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Agentic House Search

by io.github.asturksever

UK postcode neighbourhood report

postcode_report
Read-onlyIdempotent

Full neighbourhood report for one UK postcode, assembled live from UK government open data.

Covers eleven categories: demographics (Census 2021), crime (data.police.uk), deprivation (IMD/WIMD/SIMD/NIMDM), property prices (HM Land Registry), broadband and 5G (Ofcom), noise (Defra), transport, amenities (OpenStreetMap), schools (DfE register: name, phase and distance only, no Ofsted grades) and planning constraints (Planning Data platform, Environment Agency).

Every figure carries the geography it describes (a postcode, an LSOA of ~1,500 people, or a whole local authority), the period it covers, and a benchmark where one exists. Read those: a crime count describes a 1 km square, and Ofcom mobile coverage describes an entire local authority, not the street.

Args:

  • postcode (string): UK postcode, spaces optional

  • categories (string[], optional): subset to fetch; omit for all eleven

  • include_sources (boolean): append publisher and licence per category (default false)

  • response_format ('markdown' | 'json'): default 'markdown'

Returns: Per category: a status, a one-paragraph summary, the facts (label, value, band, benchmark, geography, period, source id) and any caveats.

Statuses that are normal rather than failures:

  • "out_of_coverage": the dataset genuinely does not cover this nation. Census tables are England & Wales; police.uk excludes Scotland; Planning Data, Defra noise and the DfE school register are England-only. The reason and the devolved equivalent are given.

  • "unavailable": a pre-built extract has not been generated for this area yet (noise, currently).

  • "partial": some sources answered and some did not; check notes.

Examples:

  • "What's SW11 1AA like?" -> postcode="SW11 1AA"

  • "Is the broadband any good at M1 1AE?" -> postcode="M1 1AE", categories=["broadband","mobile"]

  • "Crime and schools near LA23 1AA" -> postcode="LA23 1AA", categories=["crime","schools"]

Errors:

  • "No such postcode" with suggestions when the postcode does not exist

  • Terminated postcodes are reported with the year they were withdrawn

Not a survey, valuation or conveyancing search. Check anything decision-critical against the source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
postcodeYesA UK postcode, with or without a space, e.g. "SW11 1AA" or "m11ae"
categoriesNoWhich sections to fetch. Omit for all eleven. Fetching only what you need is faster and much smaller — e.g. ["crime","schools"] for a family-safety question.
include_sourcesNoAppend the publisher and licence of each dataset used (default false)
response_formatNo'markdown' to read, 'json' for the full structured factsmarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryYes
postcodeYes
categoriesYes
local_authorityYes

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint, openWorldHint, idempotentHint, and destructiveHint annotations. It discloses that data is assembled live, explains geographic scope caveats (e.g., crime describes a 1 km square, Ofcom coverage describes a local authority), and details normal statuses like 'out_of_coverage', 'unavailable', and 'partial'. It also describes error behavior, making the tool's operational boundaries transparent.

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 long but well-structured with clear sections (Args, Returns, Examples, Errors). Every section adds genuine value, from coverage caveats to status explanations, and the opening sentence immediately establishes the core purpose. Despite length, there is no wasted or redundant text.

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?

The description is remarkably complete for a tool of this complexity. It covers the 11 categories, data sources, geographic benchmarks, normal vs. error statuses, parameter behavior, and provides concrete examples. Combined with the output schema presence, the agent has everything needed to invoke and interpret the tool correctly.

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?

The input schema already documents all four parameters with descriptions, including enums and defaults (100% coverage). The main description's Args section mostly repeats the schema rather than adding new semantic meaning. The examples add practical usage context but do not materially deepen parameter understanding beyond what the schema already provides.

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 a specific purpose: 'Full neighbourhood report for one UK postcode, assembled live from UK government open data.' It distinguishes itself from siblings like postcode_compare, postcode_lookup, and postcode_get_dataset by emphasizing the comprehensive, single-postcode report nature.

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?

The description gives clear context for when to use the tool, including natural-language examples and the ability to limit categories. It also states exclusions ('Not a survey, valuation or conveyancing search') and warns users to check decision-critical data against the source. However, it does not explicitly name sibling tools as alternatives, so it stops short of full 5-level guidance.

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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: compare does side-by-side multi-postcode analysis, report does a single-postcode deep dive, lookup resolves geography, search_datasets finds datasets, and get_dataset retrieves a full dataset entry. The descriptions explicitly cross-reference when not to use each tool, eliminating any ambiguity.

Naming Consistency5/5

All tool names follow a consistent pattern: lowercase snake_case with a 'postcode_' prefix followed by a verb_noun (compare, get_dataset, lookup, report, search_datasets). No mixed conventions or vague verbs like 'process' or 'execute'.

Tool Count5/5

At 5 tools, the set is tightly scoped for a UK house-search data server. Each tool covers a distinct operation (lookup, report, compare, dataset search, dataset details) and neither feels redundant nor bloated. This is well within the ideal 3-15 range.

Completeness5/5

The tool set provides a full workflow: look up a postcode's geography, get a comprehensive neighbourhood report, compare multiple postcodes, search for external datasets to fill gaps (e.g., EPC, flood risk), and retrieve full dataset details. There are no dead ends; every tool has a clear follow-up and the coverage matches the stated purpose.

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