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Statec Lu

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 declare read-only, idempotent, open-world, and non-destructive behavior. The description adds useful context by explaining the process (fetches page, extracts title/description/key links, emits markdown) and output format, going beyond annotations without contradicting them.

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 and well-structured: first sentence states purpose, second explains process, third describes output, fourth lists use cases. Every sentence adds value with no redundancy or fluff.

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?

For a simple two-parameter tool with no output schema, the description is complete. It explains what the output is (a text blob in llms.txt format), where to place it, and when to use it, leaving no significant gaps.

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 parameters are fully documented in the schema. The description adds minimal extra meaning (e.g., 'any URL') but does not elaborate on max_links, so it does not significantly enhance parameter understanding beyond the 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 a production-ready llms.txt file for any URL, with a specific output format. It distinguishes itself from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on file generation and listing distinct use cases.

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?

Provides explicit 'Useful for' scenarios (client sites, own projects, auditing competitors), giving clear context for when to use. However, it does not name alternative tools or explicitly state when not to use it, so it lacks full exclusion 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

A3.8/5.0
Disambiguation2/5

Multiple tools have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (beta is explicitly 'exactly' the stable version), and ai_visibility_check vs scan_competitor_ai_presence is a single-vs-batch duplicate. The six polymarket_* tools are differentiated by long descriptions, but their overlapping concerns (edges, arbitrage, fill risk, spread) would frequently misroute an agent, and discover_tools vs suggest_questions also compete.

Naming Consistency3/5

Names follow two coexisting conventions: verb_noun for actions (get_data, resolve_entity, validate_claim) and domain-prefixed families (polymarket_*, pipeworx_*, ask_pipeworx_*). Within each family the pattern is consistent, but mixing the two styles across the set, plus outliers like generate_llms_txt and bare verbs (remember, forget, recall), makes the overall scheme feel uneven though still readable.

Tool Count2/5

34 tools is well past the 'heavy' threshold and the count is not justified by the server's stated identity: a server named 'Statec Lu' (Luxembourg statistics) contains only 3 STATEC tools buried among general data-platform, prediction-market, AI-visibility, npm-scanning, and memory utilities. The sprawling, multi-domain surface would be more coherent split into separate servers.

Completeness3/5

The STATEC subset is complete (list_dataflows → dataflow_structure → get_data forms a full browse/fetch lifecycle), and the broader research surface covers entity resolution, grounded answers, comparison, claim verification, and subscription/alert/memory management. However, the overall domain is incoherent—a STATEC server missing nothing for statistics but carrying 31 unrelated tools—and there are notable gaps such as no tool to directly fetch a pipeworx:// citation URI and no execution side for the extensive Polymarket analysis tools.

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