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Companies House

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 1497 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 5,724 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?

Beyond the annotations (read-only, idempotent), the description discloses internal behavior: parallel decomposition, gap recovery, contradiction scanning, semantic excerpting, latency expectations, and the explicit 'never invented' gaps[] policy. It also clarifies the meaning of openWorldHint by stating this is not open-web search, preventing a possible misinterpretation.

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 every sentence carries actionable information—account requirements, sibling routing, what the tool returns, limitations, depth trade-offs, latency. It is front-loaded with the most urgent caveats (account and fallback). A 4 rather than 5 because the dense wall of text could be more scannable with bullets or section breaks, though nothing is redundant.

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?

Despite having no output schema, the description thoroughly explains the return shape (findings packet: evidence, confidence, source, fetched_at, citation, gaps[], contradictions[], hop) and the operational constraints (auth, latency, cost by depth). An agent has everything needed to decide, invoke, and interpret results.

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?

Schema coverage is 100%, but the description adds real semantic value: it explains that 'question' accepts broad natural-language questions, and it expands each depth value beyond the schema's one-liner with concrete behavioral differences (single hop, gap recovery, contradictions, paid tier, latency). This substantially helps an agent choose parameters correctly.

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 ('research'), an exact resource ('Pipeworx's 1497 STRUCTURED data sources... in ONE call'), and explicitly differentiates from both open-web search and the sibling ask_pipeworx. An agent can immediately understand what deep_research does and what it is not.

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 provides explicit when-to-use ('Best for broad/multi-part questions over structured data'), when-not-to-use ('For a single lookup use ask_pipeworx'), and even a scenario where a sibling is preferred ('BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). It also covers the signed-out fallback, leaving no ambiguity about selection.

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 overlapping jobs: ask_pipeworx_beta is currently identical to ask_pipeworx, ask_pipeworx_grounded is the same router with stricter extraction, and discover_tools/suggest_questions both serve discovery. Company-facing tools also overlap (entity_profile vs recent_changes vs compare_entities), so an agent could easily route a query to the wrong tool despite detailed descriptions.

Naming Consistency3/5

Names are mostly snake_case and grouped prefixes like get_*, ask_pipeworx*, and polymarket_* are readable. However, conventions are mixed across the set: some are verb_noun (search_companies), some are noun phrases (entity_profile, deep_research, recent_changes), and the Companies House family sits awkwardly beside unrelated Pipeworx and prediction-market families.

Tool Count1/5

With 36 tools, the server is already heavy, but only five tools actually serve the named Companies House domain. The other 31 belong to Pipeworx querying, memory, subscriptions, and Polymarket trading, which is a severe mismatch between the server's stated purpose and its actual surface.

Completeness3/5

For UK company data, the core surface is mostly covered: search, company profile, filings, officers, and PSCs. However, charges and official document retrieval are missing even though get_company links to them, and the unrelated tools do nothing to complete the Companies House domain.

Resources