Skip to main content
Glama

Arbeitnow

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

A4.3/5.0
Behavior4/5

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

Annotations already indicate a safe, read-only, idempotent operation. The description adds process details (fetches page, extracts title/description/key links) without contradicting 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?

Two sentences plus a bullet list of use cases; every sentence adds value. No redundant or vague phrasing.

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?

The output format is described as a single text blob, which is sufficient given no output schema. The process is explained, and the tool's purpose is fully covered for an agent.

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 description coverage is 100%, so both parameters are fully documented. The description adds no additional meaning beyond what the schema already provides.

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 exact output format and purpose for AI crawlers. It is distinct from all sibling tools.

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?

Explicit use cases are provided (client indexing, own project, competitor auditing), making it clear when to use this tool. No direct alternatives are mentioned, but the unique function makes this less critical.

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

All Arbeitnow toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from job searching to company research to bet analysis. Even closely related tools like ask_pipeworx and ask_pipeworx_grounded are differentiated by one being hallucination-resistant. Memory and subscription tools are clearly separated.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., search_jobs, validate_claim, subscribe). However, some tools like pipeworx_feedback, pipeworx_trending, entity_profile deviate with noun_noun or proper noun patterns, creating minor inconsistency.

Tool Count4/5

29 tools is above the typical 3-15 range, but the server covers a wide breadth of domains (jobs, company data, betting, memory, monitoring) so each tool earns its place. Slightly over-scoped but reasonable.

Completeness3/5

The tool set covers many domains but has notable gaps. For jobs, only search/list/get exist (no create/update/delete). For company data, update is missing. For betting, there is analysis but no placement. The set is broad but not deeply complete for any single domain.

Resources