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hackernews

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

A4.9/5.0
Behavior5/5

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

Even though annotations already mark the tool as read-only, idempotent, and non-destructive, the description discloses substantial additional behavior: account requirements and paid depth tier, parallel routing across 5,724 tools, gaps[] for unanswered facets, contradictions[] for disagreements, hop and citation fields, semantic excerpting, and expected latency. This goes far beyond the annotations and sets accurate expectations.

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 it is front-loaded with the most decision-critical information: account requirements and alternatives. Every sentence contributes substantive guidance, though the wall-of-text format and some repetition of depth semantics from the schema keep it from being a perfect conciseness model.

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?

Given the tool's complexity, the absence of an output schema, and the need to disambiguate from several sibling tools, the description covers nearly everything an agent needs: auth, cost, alternatives, output structure, failure behavior, latency, and citation resolvability. Nothing critical is left out.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage, but the description adds meaningful context beyond it: the "question" parameter is for broad/multi-part natural language and decomposition is the point, and the "depth" parameter's behavioral implications are enriched with gap recovery, lead-chasing, contradictions, and plan requirements. This helps an agent select the right depth value in context.

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: "grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources" in one call, and explicitly differentiates itself from open-web search. It also distinguishes itself from siblings by contrasting with ask_pipeworx for single lookups and current news.

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 questions over structured data," and when-not-to-use guidance: "For a single lookup use ask_pipeworx" and "For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx." It also names ask_pipeworx as the alternative for unsigned-in users, leaving no ambiguity.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, and deep_research also overlaps with them. The Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have fuzzy boundaries. An agent could easily pick the wrong meta-tool or duplicate functionality.

Naming Consistency3/5

All names are snake_case, but the pattern is mixed: some are verb_noun (get_item, list_subscriptions, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_edges), some are adjective_noun (deep_research, recent_alerts), and a few are single verbs (forget, recall, remember, subscribe). This is readable but not a consistent convention.

Tool Count2/5

With 36 tools, this is far too many for a server named 'hackernews'. The bulk of the tools concern Pipeworx data research, prediction markets, memory, and subscriptions — unrelated to the server's apparent purpose. Many of these could be split into separate servers, and the HN-specific functionality would be better served by a focused set of ~5-8 tools.

Completeness2/5

For the Hacker News domain implied by the server name, the surface is incomplete: there are read-only tools (search, top stories, item/comments) but no write functionality (submit, comment, vote) and no user profile access. The broader data-research capabilities are fairly comprehensive, but that does not rescue the server's coherence given its stated name.

Resources