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Librivox

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

Schema Changelog

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

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

The description details the process ('Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format') and the output ('single text blob ready to drop at site-root/llms.txt'). Annotations already declare readOnlyHint and idempotentHint, so the description adds operational context without contradicting them.

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, each serving a distinct role: purpose, process/output, and use cases. It is concise and front-loaded, with every sentence contributing value.

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?

Even without an output schema, the description explains the return type ('single text blob') and the general format (standard llms.txt markdown). It covers multiple use cases and the operational flow, making it complete for a tool of this complexity.

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?

The input schema has 100% description coverage for both parameters (url and max_links), so the description does not need to add parameter-level details. It supports the tool's purpose but does not explain parameter behavior beyond what the schema already conveys.

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 and resource: 'Generate a production-ready llms.txt file for any URL,' which clearly defines the tool's action and target. It also mentions the intended audience (AI crawlers) and distinguishes itself from sibling tools by focusing on generating the file rather than just analyzing visibility.

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 lists three concrete use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' This provides clear context for when to use the tool, though it does not explicitly name alternative tools for exclusions.

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