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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. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety traits. The description adds valuable process context: fetching the page, extracting content, and emitting markdown. It also clarifies the output is a single text blob. It does not discuss edge cases (e.g., inaccessible URLs) but adds meaningful transparency beyond 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?

The description is three sentences, front-loaded with the core action, and contains no fluff. Every sentence contributes purpose, process, or context.

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?

For a simple 2-parameter tool with no output schema, the description explains inputs, process, output format, and use cases. It does not mention error handling or limitations, but it covers the essential information needed to use the tool effectively.

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 description coverage is 100%, with both parameters clearly documented. The description does not add extra parameter-specific details beyond what the schema provides, so the baseline score 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 a specific verb and resource: 'Generate a production-ready llms.txt file for any URL'. It explains the process (fetches, extracts, emits) and distinguishes itself from all sibling tools by describing a unique function. The use cases further reinforce its purpose.

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, which give clear context on when to use the tool. However, it does not mention alternative tools or when NOT to use it, so it stops short of full exclusionary guidance.

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

Many tools overlap in purpose: ask_pipeworx, ask_pipeworx_grounded, and deep_research all answer questions; multiple prediction market tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) have subtle distinctions. An agent would struggle to choose correctly among these.

Naming Consistency4/5

Tool names follow a consistent snake_case verb_noun pattern (e.g., list_subscriptions, generate_llms_txt). A few are noun phrases (stable_phases) but the style is uniform and predictable.

Tool Count2/5

33 tools is high for a single server, especially given the mix of two unrelated domains (materials database and general data querying). Many prediction market tools could be consolidated, and the broad scope suggests over-engineering.

Completeness3/5

The materials data side covers search and retrieval adequately. The query side offers many capabilities but has redundant paths (e.g., multiple ways to ask questions) and gaps in editing or updating data. Overall coverage is mixed.

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