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Uk Parliament

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

TDQS

A4.9/5.0
Behavior5/5

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

Annotations include readOnlyHint and idempotentHint. Description adds technical details: embedding model BGE-base-en, cosine similarity, 500-char overlapping windows, 200K char limit, truncation flag, and that passages include offsets for verification.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Six sentences front-loaded with purpose. Each sentence adds distinct value: usage, benefits, pairing, technical details. No redundancy.

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?

No output schema but description explains return: 'top-N passages with character offsets and similarity scores.' Covers expectations and parameters.

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%. Description adds context: 'max ~200K chars' for text, natural-language query examples, default and range for limit. Adds meaning beyond 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 clearly states 'Semantic search INSIDE a fetched record.' It specifies the verb (search inside) and resource (a fetched record). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded.

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?

Explicit guidance: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' Also mentions alternative approach with ask_pipeworx_grounded.

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.

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