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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).

TDQS

A4.2/5.0
Behavior4/5

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive behavior, all consistent with the description. The description adds behavioral details (fetches page, extracts title/description/key links) that go 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 concise (two sentences plus bullet points) and front-loaded with the key purpose. Every sentence adds value without redundancy.

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 tool with two parameters and no output schema, the description covers purpose, usage, output format, and examples. Minor gaps (error handling) are acceptable given the tool's simplicity.

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% and describes both parameters adequately. The description does not add additional meaning beyond the schema, so it meets the baseline for a well-covered 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 clearly states the verb (Generate), resource (llms.txt file), and purpose (for AI crawlers to index the site). It distinguishes itself from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing on file generation rather than analysis.

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 specific use cases (client indexing, own project, competitor auditing) but does not explicitly state when not to use the tool or mention alternatives. Still, the guidance is clear and helpful.

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
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the several polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping prediction-market territory. Descriptions help differentiate, but the overlaps could still cause misselection.

Naming Consistency4/5

The majority follow a clear verb_noun snake_case pattern (e.g., compare_entities, resolve_entity, generate_llms_txt). A few tools deviate with noun/adjective prefixes (montreal_datasets, montreal_recent, pipeworx_feedback, recent_alerts) or single verbs (remember, recall, forget), but the overall style is consistent and readable.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy. While each tool has a distinct role, the sheer number—spanning data access, prediction markets, memory, subscriptions, and meta-tools—makes the surface harder for agents to navigate compared to a more focused server.

Completeness4/5

For its broad data-gateway purpose, the server covers a wide range: lookups, research, entity resolution, claim validation, memory, subscriptions, and feedback. The Montreal-specific subset (datasets, query, recent) is adequate for the apparent scope, with only minor gaps like no subscription-update tool.

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