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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

A4.8/5.0
Behavior5/5

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

Annotations already provide readOnly/idempotent/openWorld hints, and the description adds extensive behavioral context: account and plan requirements, decomposition and parallel routing, findings-packet structure (gaps[], contradictions[], hop, citation_uri), semantic excerpting, latency expectations, and the explicit 'never invented' guarantee. 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 long but densely packed, front-loading critical constraints (account required, paid tier) before usage guidance. Some repetition of ask_pipeworx alternatives and a wall-of-text structure prevent a 5, but every major operational aspect is addressed for 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?

Given the complexity, 2 parameters, rich annotations, and no output schema, the description is remarkably complete: it explains return packet fields, depth-specific behaviors, latency, prerequisites, exclusions, and citation resolution. An agent has everything needed to select and invoke this tool correctly.

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?

Input schema coverage is 100%—both question and depth have clear schema descriptions. The description adds value beyond the schema by explaining depth-tier behaviors (quick/standard/thorough), the paid requirement for thorough, gap-recovery semantics, and contradictions[] presence, which goes beyond schema-level enum descriptions.

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 1500 STRUCTURED data sources' in one call, with decomposition and parallel routing. It also explicitly differentiates itself from open-web search and from ask_pipeworx, so an agent can reliably distinguish it from siblings.

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?

Gives explicit when-to-use guidance: best for broad/multi-part structured-data questions, and explicitly routes to ask_pipeworx for single lookups, breaking/current-news topics, and unsigned-in users. This is precise alternative-selection guidance that leaves little to inference.

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 the meta-querying tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools, which could cause confusion for an agent deciding which to use.

Naming Consistency3/5

Tool names use a mix of verb_noun and noun patterns, with snake_case throughout but no single consistent structure (e.g., ask_pipeworx vs. bet_research vs. dataset). The naming is readable but not uniform.

Tool Count4/5

At 33 tools, the count is on the higher side but justifiable given the broad scope of the Pipeworx platform, covering data querying, entity analysis, prediction markets, memory, subscriptions, and feedback.

Completeness4/5

The toolset covers a wide range of data sources and operations, including querying, entity profiling, comparisons, prediction market analysis, and monitoring. Minor gaps exist (e.g., no direct SEC filing viewer), but the meta-tools handle these adequately.

Resources