Close Knit

Check one listing the user found against local evidence: asking price vs the same home type's recorded £/m² (with floor area or a bedroom-based ballpark) or median price, the LSOA then its MSOA (all types only as indicative-only), typicalPrice, a flood line, turnover, growth, the local authority's official HPI, costs (stamp duty on the asking price, the council tax by band), crime with its offence mix, noise, schools (state and independent counts), planning rules (home: conservation, Article 4, listed buildings), listed buildings recorded at or near the postcode (listedNearby), the IMD decile, and the questions to ask the agent (lease, service charge, EWS1, listing). Not a valuation.

Turn a finished search into a watch: portal searches per postcode district, filters, a state file path for de-duplication and a ready prompt for a scheduled task in the user's Claude that reports only new listings. Stateless: nothing is stored on Close Knit.

Rent prices in an area by bedrooms or home type (ONS Price Index of Private Rents, borough-wide): the average, London's, the trend and 24 months, rights on a rise (gov.uk), a page link. With current_rent: against the average; with current_since: indexedRent and a verdict on proposed_rent; levers, talking points, and links.report (the page with the tenant's figures, to send the landlord). Never one home's market rent.

The map of Close Knit's data; call it first, and whenever you are unsure of a metric, path or category name. No arguments: a summary (themes with metric counts and 2-3 headline metrics, Census in one line, places in one line, the entity kinds). theme='crime' lists that theme's metrics: unit, levels, period, coverage (England or London only), better end, what each measures and is not (name the metric you use; say when it differs from the question). query='trees' finds metrics with ranked within, source, own-row levels and England and region refs, paths, place categories and brands by words. kind='place' lists place categories with counts and brands; kind='school' the school measures; kind='lsoa' (or msoa, lad, rgn, pc, uprn, sector) the sections and select paths of that kind; kind='custom' the place presets; kind='ranking' the saved rankings for query(saved:); kind='guide' the site's guide articles, read with get(ids: ['guide:crime']).

For deep research: one search result by id as text with its page url to cite (an area: every section's headline facts, caveats, sources; a guide: its article). In chat prefer get.

Shortlist neighbourhoods in England (London in depth) for a household: budget, bedrooms, weighted priorities (crime, station access, price level, official deprivation decile, noise, disorder, energy rating, leasehold share, good primaries) and optional stations or local authorities to live near (regions narrows to English regions, e.g. ['london']; with no regions, stations or boroughs all of England is searched). Returns areas in one order (orderedBy): tier (every must met, then unverified), budget band (within, stretch, over, unknown), then standing 0-100 (with property_type every within-budget area first); present them in that order, never by score (mapScore, the map's colour). meets ('4 of 5') and aheadBecause explain it; budgetLabel says the budget in words ('within budget' only for the type's own figure); factor values with a typed fact (quality measured, modelled, estimate or unknown; range; verifyUrl), typicalPrice (the median recent sale), estimatedPrice and budgetFit (with property_type the type's own figure, priceBasis says which; else a ballpark for the bedroom count), postcode districts and website links. A deal-breaker needs must true (a range or place without it only ranks); tier unverified (mustStatus unknown, verified false, tierReasons) marks a must on an estimate, National Rail beside the routed lines or modelled minutes inside the margin: open the factor's fact.verifyUrl or check that journey on the web before calling it met; a must on a coverage gap leaves the area out (gapLeftOut). A ranked criterion an area has no data for (no crime is published for Greater Manchester) counts as half a fit and is named in that area's notScored: say so. Pass rejected areas in exclude_area_ids when refining after the user's feedback (LSOA, MSOA or local authority ids). The first tool for 'where should we live?'; query sorts by one metric only, match_areas scores a ready preference-profile.v2, search_places only turns names into ids. Answers big to small. value.levels holds up to 3 lists for the user's scope (London: boroughs, neighbourhoods, then value.areas; England: regions, local authorities, then areas). Each list ranks the whole scope at its own level and has at most 5 items. fits/total = areas in the group that meet every must within budget (share = fits/total in %). Lead with the level the user asked about, then drill down with boroughs=[id]. value.standouts names top areas inside groups that rank low: always mention one. value.nearby is one group just outside the scope that scores higher: one sentence at most. Each item's f holds facts as [value, rating 1-10 within factScope]. value.defs says once what each key counts, its unit, period, level, reference (ref, the scope median) and source. Quote a rating with its value and ref ('crime 8/10: 44 vs London 78 per 1,000'), never alone. Name a neighbourhood by near and outcode ('around Highams Park station, E4'). detail='full' adds every fact; get has the rest.

Neighbourhoods like one the user liked (crime, deprivation, income, price level and station access percentiles), optionally only cheaper ones (cheaper_by_percent lower £/m², crime no more than 10 points worse) and within max_budget for bedrooms: 'like this but cheaper / elsewhere'.

Everything about 1-10 things by typed id: uk:lsoa:E01…, uk:msoa:E02…, uk:lad:E09…, uk:rgn:E12…, sector:e17-4, pc:E17 4PP, uprn:, school:, place:osm:n123 (search_places finds ids from names), or a guide article, guide:crime, in a call of its own (describe_data(kind: 'guide') lists them): the page's text, how a figure is made and what it cannot say. No select: a summary and an index of sections. select takes plain dotted paths (safety.rate, layers.air-no2, flood, rents); an unknown path answers didYouMean and the valid paths. sections='all' gives every section's headline facts; sections=['layers'] the headline map layers (Census only with theme or query); detail='full' adds series by year. Two or more ids add a compare view: a row per fact with each id's value and who leads. An unknown id among others is its own entry in errors; the rest are answered. Each item has nav (up, inside, nearby, act) and the site page url. Figures equal the site's pages; flags as on the site.

Door-to-door minutes from one neighbourhood to the user's places on a weekday morning, by public transport (R5 on TfL timetables), bike (at the speed Santander Cycles riders reach; checked against TfL), car (typical morning traffic, no parking) and walking, each with a range. Places within 1.5 km of a commute hub (King's Cross, Bank, Canary Wharf, Oxford Circus…) are routed through it; others in Greater London get a straight-line estimate (estimate: true); places outside Greater London, and every place from a neighbourhood outside London (the routing is London only), get straight-line estimates in every mode, long trips at a main-line speed (estimate: true, reason 'outside-routing'). A neighbourhood served by National Rail only also gets a rail estimate (modes.rail, estimate: true). Example routes start at the neighbourhood's MSOA centre (origin); one far from the headline minutes is representative: false and lists its lines only. Journeys over 120 minutes are not timed (minutes null, capped: true). A place takes latitude/longitude or center [longitude, latitude] from search_places; quote estimate minutes as estimates. Each mode's confidence: 'timetable' (TfL), 'routed' (London streets) or 'approximate' (straight-line estimates). googleMapsUrl opens Google Maps' own route for the user to check: its time, leaving now, not ours.

Pre-filtered searches on Rightmove, Zoopla, OnTheMarket (and OpenRent for rentals) for the postcode districts of shortlisted areas, filtered by bedrooms, price and property_type (house, flat, bungalow). Links only: open them on the user's behalf to read listings; Close Knit itself never fetches or stores listings.

The places themselves (2.58 m: shops, gyms, cafes, parks, pubs, GPs, ... in 234 categories) near a typed id or {lat, lon} (radius_m 100-3000) or inside an area, by category (describe_data(kind: 'place') lists them) and brand (PureGym, Waitrose). Each: name, category, brand, metres, walk minutes (straight line, an estimate), its LSOA and sources. ODbL attribution; the full places.csv download link. Places of worship are never listed.

Score every area against a household's ordered criteria (preference-profile.v2: budget, ranges on any map layer, places, where zones, own criteria; must = filter). Returns a match-pack.v2 with the best top_n (default 20, max 100) areas as a list with score, rank and percentile per area, its top 2 reasons and 2 weakest criteria, the bottom 10, and per-criterion components (value, fit, in/edge/out) as evidence for the listed areas only. Whole-coverage counts (scored, unscored, excluded, and excludedBy per unmet must) cover every area; mapUrl opens the full map for the search. Set analyticsConsent true only if the user agreed to store an anonymised profile summary; the tool works identically with false. Takes a ready profile (built by you or exported from the site); for a conversational home search use find_areas, which builds the profile from plain answers and adds ballpark prices.

Open a search the user shared from the Close Knit site (a link with #profile=v1., v2. or v3.): their budget, priorities, places (gym, work, schools…), liked and ruled-out areas and private notes, re-ranked now with the same shortlist as find_areas. Use it when the user pastes such a link or says 'discuss my search'.

Analyse many areas or schools with a JSON query (never SQL). kind='area' (default): level lsoa, msoa, lad or rgn; scope england, london, a region or typed area ids; filters by value {metric, min, max}, percentile in scope {metric, pct_min}, vs the scope's reference {metric, vs: above}, change over N years {metric, change: {years, min}}, places inside {places: {category, brand, min}} or within_m; sort by a metric, a group stat {stat: median, metric} or deviation; above the metric's carrier level rows are groups: each metric is the group's own figure (its own row where the metric holds one at the level: crime, crime-, green-share, gross-yield; official where the publisher gives the council's or region's; else the weighted mean of all its areas; items[].method says which), with median, p25-p75, best tenth, count and fit share of the passing areas when asked, and group_filters filter them. defs say what each metric measures and is not, with England and region refs. kind='school' ranks schools (phase, ofsted, measure, near). saved='metric:' or 'custom:' runs a named ranking (describe_data(kind: 'ranking')). Pages of 10 (next.release flags moved rows) and a CSV download link. A London-only metric with an England scope is the coverage error with a hint; people metrics (Census) are never filtered or ranked.

Pass on the user's rating of the last Close Knit answer in this conversation, when they give one or agree to it: rating 1-5, what was good, what was bad or missing, the fix they want. It describes the answer, never the user. This stores the rating for 90 days with the call's tool name, client name and the conversation's one-way tag, contact details and full postcodes removed, no IP address or user agent. Close Knit's other data tools only read.

For deep research: areas, postcodes, schools, stations and parks (as their area) and guide articles by words; results [{id, title, url}], read one with fetch. In chat prefer find_areas, search_places, get, query.

Resolve a place the user mentions (station, borough, neighbourhood, postcode, street) to ids. Station lookup forgives spelling (Kings Cross, St James Park, Elephant and Castle) and knows London codes and nicknames (KX, TCR, LST, E&C, ally pally). Returns stations (osm:…, with TfL modes, lines and zone; twin stations such as Hammersmith are listed apart) for find_areas.near_stations, local authorities (uk:lad:…; London's boroughs) for find_areas.boroughs, areas (with ladId) and addresses for get (typed ids: uk:lsoa:…, pc:…, uprn:…) and assess_listing, and venues (a pool, gym or sports centre by name, with latitude/longitude to pin as a place). best is the likeliest meaning: a venue named exactly so, then a town, city or council named exactly so above a stop that only shares the name; 'Liverpool St' is the station. A street found only outside the town typed gives best null, reason town-mismatch and candidates with their towns: ask which. query is an alias of q.

Send feedback about Close Knit to the team: a bug, missing or wrong data, an idea, or what worked. Call it only after the user agrees: show them the message first. Describe the product and what happened, never the person: no names, contact details, addresses, family, budget or other personal circumstances. Kept 12 months, e-mail addresses and phone numbers removed; no reply channel: the answer gives an id, the changelog names ids fixed. Ratings sent with rate_answer: 90 days.