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ChrisBrooksbank

UK Parliament MCP Server

get_division_details

Retrieve full UK Parliament division details, including debate title, vote counts, and how each member voted. Use a division ID to view individual voting records.

Instructions

Get full division details including how each member voted | Hansard, division, voting records | Use to see individual voting records for a specific division. Returns division with debate title, counts, and member voting records.

    Args:
        division_id: External ID of division (from get_debate_divisions).
        is_evel: Filter to EVEL (English Votes for English Laws) voters only.

    Returns:
        Division details with member voting records.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
is_evelNo
division_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses the return shape (debate title, counts, member voting records) and the EVEL filtering behavior, but 'Get' only implicitly signals read-only and it says nothing about error cases, ID type restrictions (Commons vs Lords), or rate limits.

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

Conciseness3/5

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

Front-loaded with the core purpose and keyword tags, but the return information is stated twice ('including how each member voted' and again as 'Returns division with... member voting records'), which is redundant given an output schema exists.

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?

The output schema covers return values, and the description supplies the two parameter semantics plus the ID provenance, which is what an agent needs to invoke it. The main residual gap is the absence of sibling differentiation for a family of near-identical division tools.

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 does: it explains that division_id is an external ID sourced from get_debate_divisions and that is_evel restricts results to English Votes for English Laws voters. This meaning is not derivable from the bare boolean/string schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Get full division details') and scopes it to individual member voting records within a division. However, it does not distinguish itself from closely related siblings such as get_commons_division_by_id or get_lords_division_by_id, leaving the agent to infer which one applies.

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

Usage Guidelines3/5

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

It gives a usage context ('Use to see individual voting records for a specific division') and points to get_debate_divisions as the source of the ID, which is a useful workflow hint. There are no when-not conditions and no comparison against the many sibling division-retrieval tools, so the guidance is implied rather than explicit.

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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