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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 1517 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 5,798 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. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."New value: +"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)."
  2. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."
  3. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."
  4. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans)."New value: +"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."
  5. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the description needn't repeat safety traits. It adds valuable behavioral context: result contents (verbatim evidence + confidence + source + fetched_at + pipeworx:// citation), gaps[] never invented, contradictions[] for standard/thorough, semantic excerpting of large records, latency expectations (15-60s, up to ~90s for thorough). Could arguably be 5, but the note about 'depth:"thorough" needs a paid plan' and account requirements is placed in the description rather than structured, and the behavioral disclosures are rich but slightly dense.

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?

The description is information-dense but long and somewhat run-on; several asides ('depth:"thorough" needs a paid plan') and parentheticals are packed into the first sentence, making it harder to parse. Every sentence does earn its place in terms of content, but the structure is not front-loaded cleanly: the account note interrupts the core purpose statement. It is concise relative to how much it covers, but could be better organized into shorter sentences with the most critical routing info ('For a single lookup use ask_pipeworx instead') earlier.

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 only 2 params, no output schema, and rich annotations, the description carries the full burden and meets it: it explains the return packet shape, citation resolvability, gap/contradiction behavior, latency, account prerequisites, and fallback routing. The sibling list includes ask_pipeworx and the description explicitly differentiates them. Nothing an agent needs to decide whether to call this tool and what to expect is missing.

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 description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema: it explains what depth levels mean in terms of facets (quick=3, standard=3 with gap-recovery hop, thorough=6 with iterative hop), and clarifies that the question can be broad/multi-part. This enriches both parameters meaningfully beyond the enum descriptions, justifying a 4.

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 clearly states what deep_research does: grounded multi-source research across Pipeworx's 1517 structured data sources in one call, decomposing questions into facets and routing to tools in parallel. It distinguishes itself from open-web search and names sibling ask_pipeworx as the alternative when not signed in. The verb 'researches' plus specific resource scope ('STRUCTURED data sources', 'SEC filings, FRED/BLS, FDA...') makes the purpose unmistakable.

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?

The description gives explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' with concrete examples, and explicitly says 'For a single lookup use ask_pipeworx instead.' It also states account requirements and when to fall back to ask_pipeworx if not signed in. This is exemplary routing guidance that names alternatives and conditions.

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

Several tools form overlapping families (ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research, plus the six polymarket_* tools), and ask_pipeworx_beta is currently an exact behavioral duplicate. The long descriptions usually disambiguate them, but an agent could still struggle to quickly choose between similar research and edge-detection tools.

Naming Consistency3/5

Names are uniformly snake_case and prefix families like pipeworx_* and polymarket_* help, but there is no consistent verb_noun pattern: subjects, table_meta, recent_alerts, and entity_profile are noun phrases while remember, generate_llms_txt, and compare_entities are action-first. The mixed conventions are readable but less predictable than a uniform pattern.

Tool Count2/5

34 tools is well into the 'too many' range for a single tool set, even if each is individually documented. Several could plausibly be consolidated, such as ask_pipeworx_beta, the polymarket edge tools, and the AI-visibility pair.

Completeness4/5

For its broad stated purpose, the set covers the full lifecycle: lookup/research, entity profiles, comparisons, claim validation, prediction-market edge analysis, memory, subscriptions, and discovery. Minor gaps exist, such as no direct fetch-by-URI tool or subscription update path, but most workflows have a clear route.

Resources