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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

A4.9/5.0
Behavior5/5

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

Annotations already mark this read-only, idempotent, and non-destructive, but the description adds substantial behavioral detail: account and paid-tier requirements, parallel facet routing, gaps[] for unanswered facets, contradictions[] only on standard/thorough, citation_uri only when fetchable, semantic excerpting of large records, and latency expectations. No contradiction with 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 dense and front-loaded with the most critical gate (account requirement and alternative), and every major topic gets covered. It is somewhat long and contains mild redundancy around the multi-source/decomposition concept, but the length is mostly justified given no output schema and a complex tool.

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 correctly shoulders the burden of explaining return values: findings packet, verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], and citation fetchability. It also covers account requirements, depth behavior, and timing, leaving no material gap for an agent deciding whether and how to call it.

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?

Schema coverage is 100%, but the description adds meaningful depth semantics beyond the schema: it maps each depth value to hops, gap-recovery behavior, and the contradictions[] scan, and clarifies that 'question' is natural-language and broad/multi-part by design. This materially improves an agent's ability to choose parameters.

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 action ('research'), a concrete resource (Pipeworx's 1500 structured data sources), and a clear mechanism (decompose into facets, route to tools in parallel, return a findings packet). It also explicitly contrasts itself with open-web search and with ask_pipeworx, making sibling differentiation immediate.

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 explicitly states when to use the tool ('broad/multi-part questions over structured data') and when not to ('For a single lookup use ask_pipeworx instead'), including the account/tier gate. Saying 'this is NOT open-web search' and naming the alternative tool removes ambiguity about tool selection.

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

A3.8/5.0
Disambiguation3/5

Tools are mostly distinct but several ask_pipeworx variants and research tools overlap in purpose, which could lead to agent confusion. The presence of memory and subscription tools adds unrelated functionality.

Naming Consistency2/5

Naming is inconsistent, mixing snake_case with varying verb patterns (ask, get, search, scan, etc.) and no clear convention. Some tools have descriptive phrases (e.g., generate_llms_txt) further breaking consistency.

Tool Count2/5

34 tools is excessive for a coherent server, covering too many disparate domains (genes, data queries, betting, memory) without clear focus. A gene server should have far fewer tools.

Completeness3/5

Gene-related tools are complete for basic queries (search, get, resolve), but the server's main purpose (HGNC) is overshadowed by many unrelated Pipeworx tools, creating a mismatch. The overall surface is broad but lacks domain focus.

Resources