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Open Sanctions

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 1499 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 5,738 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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, so the safety profile is known. The description goes far beyond with critical behavioral details: account/auth requirement and fee for 'thorough', gap recovery and contradictions scan, citation_uri resolution semantics, excerpting of large records, and expected latency. No contradiction with annotations; rather, it enriches them.

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 long but dense — every sentence introduces a distinct piece of information (auth, alternatives, sources, decomposition, gaps, contradictions, excerpting, timing, citation resolvability). The critical info (account, alternative) is front-loaded. It could be slightly tightened but given the tool's complexity, the length is justified and not padded.

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 must carry the return format — it does, detailing the findings packet (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[]). It also covers edge cases (empty gaps for non-catalog topics), depth variations, and timing. For a tool this complex, nothing an agent needs in order to call it correctly is missing.

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?

Schema provides 100% coverage with descriptions for both params. The description adds value by explaining `depth` semantics in more human terms ('standard' adds gap-recovery hop, 'thorough' chases leads) and clarifying that `question` accepts broad/multi-part queries. This exceeds the baseline of 3 for high coverage, though it doesn't add new parameter-level constraints.

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 verb (research) with a precise scope (Pipeworx's 1497 structured data sources) and differentiates itself from open-web search and sibling ask_pipeworx. It clearly communicates the resource and the high-level behavior (decompose, route, return findings packet), leaving no ambiguity about what the tool 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?

Explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' and when-to-avoid: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It even names the alternative tool and the conditions that select it, which is the highest level of routing 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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, especially the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research) which all serve to query data but with subtle differences. An agent would struggle to distinguish which to use without deep understanding of their nuances.

Naming Consistency3/5

Most tool names follow a verb_noun or noun_noun pattern in snake_case, but there are exceptions like 'forget', 'recall', 'remember' which are single verbs, and names like 'ask_pipeworx' mix verb and proper noun. Overall pattern is discernible but not uniform.

Tool Count2/5

33 tools is high for a server named 'Open Sanctions' which implies a focused domain. The tool set spans sanctions, meta-querying, prediction markets, memory, subscriptions, and more, making it feel bloated and unfocused relative to the server's name.

Completeness2/5

For a sanctions server, only two tools (search_entities, get_entity) directly address the domain, missing obvious operations like update, delete, or list. Many tools are unrelated to sanctions, leaving the core domain incomplete.

Resources