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Traject Ecommerce

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

A4.5/5.0
Behavior4/5

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

Annotations already provide rich metadata (readOnlyHint, idempotentHint, etc.). The description adds value by explaining the process: fetches page, extracts title/description/key links, emits standard markdown. No contradictions with 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, no filler, front-loaded with key action. Every sentence serves a purpose: what it does, how it works, use cases.

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, the description states output is a single text blob ready for site-root. Annotations cover all behavioral aspects. Parameter count is low and fully described. The tool is simple and the description is complete.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so description doesn't need to over-explain. It complements by describing the URL parameter as 'Full URL' and max_links with default/max, and the description of extraction actions adds context beyond 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 tool generates an llms.txt file for a URL, with specific verb 'Generate' and resource 'llms.txt'. It distinguishes from siblings by focusing on this specific task, while siblings include unrelated tools like search, research, etc.

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 use cases: getting a client's site indexed, drafting for own project, auditing competitor. It implies when to use but does not explicitly exclude alternatives or state when not to use, though context makes it clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Traject Ecommerce toolsInspect
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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes, e.g., Amazon and Walmart tools are platform-specific. A few overlapping tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by clear usage guidance, so an agent can disambiguate with reasonable effort.

Naming Consistency3/5

Tool names mix verbs and nouns with varying styles (e.g., ai_visibility_check, compare_entities, scan_competitor_ai_presence). There is no uniform pattern like verb_noun; some are descriptive phrases. The inconsistency is noticeable but not chaotic.

Tool Count2/5

36 tools is too many for a server named 'Traject Ecommerce', as many tools cover unrelated domains (Polymarket, npm packages, SEC filings). The scope is excessively broad, making the server feel like a general-purpose plugin rather than a focused ecommerce toolset.

Completeness2/5

For an ecommerce-focused server, it only covers Amazon and Walmart product/search/reviews, missing major platforms and backend operations. The broader tool set is detailed but not ecommerce-specific, leaving obvious gaps for the intended purpose.

Resources