UK Data for Agents

oral_questions_upcoming

Read-onlyIdempotent

List House of Commons oral questions tabled for answer at departmental question time from a given date: who is asking, which department and minister answers, and the question text, including topical questions. Answers "what will MPs ask at Home Office questions this week?". Source: UK Parliament Oral Questions and Motions API. Commons only; it lists questions, not what was said in reply (that is in Hansard). (cost: 1 credit)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "oral_questions_upcoming"
  }
}

Input Schema

Name	Required	Description
`take`	No	int, optional, number of questions 1-100, default 20
`to_date`	No	string, optional, YYYY-MM-DD, latest answering date
`from_date`	No	string, required, YYYY-MM-DD, earliest answering date

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 5, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent and open-world traits, and the description adds value beyond them: the upstream source (UK Parliament Oral Questions and Motions API), the Commons-only scope restriction, and the explicit exclusion of answers. The '1 credit' cost note is genuinely useful operational context. It stops short of describing ordering or pagination behavior.

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 core verb and resource, then scope limitations and cost. The single long opening sentence is dense but every clause carries real information; nothing is redundant.

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?

With no output schema, the description usefully enumerates what comes back (asker, answering department and minister, question text, topical questions), which is the key missing structured data. It omits ordering and whether historical dates are supported, a minor gap for a three-parameter list tool.

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 description coverage is 100%, so all three parameters are already documented in the schema and the baseline of 3 applies. The description only loosely anchors 'from a given date' to from_date and adds no meaning for take or to_date beyond what the schema states.

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: 'List House of Commons oral questions tabled for answer at departmental question time', and enumerates the returned facets (asker, department, minister, question text, topical questions). It also draws a hard boundary against Hansard replies, so an agent can distinguish it from the written-question siblings without opening a 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?

Provides a concrete use-case question ('what will MPs ask at Home Office questions this week?') and an explicit exclusion ('lists questions, not what was said in reply'). It does not, however, name the written-question siblings (e.g. written_question_search, unanswered_written_questions) as alternatives, so routing between oral and written questions is left implicit.

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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