UK Business Tools - Ledgerhall

Get Member Debates

law_parliament_member_debates

Read-onlyIdempotent

USE THIS TOOL WHEN you have a member_id and want contributions where THAT member used a specific topic phrase verbatim (text-body search).

CALL parliament_find_member(name) FIRST to obtain the integer member_id.

This is a name-based text-body search — it matches contributions whose TEXT contains the topic phrase. A member who spoke in a debate but didn't use your phrase verbatim is filtered out. For verbatim retrieval of every contribution by a member in a known debate (regardless of vocabulary), use parliament_get_debate_contributions(debate_ext_id, member_id=...) instead.

Each contribution's text field is capped at 3000 characters.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum contributions to return. Default 20.
`topic`	No	Optional phrase to find in THIS member's contribution text bodies. Hansard searches the words the member actually said, NOT the topic or title of the debate. Pass tokens this member would have spoken — distinctive arguments ('disproportionate sanction'), statutory references ('section 21'), or motion numbers ('Motion C1') — not the bill's name (members rarely say e.g. 'Renters\' Rights Bill' verbatim in their speeches). If you want 'every contribution this member made in a specific debate' regardless of words used, find the debate_ext_id then use parliament_get_debate_contributions(debate_ext_id, member_id=...).
`offset`	No	Number of contributions to skip before this page. Default 0. Re-call with offset=offset+returned while has_more is true.
`member_id`	Yes	Parliament Members API integer ID. Obtain from parliament_find_member.

Output Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Page size requested
`topic`	No	Topic phrase filter applied, if any
`total`	Yes	Number of contributions returned in this call
`offset`	No	Skip applied to this page
`has_more`	No	True if a full page was returned (more may exist)
`member_id`	Yes	Parliament Members API member ID
`contributions`	No	Hansard contributions for the member. Each `text` field is capped at 3000 characters.

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds valuable context: matches contributions whose TEXT contains the phrase, filters out speakers who didn't use the phrase, and caps text at 3000 characters. It also clarifies that topic searches the words actually spoken, not debate titles—beyond what annotations convey.

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?

The description is front-loaded with a directive, then provides a prerequisite, matching semantics, an alternative, and a truncation note—each sentence earns its place. It is appropriately sized for a tool with nuanced search behavior and contains no repetition of schema details that are already obvious.

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?

Combined with the rich schema (which covers pagination via offset/limit/has_more) and an output schema, the description covers the essential context: prerequisite lookup, text-search semantics, behavioral constraints, and when to use an alternative. Nothing critical is missing for an agent to correctly invoke this tool.

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 100%, but the description adds significant semantic meaning to the 'topic' parameter with concrete examples ('disproportionate sanction', 'section 21', 'Motion C1') and a warning against using bill names. This is practical guidance that prevents misuse and goes well beyond the schema description.

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?

The description opens with 'USE THIS TOOL WHEN you have a member_id and want contributions where THAT member used a specific topic phrase verbatim (text-body search),' which is a specific verb+resource+scope statement. It distinguishes itself from sibling law_parliament_get_debate_contributions by contrasting the text-body search approach.

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?

Explicit guidance says to call parliament_find_member FIRST, and provides clear exclusions: 'For verbatim retrieval of every contribution by a member in a known debate (regardless of vocabulary), use parliament_get_debate_contributions(debate_ext_id, member_id=...) instead.' This tells both when and when not to use the tool.

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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TDQS

A4/5.0

Disambiguation4/5

Tools are well-grouped by domain prefixes (dd_, gov_, law_, prop_) with clear descriptions that differentiate them. However, there is minor overlap, e.g., dd_search could be used instead of individual searches, and dd_fetch versus dedicated profile tools might cause confusion.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with domain-specific prefixes (dd_, gov_, law_, prop_). Names are descriptive and predictable, e.g., dd_charity_search, gov_govuk_search, law_bills_search_bills.

Tool Count4/5

70 tools is high but justified by the broad scope covering due diligence, government, legal, and property domains. Each domain has a reasonable number of tools (about 15-20 each). The count is on the upper end but still manageable.

Completeness5/5

The tool set is comprehensive across all domains: full CRUD for companies and charities, detailed legal research (cases, legislation, parliament, citations), property data (EPC, planning, price paid, rentals), and government information. No obvious gaps for the intended use cases.

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