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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

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds that the tool fetches an external URL and extracts content, which is crucial for understanding side effects. No contradictions and no missing major traits, though rate limits or URL validity constraints could be noted.

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 four sentences with a clear front-loaded purpose, followed by mechanism and use cases. Every sentence is informative with no redundancy or unnecessary words.

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?

Despite no output schema, the description explains the output format ('standard llms.txt markdown format', 'single text blob') adequately. Parameters are fully covered. It does not mention potential errors or limitations, but overall completeness is high for a straightforward generation tool.

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% with both parameters described. The description elaborates on the 'url' parameter by stating what the tool fetches and extracts, and it gives default and max values for 'max_links' beyond the schema. This adds value over bare schema.

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 specifies a concrete verb ('generate'), a clear resource ('llms.txt file'), and the target audience ('AI crawlers'). It distinguishes the tool from siblings by its unique output format and use case, with no overlapping sibling tool.

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 use cases ('getting a client's site indexed', 'drafting for your own project', 'auditing a competitor') but does not mention when to avoid using it or list alternatives. However, no sibling tool competes directly, so the guidance is adequate.

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

A3.8/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical right now, making them near-duplicates. ai_visibility_check and scan_competitor_ai_presence also heavily overlap, and deep_research vs ask_pipeworx requires careful reading to know which to pick.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (list_templates, create_*, validate_claim). However, a few bare verbs break the pattern: compose, forget, recall, and remember.

Tool Count1/5

34 tools is already heavy, but the server is named 'Gitignore' and only 3 of 34 tools relate to .gitignore templates. The other 31 tools form a completely unrelated data platform, making the count an extreme mismatch for the apparent scope.

Completeness3/5

As a data platform, the surface is broad but has notable gaps: citations return pipeworx:// URIs yet no fetch/read-by-URI tool exists, and subscriptions support subscribe/unsubscribe/list but not update. For the gitignore name, only basic template list/get/compose is present, with the rest irrelevant to the stated purpose.

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