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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 1495 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 5,714 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?

Annotations already signal readOnly, openWorld, idempotent, non-destructive. The description adds substantial behavior beyond that: account/plan requirements, decomposition into facets, parallel routing to 5,714 tools, findings packet structure, explicit gaps[] that are never invented, contradictions[] on deeper depths, semantic excerpting, citation_uri resolvability, and latency windows. 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 long (~300 words) but front-loaded with the account warning and core purpose. Most sentences earn their place, though the 'Second-hop iteration' paragraph duplicates the depth enum documentation in the schema, and the citation_uri explanation is more verbose than necessary. Slightly redundant but still well-organized.

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 fully explains return values: findings packet fields, gaps[], contradictions[], hop field, citation_uri, and latency. It also covers account prerequisites, fallback alternatives, failure modes (news topics yield gaps), and multi-step resolution—everything an agent needs to call 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?

Schema description coverage is 100%, and the depth enum's schema description already explains quick/standard/thorough behaviors including the paid plan. The tool description repeats rather than extends these semantics, so it adds no significant meaning beyond the schema. Baseline 3 applies.

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?

States a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1495 STRUCTURED data sources ... in ONE call.' Explicitly contrasts with open-web search and names ask_pipeworx as the alternative, so an agent can immediately tell what this tool is and is not.

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 when-to-use guidance: 'Best for broad/multi-part questions over structured data,' and direct when-not-to: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx.' Also specifies the account/plan prerequisite and fallback for unsigned-in users.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among similarly named tools like ask_pipeworx, deep_research, and bet_research, which could cause misselection. However, detailed descriptions help differentiate them.

Naming Consistency3/5

Tool names follow a mix of patterns (verb_noun, noun_noun, etc.) and use different prefixes (polymarket_, sec_8k_, pipeworx_), which is somewhat inconsistent but still readable and descriptive overall.

Tool Count3/5

34 tools is on the higher side, with several tools dedicated to specific subdomains (e.g., 6 Polymarket-related, 4 SEC 8-K tools). While each has a distinct role, the number feels slightly bloated for a single server.

Completeness4/5

The tool surface covers a broad range of data research and monitoring tasks, including filings, entity profiles, claims, and prediction markets. Minor gaps exist (e.g., no data writing tools), but core workflows are well-supported.

Resources