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Hansard Policy Position Summary (deterministic facets)

law_parliament_policy_position_summary

Read-onlyIdempotent

Get debate-level Hansard signals on any topic, with breakdowns by house, year, and section—so you can gauge parliamentary attention without reading every speech.

Instructions

USE THIS TOOL WHEN you want debate-level corpus signals on a topic — by_house, by_year, by_section breakdowns — without reading every contribution.

Aggregates Hansard debate-level signals on a topic. Pure counts — no LLM, no editorial labels. Sweeps /search/Debates.json with pagination (up to max_debates_scanned), then aggregates by_house, by_section, by_year, by_month, and top_debates from debate metadata. Also captures the corpus-wide envelope counts (total_contributions, total_written_statements, total_divisions, etc.) from /search.json for cross-section scope.

AFTER calling, pick a debate from top_debates and pass its debate_ext_id into parliament_get_debate_contributions to drill into who said what.

Note on member-level facets: Hansard's search API exposes debate metadata, not per-contribution member identifiers, at the corpus level. by_party and top_contributors are therefore omitted from this deterministic summary. To see who spoke in a specific debate, read hansard://debate/{debate_ext_id}/header for an ordered contribution index, or call parliament_member_debates for one named member.

This is the authoritative source for UK Hansard corpus-level signals.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`house`	No	Restrict to one House. Default 'both'.	both
`topic`	Yes	Phrase to find in Hansard contribution text bodies for the facet aggregation. Same semantics as parliament_search_hansard.query: tokens that appear in members' actual speeches, not bill titles or topic metadata. The aggregator sweeps top_debates[] returned by /search/Debates.json — those debates are matched on the phrase appearing in titles or contribution text, so passing a Bill title (e.g. 'Renters\' Rights Bill') usually works for THIS tool even though it wouldn't for member-level text search, because debate-level matching uses metadata in addition to body text.
`to_date`	No	End date (YYYY-MM-DD)
`from_date`	No	Start date (YYYY-MM-DD)
`max_debates_scanned`	No	Hard cap on debates sampled from /search/Debates.json to compute facets. Default 200 issues ≤4 upstream calls (take=50 each). Raise to 2000 (≤40 calls) for an exhaustive sweep on a heavily-debated topic. Hansard rate limit: 1000 req/5min.

Output Schema

TableJSON Schema

Name	Required	Description
`house`	Yes	House filter applied
`topic`	Yes	Phrase searched in Hansard
`by_year`	No	Counts of debates by sitting year, desc by year
`to_date`	No	End date filter applied
`by_house`	No	Counts of debates by house (Commons vs Lords)
`by_party`	No	Counts by party. ALWAYS EMPTY in this summary — Hansard's search API only exposes member identifiers at the per-debate level, not the corpus level. For party breakdown within one debate, read hansard://debate/{ext_id}/header. For one member's contributions across the corpus, use parliament_member_debates.
`from_date`	No	Start date filter applied
`by_section`	No	Counts of debates by Hansard section bucket (Chamber / Westminster Hall / Written Answers / Written Statements)
`top_debates`	No	Top 20 debates ranked by upstream relevance_rank, with debate_ext_id for hansard://debate/{debate_ext_id}/header drill-down. contribution_count is null in this preview shape (would require a secondary call per debate).
`total_debates`	Yes	Total distinct debates touching this topic (TotalDebates)
`debates_scanned`	Yes	Number of debates pulled from /search/Debates.json for the facet breakdown (≤ max_debates_scanned)
`total_divisions`	Yes	TotalDivisions upstream count. Non-zero → consider votes_search_divisions.
`top_contributors`	No	ALWAYS EMPTY in this summary — see by_party note. Use parliament_member_debates after picking a debate from top_debates.
`by_month_recent_12`	No	Counts of debates by YYYY-MM for the most recent 12 months in the sample, desc by month
`total_contributions`	Yes	Total contributions in Hansard matching topic+filters (TotalContributions)
`total_written_answers`	Yes	TotalWrittenAnswers upstream count
`total_written_statements`	Yes	TotalWrittenStatements upstream count

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Sep 21, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations: discloses that output is pure counts with no LLM or editorial labels, that it sweeps /search/Debates.json with pagination, the upstream rate limit (1000 req/5min), and explicitly why by_party/top_contributors are omitted. This is unusually informative behavioral context.

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?

Front-loaded with the trigger and core purpose, then organized into scoping, follow-up, and omission paragraphs. Some sentences (e.g. 'This is the authoritative source...') are promotional padding, but overall it is well structured for its complexity.

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 annotations covering safety, an output schema present, and 100% parameter coverage, the description fully equips an agent: what it computes, how it scopes, its cost profile, and the correct follow-up traversal to drill into speakers.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents house, topic, dates and max_debates_scanned, including the Bill-title matching nuance. The description reinforces the topic semantics but adds little parameter detail beyond what the schema already carries, making the baseline 3 appropriate.

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?

States a specific verb and resource (aggregates Hansard debate-level signals on a topic) and enumerates the exact facets produced (by_house, by_section, by_year, by_month, top_debates), which distinguishes it from sibling search/member tools.

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?

Opens with an explicit when-to-use trigger ('USE THIS TOOL WHEN you want debate-level corpus signals... without reading every contribution') and names the downstream alternative ('pass its debate_ext_id into parliament_get_debate_contributions to drill into who said what'), plus routes member-level questions to parliament_member_debates.

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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