Close Knit

它列出的 31 个工具

“只读”和“会改数据”是服务方自己标的。带下划线的参数是必填的。

1. assess_listing只读

   Assess listing

   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.

   postcode asking_price market rent_period bedrooms floor_area_sqm floor_area_sqft property_type tenure share_percent listing_url intent rate_previous

2. build_watch_spec只读

   Build watch spec

   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.

   name area_ids market max_price min_bedrooms property_type must_haves frequency intent rate_previous

3. describe_data只读

   Describe data

   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']).

   kind theme query page intent rate_previous

4. evaluate_custom_criterion只读

   Evaluate custom criterion

   Moved: use query(saved: "custom:<preset>"). Answers until 2026-11-30.

   preset_id criterion direction area_ids top_n intent rate_previous

5. find_areas只读

   Find areas

   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 pro

   market max_budget target_budget bedrooms priorities ranges budget_importance budget_must near_stations boroughs regions exclude_boroughs only_in_zones zone_soft_metres exclude_area_ids include_over_budget places liked_area_ids property_type top_n custom segment detail intent rate_previous

6. find_similar_areas只读

   Find similar areas

   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'.

   area_id cheaper cheaper_by_percent max_budget bedrooms property_type top_n intent rate_previous

7. get只读

   Get

   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:<digits>, school:<URN>, 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.

   ids select sections detail theme query period intent rate_previous

8. get_address_history只读

   Get address history

   Moved: use get(["uprn:…"], select: ["history"]). Answers until 2026-11-30.

   address_id intent rate_previous

9. get_area_evidence_pack只读

   Get area evidence pack

   Moved: use get([id], sections: ["pack"]). Answers until 2026-11-30.

   area_id address_id intent rate_previous

10. get_area_metric只读

    Get area metric

    Moved: use get([id], select: ["layers.<metric>"]). Answers until 2026-11-30.

    area_id metric_id period intent rate_previous

11. get_area_overview只读

    Get area overview

    Moved: use get([id]). Answers until 2026-11-30.

    area_id bedrooms market property_type detail intent rate_previous

12. get_commute只读

    Get commute

    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,

    area_id places intent rate_previous

13. get_flood_risk只读

    Get flood risk

    Moved: use get([pc | uprn | id], select: ["flood"]). Answers until 2026-11-30.

    area_id uprn postcode intent rate_previous

14. get_home_prices只读

    Get home prices

    Moved: use get(["uprn:…"]). Answers until 2026-11-30.

    property_ref intent rate_previous

15. get_listing_search_links只读

    Get listing search links

    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.

    area_ids market max_price min_bedrooms property_type outcodes_per_area intent rate_previous

16. get_market_depth只读

    Get market depth

    Moved: use get([id], select: ["market"]). Answers until 2026-11-30.

    area_id intent rate_previous

17. get_nearby_sales_summary只读

    Get nearby sales summary

    Moved: use get(["uprn:…"], select: ["nearby-sales"]). Answers until 2026-11-30.

    address_id intent rate_previous

18. get_places只读

    Get places

    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.

    near area category brand radius_m page page_size intent rate_previous

19. get_rent_levels只读

    Get rent levels

    Moved: use get([id], select: ["rents"]). Answers until 2026-11-30.

    area_ids bedrooms intent rate_previous

20. list_custom_presets只读

    List custom presets

    Moved: use describe_data(kind: "custom"). Answers until 2026-11-30.

    intent rate_previous

21. list_metrics只读

    List metrics

    Moved: use describe_data(). Answers until 2026-11-30.

    compact detail intent rate_previous

22. list_school_measures只读

    List school measures

    Moved: use describe_data(kind: "school"). Answers until 2026-11-30.

    intent rate_previous

23. match_areas只读

    Match areas

    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.

    profile top_n intent rate_previous

24. open_shared_search只读

    Open shared search

    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'.

    link top_n intent rate_previous

25. query只读

    Query

    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-<category>, 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'

    kind level scope fields filters group_filters sort page page_size standouts saved period release intent rate_previous

26. rank_areas_by_metric只读

    Rank areas by metric

    Moved: use query(saved: "metric:<id>"). Answers until 2026-11-30.

    metric_id period top_n order region level intent rate_previous

27. rank_schools只读

    Rank schools

    Moved: use query(kind: "school"). Answers until 2026-11-30.

    criteria pool area_id lat lng radius_km council region phase filter min_cohort limit intent rate_previous

28. rate_answer

    Rate answer

    Rate a Close Knit answer by its callId (none: the last answer): rating 1-5, what was good, what was bad or missing, the fix you would want. Use it for the last answer in a conversation; earlier ones go in the next call's rate_previous. It describes the answer, never the user. Stored for 90 days with the call's intent summary, tool, client name and the conversation's one-way tag, contact details and full postcodes removed, no IP address or user agent.

    call_id rating good bad fix

29. search_areas只读

    Search areas

    Moved: use search_places(q). Answers until 2026-11-30.

    q limit intent rate_previous

30. search_places只读

    Search places

    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.

    q query limit intent rate_previous

31. send_feedback

    Send feedback

    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. Other tools' intent summaries and answer ratings: 90 days.

    kind message rating tools client