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Sistat Si

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
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive behavior, and the description adds: 'Fetches the page, extracts title/description/key links, and emits standard llms.txt format.' No contradictions; transparency is excellent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a bullet list, front-loaded with the core action. It efficiently conveys purpose and usage, though could be slightly tighter. No wasted words.

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?

Given two parameters, no output schema, and annotations covering safety, the description adequately explains the output format ('standard llms.txt markdown format') and the extraction process. Completeness is sufficient for 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 descriptions cover both parameters (url and max_links) fully. The description only adds minimal examples ('e.g. https://example.com', 'default 25, max 50'), which aligns with schema. Baseline 3 is appropriate since schema already provides meaning.

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 verb and resource. It distinguishes from sibling tools like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on llms.txt generation.

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 explicit use cases (client site indexing, personal projects, competitor auditing), providing clear context. It does not mention alternatives or when not to use, but the use cases are sufficient for guidance.

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
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as between discover_tools and suggest_questions. However, detailed descriptions help differentiate them.

Naming Consistency2/5

Naming conventions are highly inconsistent, mixing snake_case (ask_pipeworx, forget), camelCase (discoverTools, suggestQuestions), and underscores (ai_visibility_check, compare_entities). No predictable pattern.

Tool Count3/5

33 tools is on the high side, but the broad domain (finance, pharma, prediction markets, etc.) partly justifies it. However, some tools like forget, remember, recall seem generic and could be separated.

Completeness4/5

The tool surface covers a wide range of functionalities: visibility checks, pipeworx queries, entity profiles, comparisons, subscriptions, memory, and more. Minor gaps may exist in real-time data or specific niche sources.

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