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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 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 (readOnlyHint, openWorldHint, idempotentHint) already establish the safety profile, and the description goes far beyond them: authentication requirement, 15-90s latency expectations, the never-invented guarantee with explicit gaps[], citations guaranteed fetchable (citation_uri present only when the source emits a resolvable one), semantic excerpting rather than head-truncation, and depth-dependent behaviors (gap recovery, contradictions[] scan, lead chasing). No statement contradicts the 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 long (~3,900 chars) but nearly every sentence earns its place for a tool with three depth levels, auth gating, and no output schema. It is front-loaded with the most decision-critical facts (account required, use ask_pipeworx if not signed in, NOT open-web search). It loses a point for being one unbroken block of text with interleaved topics (the hop field appears mid-stream, and depth behavior is split across two separated passages).

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 carries full burden for return values — and it delivers: findings packet contents (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), gaps[], contradictions[], hop field, citation_uri resolvability, latency, auth, and sibling routing. Nothing an agent needs to correctly select and invoke this tool 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% for both params, so the baseline is 3. The description adds meaning beyond the enum labels: it ties each depth to concrete behavior ('standard' re-angles unanswered gaps, 'thorough' chases leads so multi-step questions resolve in one call) and adds latency per depth. It partially repeats schema content (gap recovery, contradictions[]), which keeps it from a 5.

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+resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources... in ONE call.' It explicitly differentiates itself from siblings — 'this is NOT open-web search' and 'For a single lookup use ask_pipeworx' — so an agent can distinguish it from ask_pipeworx and the other siblings without inspecting schemas.

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?

Provides explicit when-to-use, when-not-to-use, and named alternatives: 'If you are not signed in, use ask_pipeworx instead — it works on every tier,' 'For a single lookup use ask_pipeworx,' and 'Best for broad/multi-part questions over structured data.' It also states the auth prerequisite (free account required, thorough needs a paid plan), leaving no ambiguity about eligibility.

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

A3.6/5.0
Disambiguation3/5

Tools are mostly distinct but there is some overlap among lookup tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) and among Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage). Agents might occasionally struggle to choose the right one.

Naming Consistency2/5

Naming is inconsistent: some use verb_noun snake_case (ask_pipeworx, compare_entities), some are single words (forget, recall, remember), and others are noun-based (compliance_catalog, entity_profile). No consistent pattern.

Tool Count1/5

With 34 tools, the count is too high for a server named 'ISO Standards'. The majority of tools are unrelated to ISO standards (e.g., Polymarket, memory, web scraping), making the scope highly inappropriate.

Completeness1/5

As an ISO standards server, the tool set is severely incomplete. Only 4-5 tools (search_standards, get_standard, compliance_catalog, open_data_files) actually deal with ISO standards. Essential operations like comparing standards or browsing revisions are missing.