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by io.github.pipeworx-io
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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 1653 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 6,332 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "depth": "quick",
+    "question": "What is the current US unemployment rate and how has it changed over the past year?"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so no contradiction. The description adds substantial behavioral detail: parallel decomposition, findings packet shape, gaps[] for unanswered facets, hop field, contradiction scans, semantic excerpting, citation_uri resolvability, and timing (15-60s, up to ~90s). It does not dwell on what gets destroyed or auth failures, but as a read-only research tool the disclosure is strong. It misses only minor items like rate-limit behavior, which the annotations and openWorldHint already contextualize.

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 account requirement and the key distinction from ask_pipeworx, then flows through capabilities, use cases, depth semantics, citations, and timing. Every sentence carries functional content. It is longer than ideal, but for a complex tool with three depth modes and strong sibling overlap, the length is earned. Minor deduction for some redundancy in explaining gap recovery under both the depth parameter and the 'second-hop iteration' section.

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?

For a two-parameter tool with 100% schema coverage and no output schema, the description is effectively complete. It tells the agent what the result packet contains, how citations behave, what happens when the data can't answer, how long to wait, what the depth options change, and which sibling to use instead for overlapping cases. There is no meaningful gap an agent would trip over when selecting or invoking this tool.

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 coverage is 100%, so both parameters are documented in the schema. The description adds meaning beyond the schema by explaining the behavioral consequence of each depth value: quick/standard/thorough differ in facet count and hop behavior, and 'thorough' requires a paid plan. The example schema shows a valid call shape. The description does not need to compensate for any missing parameter documentation, so a 4 reflects the value added above an already-complete schema.

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') and resource (Pipeworx's 1653 structured data sources) and clearly distinguishes itself from open-web search. It names what it is not ('this is NOT open-web search') and contrasts with ask_pipeworx for single lookups, resolving sibling ambiguity. Purpose is concrete: route multi-facet questions to parallel tools and return a grounded findings packet.

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, with concrete examples. Also explicit when-not-to-use: for single lookups or breaking/current-news topics, prefer ask_pipeworx. The account requirement and depth-tier prerequisites are stated up front, so an agent can decide whether to call it or route elsewhere.

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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