Abn Lookup

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 1868 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 7,096 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

Name	Required	Description
`q`	No	Alias for question.
`ask`	No	Alias for question.
`text`	No	Alias for question.
`depth`	No	How 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).
`input`	No	Alias for question.
`query`	No	Alias for question.
`prompt`	No	Alias for question.
`message`	No	Alias for question.
`question`	Yes	The research question, in natural language. Broad/multi-part is fine — decomposition is the point. Accepts query, q, prompt, text, input, ask, message as aliases.

Schema Changelog

Changes observed during successful MCP inspections.

Changed8 schema fields changedSep 29, 2026

addedInput schema / properties / ask

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

addedInput schema / properties / input

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

addedInput schema / properties / message

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

addedInput schema / properties / prompt

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

addedInput schema / properties / q

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

addedInput schema / properties / query

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

changedInput schema / properties / question / description

Previous value: -"The research question, in natural language. Broad/multi-part is fine — decomposition is the point."New value: +"The research question, in natural language. Broad/multi-part is fine — decomposition is the point. Accepts query, q, prompt, text, input, ask, message as aliases."

addedInput schema / properties / text

Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}

Changed1 schema field changedSep 18, 2026

addedInput schema / examples

Added value: +[
+  {
+    "depth": "quick",
+    "question": "What is the current US unemployment rate and how has it changed over the past year?"
+  }
+]

First observedSep 6, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations only cover read-only/idempotent/open-world; the description adds auth and tier gating (free account, paid for thorough), expected latency (15-60s, up to ~90s), gap-handling semantics (gaps[] never invented), a contradictions[] pass on standard/thorough, cited-and-fetchable citation_uri guarantees, and semantic excerpting instead of head-truncation. That is substantial behavioral context beyond the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the account requirement, which is useful, but the body is a dense multi-clause wall that repeats itself: citations are explained twice (citation_uri and 'stable pipeworx:// citation') and gaps[] is mentioned twice. It is information-rich but under-edited and hard to scan.

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 still fully describes the return packet: verbatim evidence, confidence, source, fetched_at, citation URI, hop field, gaps[], and contradictions[]. An agent knows what it will get back and how long to wait.

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% and the enum description for depth already documents facet counts and hop behavior. The description adds the practical payoff framing — re-angled gap recovery versus lead-chasing so multi-step questions resolve in one call — which helps choose a depth, though it largely restates the schema's own depth text.

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+resource+scope: grounded multi-source research across 1868 structured sources, decomposed into facets routed to 7,096 tools in parallel. It explicitly contrasts itself with open-web search and with the sibling ask_pipeworx, so an agent can identify it without opening a schema.

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 (broad/multi-part questions over structured data, with concrete examples), when-not (single lookup → ask_pipeworx; breaking/colloquial current news → ask_pipeworx), and an auth fallback (not signed in → ask_pipeworx). Alternatives and exclusions are both named.

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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Deep Research

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

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