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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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds substantial context: account requirements and the paid threshold for 'thorough', latency expectations (15-60s, up to ~90s), the findings-packet return shape (verbatim evidence + confidence + source + fetched_at + citation), never-invented gaps[], contradictions[], and semantic excerpting of large records. This far exceeds what the annotations convey.

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?

Critical constraints are front-loaded (account required, use ask_pipeworx if not signed in), which is good. However, the description is unusually long and the middle section meanders through hop semantics, citation_uri fetchability, and returned fields in a dense stream-of-consciousness style. Dense but over-stuffed; some sentences could be tightened or reordered for agent parseability.

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 complex 2-parameter tool with no output schema, the description covers everything an agent needs: auth and tier constraints, runtime expectations, return-packet format, citation semantics, gap/contradiction behavior, and honest limitations (structured sources only). Nothing essential to calling it correctly 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 coverage is 100%, so baseline is 3, but the description adds real meaning: it explains the depth enum's behavioral consequences ('standard' adds gap-recovery hop + contradictions scan; 'thorough' is paid and adds an iterative lead-chasing hop) and clarifies that 'question' accepts broad/multi-part phrasing because 'decomposition is the point.' This goes beyond the schema's parameter comments.

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 1517 STRUCTURED data sources in ONE call') and explicitly distinguishes itself from open-web search. The 'NOT open-web search' contrast plus the sibling reference to ask_pipeworx makes it reliably selectable.

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 ('Best for broad/multi-part questions over structured data') and an explicit exclusion with a named alternative: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx.' This is model-quality routing guidance.

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
Disambiguation3/5

The tool set includes several overlapping tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve very similar purposes with only subtle differences in behavior (beta/grounded). Additionally, deep_research and validate_claim partially overlap with these. The prediction market tools are numerous but distinct, and memory/duration tools are clear. Overall, an agent would face some confusion when selecting among the ask_pipeworx variants.

Naming Consistency4/5

Most tools use consistent underscore_case with descriptive verb_noun patterns (e.g., list_subscriptions, resolve_entity, validate_claim). A few memory and subscription tools are single-word verbs (forget, recall, remember, subscribe, unsubscribe), which deviates slightly but remains readable. Overall naming is predictable and clear.

Tool Count3/5

With 33 tools, the server is on the heavier side. The server covers a broad scope (data queries, prediction markets, AI visibility, memory, utilities, subscriptions), which justifies many tools, but some feel redundant (e.g., three variants of ask_pipeworx, multiple polymarket edge tools). A more focused set (around 20-25) would be more typical for coherence.

Completeness4/5

The tool surface is extensive, covering factual Q&A via a universal router, entity profiles, comparisons, prediction market analysis, memory, subscriptions, AI visibility, and utilities. The meta-tool ask_pipeworx provides access to thousands of structured sources, so most data needs are addressable. Minor gaps include no direct tool for specific SEC filing retrieval beyond the meta-router, but this is covered indirectly.

Resources