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ppd_transactions

Search UK Land Registry property transactions by postcode, address, date range, or price to analyze specific property histories and market trends.

Instructions

Search Land Registry transactions by postcode, address, date range, or price.

Use for specific property history ("what has 10 Downing Street sold for?") or filtered market queries ("all sales over 500k in SW1 last year").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
postcodeNoUK postcode (e.g. "SW1A 1AA") - required for postcode search
streetNoStreet name for address-based search
townNoTown name for address-based search
paonNoPrimary address (house name/number) for address-based search
from_dateNoStart date filter (ISO format, e.g. "2023-01-01")
to_dateNoEnd date filter (ISO format)
min_priceNoMinimum price filter in £
max_priceNoMaximum price filter in £
property_typeNoFilter by type: F=flat, D=detached, S=semi, T=terraced
limitNoMax results to return (default 25)
Behavior3/5

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

With no annotations provided, the description carries full burden. It indicates this is a search/read operation but doesn't disclose behavioral traits like rate limits, authentication requirements, data freshness, or pagination behavior beyond the 'limit' parameter. The description adds some context about search types but lacks operational details.

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?

Two sentences, zero waste. First sentence states purpose and search dimensions. Second sentence provides concrete usage examples. Every sentence earns its place with front-loaded information.

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

Completeness3/5

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

For a 10-parameter search tool with no annotations and no output schema, the description provides good purpose and usage guidance but lacks information about return format, result structure, error conditions, or data limitations. The examples help but don't fully compensate for missing behavioral context.

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 the schema already documents all 10 parameters thoroughly. The description mentions the search dimensions (postcode, address, date range, price) but doesn't add meaningful semantics beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 clearly states the verb ('Search') and resource ('Land Registry transactions') with specific search criteria (postcode, address, date range, price). It distinguishes from siblings like property_epc or rental_analysis by focusing on transaction history rather than energy ratings or rental data.

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?

The description provides explicit usage scenarios with concrete examples: 'Use for specific property history ("what has 10 Downing Street sold for?") or filtered market queries ("all sales over 500k in SW1 last year").' This gives clear guidance on when to use this tool versus alternatives.

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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