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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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that it fetches the page (network call), extracts title/description/key links, and emits standard markdown, providing behavioral context beyond the 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: purpose, process, use cases. No fluff, front-loaded with key action. Every sentence adds value.

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?

Despite no output schema, description adequately describes output format ('standard llms.txt markdown format', 'single text blob ready to drop at site-root/llms.txt'). Covers purpose, process, output, and use cases entirely for a simple tool with good annotations.

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% (both url and max_links described). The description does not add extra meaning beyond schema; it implies url usage in the first sentence but doesn't elaborate further. Baseline 3 is appropriate as schema does the heavy lifting.

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 'Generate a production-ready llms.txt file for any URL' with specific verb and resource. It explains the process and distinguishes from siblings by being specific to llms.txt generation, not general AI visibility.

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 lists three concrete use cases (client site indexing, own project drafting, competitor auditing). It provides clear context for when to use, but does not explicitly exclude alternatives or mention when not 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

A3.9/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all route questions to the same underlying data catalog, making it hard to pick the right one. The Polymarket-related tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have overlapping discovery and analysis purposes.

Naming Consistency3/5

Many tools use descriptive snake_case, and the ask_pipeworx family shares a clear prefix, but the set mixes generic memory verbs (remember, recall, forget), brand-prefixed tools (here_*, pipeworx_*), and standalone names like bet_research and scan_dependency. There is no consistent verb_noun pattern across the whole server.

Tool Count2/5

35 tools is heavy for a single MCP server, and a large portion are meta-tools layered over the same 5,752-tool catalog (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad intentional scope explains the count, but the tool surface feels bloated and harder to navigate than it needs to be.

Completeness4/5

The domain is unusually broad—data querying, entity resolution, comparison, monitoring, memory, geolocation, prediction markets, dependency scanning—and the set covers most workflows end to end. Minor gaps exist, like no direct pipeworx:// citation fetcher and no update/list/delete pattern for entity profiles, but the core user journeys are well supported.

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