UK Parliament MCP Server

get_commons_divisions_grouped_by_party

Retrieve UK House of Commons division results grouped by party to analyze party voting patterns, unity, and rebellions. Filter by topic, date, member, or division number.

Instructions

Get House of Commons divisions grouped by party | party-line voting, how parties voted, rebellions | Use when analyzing how different parties voted on issues | Returns vote counts by party rather than individual members.

    Args:
        search_term: Optional: search term for division topics (e.g. 'brexit', 'climate', 'NHS').
        member_id: Optional: only divisions this member voted in.
        start_date: Optional: start date in YYYY-MM-DD format.
        end_date: Optional: end date in YYYY-MM-DD format.
        division_number: Optional: specific division number.
        include_when_member_was_teller: Optional: include divisions where the member was a teller.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`end_date`	No
`member_id`	No
`start_date`	No
`search_term`	No
`division_number`	No
`include_when_member_was_teller`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden and does so reasonably: it discloses the aggregation semantics (party-level counts, not individual member votes) and notes the teller-inclusion behavior of a parameter. It omits any note on pagination, result caps, or auth/rate characteristics.

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?

Front-loads the core purpose and usage in the first two clauses, then provides an Args block that is necessary given the 0% schema coverage. No filler sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need not be explained, yet the description still characterizes the aggregate output. All parameters are covered, making the definition sufficient for correct invocation; only edge-case filtering interactions are left implicit.

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 description coverage is 0%, so the description must compensate, and it documents all six parameters including the date format (YYYY-MM-DD), search term semantics with examples, and the meaning of include_when_member_was_teller. It could be stronger on how member_id and search_term interact or on defaults/optionality nuances.

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 ('Get House of Commons divisions grouped by party') and clearly delimits scope by explaining it returns 'vote counts by party rather than individual members,' distinguishing it from per-member sibling tools like get_commons_voting_record_for_member and get_lords_divisions_grouped_by_party.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear usage context ('Use when analyzing how different parties voted on issues') plus relevant analytical angles (party-line voting, rebellions). It stops short of explicitly naming competing siblings such as search_commons_divisions as alternatives to prefer for other 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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