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The Committee

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 1498 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 5,728 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

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it read-only and idempotent, and the description adds substantial non-obvious behavior: account/paid-tier requirements, parallel decomposition, gap[] semantics with no invention, contradiction detection, citation resolvability, semantic excerpting, and latency ranges. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although long, the description is front-loaded with the most decision-critical information (account requirement and fallback tool) and every clause adds a distinct fact. The density is high without repetition.

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 to lean on, the description fully specifies the findings packet, citations, gaps, contradictions, and latency. An agent has everything needed to choose it, set depth, and understand the return envelope.

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 input schema covers both parameters thoroughly (100% coverage), so the baseline is 3. The description adds meaning beyond the schema by linking depth levels to plan requirements, hop/gap-recovery behavior, contradiction scans, and latency, and by giving concrete example questions 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?

States a specific verb and resource: grounded multi-source research across Pipeworx's 1497 structured data sources in one call. It also distinguishes itself from open-web search and from ask_pipeworx, making its position among siblings explicit.

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 selection rules: broad/multi-part structured-data questions for this tool, single lookups for ask_pipeworx, and breaking/colloquial current-news topics for ask_pipeworx. It even handles the unauthenticated case by directing unsigned-in users to ask_pipeworx.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All The Committee toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

The ask_pipeworx family creates real ambiguity: ask_pipeworx_beta explicitly states it currently matches ask_pipeworx exactly, leaving an agent no principled way to choose between them. discover_tools and suggest_questions also overlap as meta-tools for navigating the catalog, though the remaining tools (five polymarket_* tools, entity tools, subscription lifecycle) are well-delineated by their detailed cross-referenced descriptions.

Naming Consistency3/5

The set is uniformly snake_case with coherent subfamilies (ask_pipeworx*, polymarket_*, pipeworx_*, remember/recall/forget), but it mixes imperative verb_phrase names (validate_claim, resolve_entity, generate_llms_txt) with noun_phrase names (entity_profile, recent_changes, ai_visibility_check), and the_committee_convene breaks the pattern entirely with a full-sentence name. The inconsistency is stylistic rather than chaotic, so it stays readable.

Tool Count2/5

At 32 tools, the set exceeds the 25+ threshold for 'too many' and the breadth is not fully earned: ask_pipeworx_beta is self-admittedly redundant right now, and several tools feel bolted on from unrelated domains (the_committee_convene, generate_llms_txt, scan_dependency, ai_visibility_check). The core data-research and prediction-market scope would be tighter and more navigable at roughly 20-24 tools.

Completeness4/5

The primary domain — authoritative data lookup, entity research, and prediction-market analysis — is covered with no dead ends: query (ask_pipeworx, grounded, deep_research), profile (resolve_entity, entity_profile, compare_entities), track (recent_changes), verify (validate_claim), bet research (bet_research, polymarket_edges, arbitrage, fill_risk, kalshi_spread), subscriptions (subscribe/unsubscribe/list/recent_alerts), and memory (remember/recall/forget) form complete lifecycles. Minor gaps exist only at the periphery: no execution layer for prediction-market trades (research stops at fill-risk advice) and single-tool coverage for the npm/llms.txt/AI-visibility side-domains.

Resources