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Abuseipdb

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

A5/5.0
Behavior5/5

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

Annotations already signal read-only/idempotent/open-world, but the description adds substantial behavioral detail: account and paid-tier requirements, parallel decomposition across 5,724 tools, never-invented gaps[], fetchable pipeworx:// citations, contradiction scans, semantic excerpting, and latency ranges. None of this 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.

Conciseness5/5

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

The description is long but every section earns its place: prerequisites, core behavior, routing guidance, depth mechanics, output/citation semantics, and latency. Difficult constraints are front-loaded at the top rather than buried, and there is no filler.

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 burden of explaining the return value and does so thoroughly: findings packet fields, gaps[], contradictions[], hop, citation_uri. It also covers auth, pricing, latency, and exclusions, making it complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While the input schema already documents both parameters (100% coverage), the description enriches depth semantics by explaining quick/standard/thorough as hop strategies and paid access, and clarifies that question should be broad natural language because decomposition is the point. This materially helps an agent choose argument values.

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 names a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources' in one call, and explicitly contrasts itself with open-web search and sibling tools like ask_pipeworx. It also gives concrete examples of suitable questions, so an agent can tell exactly what this tool is for.

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?

It gives explicit routing rules: use ask_pipeworx if not signed in, for single lookups, or for breaking/current-news topics; use deep_research for broad/multi-part structured-data questions. It also explains when each depth setting is appropriate, leaving little to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Abuseipdb toolsInspect
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TDQS

A3.6/5.0
Disambiguation3/5

Most tools are clearly distinct, but there are several overlapping clusters: ask_pipeworx and ask_pipeworx_beta are near-duplicates today, the Polymarket tools (edges, arbitrage, fill_risk, bet_research) have partially overlapping discovery purposes, and ai_visibility_check vs scan_competitor_ai_presence overlap on single vs multi-entity checks. These overlaps create real misselection risk, though the rest of the set splits cleanly.

Naming Consistency3/5

All names are snake_case and readable, but conventions vary noticeably: verb_noun for actions (check_ip, report_ip, list_subscriptions), domain-prefixed nouns for the Polymarket cluster (polymarket_edges, polymarket_arbitrage), and brand-prefixed meta tools (ask_pipeworx, pipeworx_feedback). Subfamilies are internally consistent, but the overall set mixes patterns.

Tool Count2/5

34 tools is already past the heavy threshold, but the bigger issue is the server name: Abuseipdb should have a handful of IP-abuse tools, yet only 3 of 34 actually relate to AbuseIPDB. The remaining 31 tools form an unrelated Pipeworx/Polymarket/memory suite, making the count wildly inappropriate for the apparent purpose.

Completeness2/5

For the declared AbuseIPDB domain, only check, report, and blacklist are covered; obvious gaps remain like removing/clearing a false report, bulk IP checks, or category metadata. The broader set is a grab bag of unrelated capabilities, so no single domain gets complete lifecycle coverage, and the nominal AbuseIPDB surface is thin and diluted.

Resources