UK Parliament MCP Server

search_oral_questions

Find UK Parliament oral questions by answering department, asking member, or status; returns matching question details.

Instructions

Search oral questions | PQs, question time, oral PQs, minister questions | Search oral parliamentary questions (not EDMs) | Returns list of oral questions with details See also: search_oral_question_times, search_written_questions.

    Args:
        answering_body_id: Filter by department/body answering (0 for all).
        asking_member_id: Filter by member asking (0 for all).
        question_status: Filter by status, e.g. 'ToBeAsked', 'Withdrawn', 'Unstarred', 'Transferred', 'Submitted' (empty for all).
        skip: Number of results to skip.
        take: Number of results to return.

    Returns:
        List of oral questions matching the filters.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`skip`	No
`take`	No
`question_status`	No
`asking_member_id`	No
`answering_body_id`	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

A4.3/5.0

Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. 'Search' and 'Returns list of oral questions' strongly imply a read-only query, but the description never confirms safety, permissions, or pagination behavior despite zero annotation coverage.

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-loads purpose before Args/Returns, and the structure is easy to scan. The pipe-delimited synonym list ('PQs, question time, oral PQs, minister questions') is slightly redundant but earns its place for keyword matching.

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?

All five parameters are documented despite 0% schema coverage, siblings are referenced, and an output schema already covers return values. Nothing an agent needs to invoke the tool correctly is missing.

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

Parameters5/5

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

Schema coverage is 0%, yet the description compensates fully: it documents all five parameters, explains the '0 for all' sentinel for both ID filters, and supplies concrete enum values ('ToBeAsked', 'Withdrawn', 'Unstarred', 'Transferred', 'Submitted') for question_status. This adds substantial meaning beyond the bare 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?

States a specific verb (Search) and resource (oral parliamentary questions), and explicitly disambiguates from siblings: 'not EDMs' plus 'See also: search_oral_question_times, search_written_questions'. An agent can distinguish it from search_written_questions and EDM tools without opening either schema.

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 negative exclusion ('not EDMs') and routes to alternatives via 'See also'. However, it does not state positive conditions for when to prefer this over search_oral_question_times or search_hansard, leaving some inference to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server