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Ine Pt

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false. The description adds behavioral context: it fetches the page, extracts title/description/key links, emits standard markdown, and outputs a text blob ready for site-root. This goes beyond annotations without contradiction.

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 two sentences plus a clear list of use cases. Every sentence earns its place, no redundant words, and front-loaded with the core purpose.

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 no output schema, the description adequately explains the output format (a single text blob in standard markdown). Parameters are well-documented, complexity is low, and the tool's behavior is fully described for an AI agent to select and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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 adds meaning by mentioning default 25 and max 50 for max_links, and gives an example for url. This adds value beyond the schema definitions.

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, specifying the verb (generate), resource (llms.txt), and the target AI crawlers. It distinguishes from sibling tools by focusing on a very specific output format, with no similar tools in the sibling list.

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 explicitly lists three use cases (client indexing, own project drafting, competitor auditing), providing good context for when to use. It does not explicitly mention when not to use or alternatives, but the purpose is clear and sibling differentiation is strong.

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
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions. However, a few tools like 'discover_tools' and 'suggest_questions' both serve exploratory functions and could cause confusion. Similarly, 'ask_pipeworx' and 'deep_research' overlap in scope but are differentiated by depth and account requirements. Overall, an agent can typically pick the right tool, but a few pairs require careful reading.

Naming Consistency3/5

All names use snake_case and are generally readable, but the convention varies: some are verb_noun (e.g., 'resolve_entity'), some are noun_noun (e.g., 'entity_profile'), and a few are just verbs (e.g., 'remember', 'forget'). The 'polymarket_' prefix helps group related tools, but the diversity in patterns slightly reduces predictability.

Tool Count4/5

With 32 tools, the set is slightly large but justified by the wide range of functionality: data querying, prediction markets, memory, subscriptions, and utilities. Each tool serves a distinct purpose, and the count is not excessive given the server's role as a gateway to thousands of data sources. It feels well-scoped for its domain.

Completeness4/5

The tool set covers most essential operations: querying data, entity profiles, comparisons, subscriptions, memory, and onboarding. Minor gaps exist, such as the lack of a generic subscription for all data changes or a way to list all available data packs directly. However, 'discover_tools' partially addresses this. Overall, the surface is comprehensive for the server's purpose.

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