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Uk Parliament

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?

The description richly discloses behaviors beyond the readOnly/openWorld/idempotent annotations: expected latency (15-60s, up to ~90s for thorough), gap-recovery hops, contradictions[], hop fields, citation_uri resolvability constraints, semantic excerpting of large records, and the paid-plan requirement for thorough depth. No contradiction with 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 sentence earns its place: account requirements, alternative routing, core behavior, depth semantics, citation guarantees, and timing. The most critical routing information (account requirement, use ask_pipeworx instead) is front-loaded.

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 there is no output schema, the description fully covers what should be returned (findings packet, verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[]) and the operational envelope (latency, account tiers, excerpting). An agent has everything needed to invoke the tool correctly.

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% for both parameters, so the schema already documents syntax. The description adds decision-relevant meaning: the paid-plan gate on depth:'thorough', the behavioral consequences of each depth mode (gap recovery, contradiction scan), and latency implications tied to depth. This goes beyond simple restatement of the schema.

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 and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources... in ONE call'. It clearly distinguishes itself from open-web search ('this is NOT open-web search') and from sibling ask_pipeworx, so an agent can identify what this tool uniquely does.

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 when-to-use ('Best for broad/multi-part questions over structured data'), when-not-to-use ('For a single lookup use ask_pipeworx'), and a named alternative with the reason ('For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). Also gives account-tier routing: if not signed in, use ask_pipeworx instead.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-duplicates (ask_pipeworx and ask_pipeworx_beta are currently identical; ai_visibility_check vs scan_competitor_ai_presence overlap), and the massive mix of unrelated domains (Polymarket betting, general data lookup, AI visibility) alongside UK Parliament tools makes selection confusing. An agent would struggle to know whether to use ask_pipeworx, ask_pipeworx_beta, or ask_pipeworx_grounded, or which of the five polymarket tools fits.

Naming Consistency3/5

Most tools use snake_case with readable names, but the action placement varies (verb_noun like get_bill vs noun_verb like bet_research, entity_profile), and there are compound names like generate_llms_txt and scan_competitor_ai_presence. The style is mostly consistent but the verb_noun pattern is not uniform across the set.

Tool Count2/5

38 tools is heavy, and the overwhelming majority are unrelated to the server's stated UK Parliament purpose. Only 7 tools (get_bill, search_bills, bill_stages, get_member, search_members, search_hansard, recent_divisions) have anything to do with Parliament, making the count wildly inappropriate for the apparent scope.

Completeness2/5

The Parliament-specific surface is thin: basic bill/member/Hansard lookups exist, but there are no tools for specific divisions/votes, committees, publications, or detailed procedural information. The vast non-Parliament tooling is irrelevant, creating a dead end for any real Parliament research beyond the basics.

Resources