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Uk Gazette

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?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses account and paid-plan requirements, parallel decomposition, latency expectations, gap[] semantics ('never invented'), contradictions[] on standard/thorough, citation_uri fetchability, and semantic excerpting of large records. This richly describes behavior without contradicting 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.

Conciseness4/5

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

The description is long, but nearly every clause contributes: auth, alternative routing, scope, output packet, gap behavior, depth modes, latencies, and citation guarantees. It front-loads the most important operational constraint (account required) and the primary alternative. A little restructuring would help, but there is no real filler.

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 burden of explaining return values, and it does so thoroughly: findings packet fields, hop, citation_uri, gaps[], contradictions[], and expected latency. It covers auth, depth semantics, and the boundary with ask_pipeworx. Nothing essential for correct invocation or expectation-setting 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% and already documents both parameters thoroughly. The description adds value by clarifying that 'question' may be broad/multi-part and by elaborating the practical consequences of each depth level (quick vs standard vs thorough passes, paid requirement). This exceeds the baseline for fully-described schemas.

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 names a specific verb ('research'), a precise resource ('Pipeworx's 1500 STRUCTURED data sources'), and a distinctive mechanism (1,500 sources, 5,743 tools, parallel facets). It sharply distinguishes itself from open-web search and from ask_pipeworx, making sibling differentiation trivial.

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?

It gives explicit when-to-use guidance: broad/multi-part questions over structured data. It also names the alternative ask_pipeworx for single lookups and for users who are not signed in, and explains depth-mode trade-offs. This is exactly the 'when vs alternatives' guidance an agent needs.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all serve similar query routing). Tools from unrelated domains (UK Gazette, Polymarket betting, AI visibility checks) are mixed together, making it hard for an agent to distinguish which tool to use for a given task.

Naming Consistency1/5

Naming is chaotic: some tools use descriptive phrases with underscores (gazette_deceased_estates, polymarket_arbitrage), others use generic verbs (remember, recall, forget), and some include version or mode indicators (ask_pipeworx_beta, scan_competitor_ai_presence). No consistent pattern across the set.

Tool Count2/5

With 36 tools, the server is overstuffed for its purported focus on the UK Gazette. The majority of tools (Polymarket, Pipeworx general, AI visibility, etc.) are unrelated to the server's name, making it feel like a bundling of many services into one, which is excessive for a coherent tool set.

Completeness2/5

For a server named 'Uk Gazette', there are only a handful of Gazette-specific tools (gazette_search_notices, gazette_insolvency_notices, etc.), while the rest cover unrelated domains. This leaves obvious gaps for Gazette-related tasks (e.g., no tool for searching particular notice types or filtering by edition), despite the large tool count.

Resources