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F1

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?

Even with readOnlyHint and idempotentHint already set, the description adds substantial behavioral context: parallel facet routing, a findings packet with evidence/confidence/source/fetched_at, explicit gaps[] that are never invented, contradictions[] for deeper depths, semantic excerpting, and expected latency. No contradiction with the annotations is present.

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 it packs in many non-obvious operational details that an agent needs before calling this tool: account requirements, alternatives, depth behaviors, output format, and timing. It is dense rather than padded, though a more scannable structure could improve it.

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 tool with no output schema and considerable complexity, the description covers everything an agent needs: prerequisites, fallback tool, input semantics, return packet structure, gap/contradiction behavior, scope limitations, and latency expectations. 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?

The schema already documents both parameters at 100% coverage, but the description adds meaning beyond it: it explains how depth values affect hop count, gap recovery, contradiction scanning, and pricing. This enriches the enum semantics rather than merely repeating the schema.

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 and resource: it performs 'grounded multi-source research' across Pipeworx's 1497 structured data sources in one call. It explicitly distinguishes itself from open-web search and from the sibling ask_pipeworx, making its purpose and scope immediately clear.

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 and when-not-to-use guidance: use ask_pipeworx if not signed in, for a single lookup, or for breaking/current-news topics. It names the alternative tool and the conditions that route to it, leaving little to inference.

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

Several tools are near-clones: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ mainly by mode, and deep_research overlaps with all of them. The five polymarket_* tools plus bet_research also blur together, and discover_tools vs suggest_questions both serve a 'what can I do' purpose.

Naming Consistency3/5

All names are readable snake_case, but the convention is mixed: verb-first (get_current_standings, validate_claim, scan_dependency), noun-first (polymarket_edges, pipeworx_trending), and bare verbs (remember, forget, subscribe). The F1 tools follow a clean get_* pattern that doesn't extend to the rest of the set.

Tool Count2/5

35 tools is excessive, especially since the server is named 'F1' but only 4 tools relate to F1. The set could be consolidated substantially: three ask_pipeworx variants, multiple overlapping polymarket scanners, and two tool-discovery helpers all add weight without clear scope.

Completeness2/5

The F1 side is thin: no qualifying results, constructor standings, lap data, circuits, or driver search by name. The Pipeworx half is broad, but it belongs to a different domain, leaving the overall surface feeling incomplete for either purpose.

Resources