criteria, but the purpose is specific enough to infer appropriate usage.

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

A3.5/5.0
Disambiguation2/5

The set mixes several unrelated domains (LibriVox, Pipeworx data lookup, Polymarket betting, memory, subscriptions), and within those domains there is heavy overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route to the same 5,798 tools, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk/bet_research all scan prediction-market opportunities. An agent can easily pick the wrong tool when the same question fits several of them.

Naming Consistency3/5

Names are mostly lowercase snake_case, but the patterns are inconsistent across domains: some are verb-first (ask_pipeworx, compare_entities, generate_llms_txt), some are noun-first (audiobook, tracks, polymarket_edges), and pluralization varies (audiobook vs audiobooks, authors vs tracks). The Pipeworx family is internally consistent, but the overall set has no unified convention.

Tool Count2/5

35 tools is heavy for a server named Librivox, and only 4 of them (audiobook, audiobooks, authors, tracks) actually relate to LibriVox. The remaining ~31 tools (Pipeworx, Polymarket, memory, subscriptions, AI visibility) make the count far exceed what the server name and apparent purpose suggest.

Completeness2/5

For a LibriVox-focused server, the tool surface is thin: search and fetch audiobooks, search authors, and list tracks, but no browse by genre, no reader/search-by-reader, no language filter, no author detail endpoint. The Pipeworx side is quite comprehensive, but it does not make up for the gap relative to the server's stated identity.

Resources