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Shodan Internetdb

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,743 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

TDQS

A4.6/5.0
Behavior5/5

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

Even with readOnly/openWorld/idempotent annotations already present, the description adds substantial behavioral context: account and plan requirements, latency expectations, the exact findings packet shape, gaps[] for unanswered facets, never-invented citations, contradictions[] scans, semantic excerpting, and stable fetchable pipeworx:// URIs. Nothing contradicts the annotations.

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 front-loaded with the account requirement and core behavior, and nearly every sentence adds useful information. However, it is long and contains a garbled tail fragment ('Over broad/multi-topic BREAKING never invents citations...') that hurts structural clarity, preventing a top score.

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?

With no output schema, the description carries the full burden for return semantics and delivers: inputs, output packet contents, citation resolvability, gap behavior, latency, auth/plan constraints, depth differences, and sibling routing. An agent has everything needed to invoke this tool correctly.

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?

The input schema already covers both parameters at 100%, including a detailed enum description for depth and a clear description for question. The description reinforces that questions can be broad/multi-part and that thorough requires payment, but it adds no new syntax or format details beyond the schema, so the 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 states a specific action: grounded multi-source research across Pipeworx's 1500 structured data sources in one call, decomposing questions into facets and routing to 5,743 tools in parallel. It explicitly distinguishes itself from open-web search and from siblings like ask_pipeworx, so an agent can tell what this tool uniquely does.

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?

The description gives explicit routing rules: if not signed in, use ask_pipeworx; for a single lookup, use ask_pipeworx. It also states what this tool is best for with concrete examples and clarifies that depth:'thorough' requires a paid plan. This is strong when-to-use and when-not-to-use 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
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