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Nyt

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 1496 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 5,718 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 only declare readOnly/openWorld/idempotent/destructive-safe, but the description adds substantial behavioral context: account and paid-tier requirements, parallel tool routing, the findings packet structure, gaps[] for unanswered facets, contradictions[], hop fields, fetchable citation_uri constraints, semantic excerpting, and expected latency. There is no contradiction with 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 but dense, and nearly every sentence contributes a distinct fact needed to call the tool correctly (account, latency, source scope, output format, gaps behavior, sibling routing). It is front-loaded with the most decision-critical constraint (ACCOUNT REQUIRED). Slightly overpacked but justified by the tool's complexity.

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 return values; it explicitly details a findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], hop, and citation_uri. It also covers account requirements, latency, depth behavior, and alternatives. Nothing essential for correct invocation or expectation-setting 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 value beyond the schema by explaining the 'quick' depth (3 facets, single hop), 'standard' (default, gap recovery + contradictions), and 'thorough' (paid, 6 facets, iterative hop), and by noting that 'thorough' requires a paid plan. This goes meaningfully beyond the enum strings.

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 states a specific verb ('research') and resource ('Pipeworx's 1496 structured data sources') and clearly distinguishes this tool from open-web search and from sibling tools like ask_pipeworx. It also describes the multi-facet, parallel decomposition behavior, so an agent can identify what the tool does and what makes it unique.

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 explicitly says when to use this tool ('best for broad/multi-part questions over structured data') and when not to ('For a single lookup use ask_pipeworx'). It also directs users to ask_pipeworx for breaking/colloquial current news and explains why deep_research would return empty gaps in those cases. This is exemplary usage routing.

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
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical today, and ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying sources with overlapping question-answering purposes. Entity-focused tools like entity_profile, compare_entities, recent_changes, and resolve_entity also have fuzzy boundaries that make selection error-prone.

Naming Consistency2/5

Names mix conventions: verb_noun (list_subscriptions, search_articles, generate_llms_txt), bare verbs (remember, recall, forget), noun phrases (polymarket_arbitrage, pipeworx_trending, entity_profile), and an ask_* family with beta/grounded variants. There is no consistent verb or noun pattern across the set.

Tool Count2/5

With 35 tools, the surface is well above the 15-tool threshold for a focused server, and most tools are unrelated to the NYT domain implied by the server name. The breadth reflects a broad data-platform grab bag rather than a scoped, intentional tool set.

Completeness4/5

The query side is unusually complete: single-lookup, grounded lookup, deep research, claim validation, entity resolution, comparison, profile, change-feed, discovery, memory, and subscription lifecycle tools are all present. Minor gaps remain, such as no direct NYT article fetch by URL and no update path for stored memories, but agents can work around them.

Resources