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Conspiracy Theory

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?

Goes far beyond the readOnly/openWorld/idempotent/destructive annotations by disclosing account and paid-plan requirements, parallel routing across 5,743 tools, gaps[] semantics, fetchable citations, contradictions[] behavior, semantic excerpting, and latency. No statement contradicts the annotations; 'not open-web search' is compatible with openWorldHint given the structured external data sources named.

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?

Dense but purposeful; the account gate and alternative tool are front-loaded, and each remaining sentence covers a distinct operational detail (source scope, return packet, gaps, citations, contradictions, excerpting, latency). Slightly long, but complexity warrants 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?

With no output schema, the description carries the full burden of explaining return values and edge cases, and it delivers: findings packet fields, gaps[] honesty, citation_uri fetchability, contradictions[], hop field, and timeouts. It also covers auth prerequisites and when the tool will underperform, making it complete for an agent deciding whether and how to call it.

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?

Input schema already documents both params with 100% coverage, so baseline is 3. The description adds value by signaling that question should be natural language and broad/multi-part is fine, and by expanding depth's behavior (single hop, gap recovery, paid thorough tier) beyond schema wording, plus latency expectations.

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 opens with a concrete verb+resource: 'Grounded multi-source research... in ONE call' over Pipeworx's structured data sources, and explicitly says 'this is NOT open-web search.' It also names the intended task shape ('broad/multi-part questions over structured data') and contrasts with the ask_pipeworx sibling, so an agent can distinguish it 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 routing rules: use ask_pipeworx when not signed in, for a single lookup, and for breaking/colloquial current-news; use deep_research for broad multi-faceted structured-data questions. This is the strongest form of usage guidance and leaves 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 have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve route/discover/research data needs, with ask_pipeworx_beta explicitly noted as currently identical to ask_pipeworx. Polymarket tools also blur together (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk), and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates. An agent would frequently need to read long descriptions just to pick between near-equivalent entry points.

Naming Consistency3/5

The set is mostly snake_case and generally readable, with clear verbs like list_subscriptions, resolve_entity, generate_llms_txt, and validate_claim. However, conventions are mixed: brand-prefixed nouns appear (pipeworx_feedback, pipeworx_trending), one tool reverses the pattern (conspiracy_theory_generate vs generate_llms_txt), and the ask_pipeworx family follows its own scheme. The inconsistency is noticeable but does not make the names unreadable.

Tool Count2/5

32 tools is heavy, and the apparent scope is a scattered mix of general data research, prediction markets, memory, subscriptions, AI visibility, npm dependency checks, and conspiracy-theory generation. Many tools are meta-routers or aggregators that could be consolidated (e.g., the ask_pipeworx family, the polymarket family, the entity-research tools). The count feels like a growing internal toolkit rather than a deliberately scoped server.

Completeness2/5

The tools cover some complete sub-domains — memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and data lookup has multiple verification and research paths. But the server's nominal 'Conspiracy Theory' purpose is essentially one generation tool with no save, share, history, or validation workflow, while the bulk of the surface is unrelated general-purpose data tooling. The overall offering is broad but not coherently complete for any clear stated purpose.