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Shodan Internetdb

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already indicate safe, idempotent, read-only behavior. Description adds context: fans out to multiple sources, mentions fallback logic and soft-fail for USPTO, and describes output structure. No contradictions observed.

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?

Description is thorough but efficient; front-loaded with example queries and key constraints. Every sentence provides useful information, though it could be slightly more concise.

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 covers return structure (changes[] grouped by source, total_changes, pipeworx:// URIs), sources, fallback, date formats, and sibling distinction. Complete for a complex tool.

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?

Input schema covers all 3 parameters with descriptions (100% coverage). The description enhances with examples like '2026-04-01' or '7d' and suggests typical usage ('Use "30d" or "1m" for typical monitoring'), adding value 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 it's a change feed for a company in a time window, with examples like 'What's new with X'. It explicitly distinguishes from sibling entity_profile, which is for static profiles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use (change feed over a window) and when not (use entity_profile for static profile). Also details sources and fallback behavior (GDELT→GNews), which helps the agent decide.

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.

Resources