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Regulatory Catalysts

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 declare readOnlyHint=true and openWorldHint=true, and the description powerfully supplements them with behavioral context: account/tier requirements, parallel decomposition across 5,743 tools, explicit gaps[] so nothing is invented, contradictions[] for deeper depths, citation_uri fetchability constraints, semantic excerpting, and latency expectations. No contradictions with 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 front-loaded with the most operationally critical facts: account requirement and fallback tool. Every sentence adds real substance, but the single dense paragraph with nested parentheticals and run-on clauses reduces scannability. It earns a 4, not a 5, because structural organization could better serve an agent parsing quickly.

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?

There is no output schema, so the description carries the full burden of explaining returns and behavior. It thoroughly covers the findings packet (evidence, confidence, source, fetched_at, citation), gaps[], contradictions[], hop field, and latency. For a complex research tool with two parameters and advanced depth semantics, nothing critical 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 the baseline is 3. The description adds meaningful value beyond the schema by detailing what each depth level actually does: 'quick=3 (single hop)', standard's gap-recovery hop and contradictions scan, and thorough's iterative lead-chasing. The question parameter is also contextualized with example phrasings.

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 names a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in one call, and explicitly contrasts itself with open-web search. It clearly distinguishes itself from ask_pipeworx and other siblings by scope, data domain, and decomposition behavior.

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?

Usage guidance is explicit and actionable: 'If you are not signed in, use ask_pipeworx instead', 'Best for broad/multi-part questions over structured data', and 'For a single lookup use ask_pipeworx'. It also names exact fallback conditions, making selection versus siblings unambiguous.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and multiple Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, etc.) could cause confusion for an agent selecting the appropriate tool.

Naming Consistency2/5

Tool names mix snake_case and camelCase inconsistently, with no strong verb_noun pattern. Examples include 'ask_pipeworx' vs 'discover_tools' vs 'validate_claim', indicating a lack of naming convention.

Tool Count2/5

33 tools is high for a single server, including many utility tools (memory, subscriptions) that seem peripheral to the core regulatory/data domain. This suggests scope creep and could overwhelm agents.

Completeness4/5

The tool set covers a wide range of regulatory and financial data needs, including company profiles, entity comparison, claim validation, FDA catalysts, and prediction market analysis. Minor gaps exist (e.g., no tool for editing data), but overall it is comprehensive for its stated purpose.

Resources