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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?

Beyond annotations (readOnly, idempotent, openWorld), the description discloses the operational flow: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. It also clarifies the output is a single blob ready for a specific location. This adds meaningful behavioral context without contradicting 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?

Three sentences cover purpose, process, and use cases with zero redundancy. Information is front-loaded—the core action appears in the first sentence. Every element 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?

The tool is simple (2 params, no output schema), and the description sufficiently explains what is returned (a standard llms.txt text blob) and how it is produced. It also gives practical contexts, making it complete for an agent to act confidently.

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% and both parameters (url, max_links) are well-described in the schema. The description adds no supplementary parameter-specific meaning, so the baseline of 3 is appropriate.

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 tool generates a production-ready llms.txt file for any URL, with a specific verb ('Generate') and resource ('llms.txt file'). It also outlines the process (fetch, extract, emit) and differentiates from likely siblings by mentioning competitor auditing. This is a model of purpose clarity.

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 'Useful for:' scenarios—client indexing, personal projects, and competitor auditing—giving clear when-to-use guidance. However, it does not name specific alternative tools or state when not to use, so it stops short of the top score.

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

A4/5.0
Disambiguation5/5

Each tool has a highly specific purpose with detailed descriptions, making them easily distinguishable. Overlaps are minimal; for instance, Pipeworx and Polymarket tools have distinct roles within their domains.

Naming Consistency4/5

Most tools follow a consistent snake_case pattern (e.g., ask_pipeworx, compare_entities), but a few single-word names (e.g., forget, recall) deviate slightly, causing minor inconsistency.

Tool Count2/5

The server is named 'recipes' but contains only 4 recipe-related tools out of 34. The majority cover unrelated domains like finance, betting, and data queries, making the scope overly broad and misaligned with the server name.

Completeness2/5

For the 'recipes' domain, essential CRUD operations and features like meal planning are missing. While the general tool set is extensive, it lacks critical recipe-related functionality, leaving significant gaps for the intended purpose.

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