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Rba

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 1499 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 5,738 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?

Beyond the readOnly/idempotent annotations, the description discloses substantial operational behavior: account and paid-tier requirements, expected latency, how gaps[] work, the guarantee never to invent evidence, contradiction detection on standard/thorough, semantic excerpting, and citation fetchability. It gives the agent a realistic model of what happens during and after the call.

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 the most operationally critical facts (account requirement, not-open-web positioning, best-use cases) are front-loaded. Almost every sentence carries actionable information even if a few points could be tightened. It is structured effectively for a complex tool that needs this level of behavioral detail.

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 the full burden of explaining what the agent will receive, and it does so thoroughly: findings packet fields, verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], and latency expectations. Account/auth requirements and tier restrictions are also covered. Nothing essential 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 a baseline of 3 applies. The description adds real value on top: depth:'thorough' needs a paid plan, standard adds a gap-recovery hop, thorough chases leads, and standard/thorough return contradictions[]. It also clarifies that broad/multi-part natural-language questions are appropriate for the question parameter.

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 clearly states a specific verb and resource: it performs grounded multi-source research across Pipeworx's structured data sources, decomposing questions into facets and routing them across tools in parallel. It actively distinguishes itself from alternatives by saying 'this is NOT open-web search' and contrasting with ask_pipeworx for single lookups and current-news topics.

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 guidance: best for broad/multi-part structured-data questions, with concrete examples. It names alternatives and exclusion conditions: use ask_pipeworx if not signed in, for single lookups, or for breaking/current-news queries. This leaves no ambiguity about selecting this tool versus siblings.

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

A3.7/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying catalog, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all target prediction-market opportunities. entity_profile, recent_changes, and compare_entities also share company-research territory, making misselection likely without reading long descriptions carefully.

Naming Consistency3/5

All names use snake_case, but conventions are mixed: some are verb_noun (list_subscriptions, generate_llms_txt, resolve_entity), some are noun phrases (entity_profile, rba_cash_rate), and some are brand-prefixed product names (ask_pipeworx, pipeworx_trending). The polymarket_* and rba_* families are internally consistent, but the overall surface has no single predictable pattern.

Tool Count2/5

35 tools is a large surface, well above the 25+ threshold that typically becomes unwieldy. While the server covers a broad domain (data lookup, prediction markets, memory, subscriptions, company research), many tools are niche variants (ask_pipeworx_beta, polymarket_edge_tracker, scan_competitor_ai_presence) that add cognitive load rather than earning their place.

Completeness3/5

Core flows are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and data access has ask_pipeworx plus grounded and research variants. However, the surface is sprawly and uneven — prediction markets get six tools while other domain areas rely on generic routing, and the server's overall purpose is diffuse enough that gaps are hard to assess cleanly.

Resources