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Shodan Internetdb

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

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds behavioral details: it fetches the page, extracts title/description/key links, and emits standard markdown. It lacks error handling or rate limits but is consistent and informative.

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 a single well-structured paragraph that front-loads the main action, then provides context and use cases. It is slightly verbose but each sentence adds value. Could be more concise.

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 the tool's simplicity (2 params, no output schema) and rich annotations, the description explains the output format (single text blob) and use cases adequately. It covers the key aspects without 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 coverage is 100%, so the description adds marginal value. It rephrases 'url' as 'Full URL of the site to summarize' but does not mention 'max_links' or its default/max values, which are in the schema. Baseline 3 is appropriate.

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, specifying the format and purpose for AI crawlers. It distinguishes from sibling tools by focusing on a unique output (llms.txt) and use cases like indexing a client's site or auditing a competitor.

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 concrete use cases (getting client's site indexed, drafting for own project, auditing competitor). It does not include when not to use or alternative tools, but the scenarios are clear and distinct from siblings.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx_grounded and deep_research both answer grounded research questions, and bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. Long descriptions help, but the overlap creates real misselection risk, especially between the ask_pipeworx variants.

Naming Consistency4/5

Most tools follow a clear lowercase snake_case verb_noun or domain_verb pattern (lookup_ip, resolve_entity, validate_claim, list_subscriptions, generate_llms_txt). There are minor deviations like noun-first names (entity_profile, polymarket_edges, pipeworx_trending) and product-branded verbs (ask_pipeworx), but the overall style is consistent enough to predict behavior.

Tool Count2/5

32 tools is well over the 25-tool threshold where a tool set becomes hard to navigate, and the server named 'Shodan Internetdb' carries only one Shodan-related tool among dozens of Pipeworx, Polymarket, memory, and utility tools. The count reflects scope sprawl rather than a focused, coherent surface.

Completeness3/5

The broader inferred domain (structured data lookup, entity research, prediction markets, memory, subscriptions) is covered surprisingly well, with lifecycle tools for subscriptions and memory. However, there are notable gaps: no tool to fetch a pipeworx:// citation URI directly, no equivalent scan coverage for non-NPM ecosystems despite mentioning them, and the Shodan surface is minimal relative to the server name.

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