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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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. Description adds valuable process details (fetches, extracts title/description/key links) and output format (standard llms.txt markdown, single text blob). No contradictions.

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 pack purpose, process, output format, and use cases efficiently. No wasted words; front-loaded with main action.

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?

Covers all key aspects: purpose, process, output format, and usage examples. No output schema needed since description notes output is a text blob. Could mention error handling for invalid URLs, but not required for typical use.

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% with clear descriptions for both url and max_links. Description doesn't add new parameter semantics beyond stating defaults already in schema. 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?

Description uses specific verb 'Generate' with explicit resource 'llms.txt file' for any URL. Includes intended audience (ChatGPT, Claude, Perplexity) and differentiates from sibling tools by focusing on file generation rather than analysis or scanning.

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?

Lists three concrete use cases (client indexing, own project drafting, competitor auditing). While it doesn't explicitly contrast with siblings like ai_visibility_check or scan_competitor_ai_presence, the examples provide clear context for when to use.

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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are three variants of the same router (beta is currently identical), while bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. ai_visibility_check is effectively a single-entity version of scan_competitor_ai_presence, and discover_tools overlaps heavily with suggest_questions.

Naming Consistency3/5

Tool names are uniformly snake_case and descriptive, but the pattern is mixed: verb-first names (ask_pipeworx, compare_entities, search_within) coexist with noun-first names (patent, scholarly, entity_profile), and the Lens.org pairing of patent/patents_search vs scholarly/scholarly_search is structurally inconsistent. Subgroups like pipeworx_* and polymarket_* are internally consistent, keeping the overall set readable.

Tool Count2/5

At 35 tools, this exceeds the 16-25 'heavy' band and bundles at least four distinct domains: Lens.org bibliometrics, Pipeworx data routing, Polymarket trading analysis, and memory/subscription utilities. While many tools serve legitimate purposes, the set feels sprawling and several variants (e.g., the three ask_pipeworx flavors) inflate the count without adding equivalent value.

Completeness4/5

The Pipeworx/Polymarket ecosystem is thoroughly covered with routing, grounded answers, deep research, entity resolution, validation, comparison, monitoring, and memory all present. The Lens.org portion is thin (search + fetch for patents and scholarly works) but covers the core read path; minor gaps exist such as no batch/export operations and no patent-number lookup.

Resources