"OpenStreetMap" matching MCP tools:

• openstreetmap_search_placesA

  openstreetmap-mcp-serverA

  Convert a place name or address to geographic coordinates and structured place data via Nominatim/OpenStreetMap. Accepts either a free-form query string (e.g., "Space Needle Seattle") or structured address fields (street, city, state, etc.) — the two modes are mutually exclusive. Returns results ordered by Nominatim relevance (importance score). Use countrycodes to restrict results to specific countries. For exhaustive POI lists in an area, use openstreetmap_query_nearby or openstreetmap_query_bbox instead — Nominatim search returns best matches, not all matching objects. Results are matched on name and address relevance, never on an OSM attribute tag: extratags decorates whichever object matched and cannot select one, so a named feature may resolve to a different OSM object than the one carrying the tags you want. To filter or enumerate by tag (surface, sac_scale, ele, access, amenity), use openstreetmap_query_nearby, openstreetmap_query_bbox, or openstreetmap_query_raw.

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• summarize_for_citationA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use right before you QUOTE a DC Hub figure to a human — it returns one paste-ready attribution line for the value you are about to cite, with the CORRECT licence for that layer. Pass what you read off the response you are citing: subject (what the figure is), as_of (the provenance as_of), url (the row's profile_url or dcpi_url), completeness (the completeness flag), and layer. ★ LICENCE IS PER LAYER AND THIS IS THE POINT: DCPI scores, verdicts, band thresholds, methodology and DC Hub's own grid/site analysis are CC-BY-4.0 and yours to quote with attribution; the facility inventory and third-party physical layers are COMPOSITES whose upstream terms DC Hub cannot waive (parts are OpenStreetMap, ODbL 1.0, share-alike), so they carry a pointer to https://dchub.cloud/data-sources instead of a grant. A flat "CC-BY-4.0" over a facility record is an over-claim. Returns {citation_text, cite_as, license, license_basis, source, url, as_of, as_of_basis, completeness, omitted}. Free, no key, no network call — it assembles what you pass and never resolves or invents a value. If you omit as_of the line says RETRIEVED rather than claiming a data date, and tells you which field to pass next time. Do NOT use to look a figure UP (call the data tool first); this cites a figure you already have.

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• openstreetmap_lookup_objectsA

  openstreetmap-mcp-serverA

  Fetch address details for one or more known OSM objects by their IDs via Nominatim. Each ID must be prefixed with N (node), W (way), or R (relation), e.g., "N240109189", "W50637691", "R146656". Up to 50 IDs per call. Use when an OSM ID is already known from a prior openstreetmap_query_nearby or openstreetmap_query_bbox result — this is more efficient than a geocoding round trip to get the full Nominatim address record. The results are exactly the objects named in osm_ids: extratags decorates them and cannot select them, and there is no way to ask this tool for objects carrying a given tag. Discover such objects with openstreetmap_query_nearby, openstreetmap_query_bbox, or openstreetmap_query_raw, then pass their IDs here.

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• openstreetmap_query_bboxA

  openstreetmap-mcp-serverA

  Find OSM features within a rectangular geographic area (bounding box) via the Overpass API. Useful for area surveys where you want everything in a region, not proximity searches. Use amenity for common POI types (hospital, pharmacy, cafe, school, etc.) or tag_key + tag_value for other OSM categories (leisure=park, shop=supermarket, natural=peak). Exactly one of amenity or tag_key/tag_value must be provided. Every feature includes its full OSM tag set; the extratags flag (used by the Nominatim-backed openstreetmap_search_places, openstreetmap_reverse_geocode, and openstreetmap_lookup_objects tools) does not apply here. For proximity searches centered on a point, use openstreetmap_query_nearby instead.

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• openstreetmap_query_rawA

  openstreetmap-mcp-serverA

  Execute a raw Overpass QL query for advanced spatial queries that the convenience tools do not cover. Use for multi-type queries, union queries, relation membership, historical queries, or any operation requiring full Overpass QL expressiveness. The query must include [out:json]. Example: "[out:json][timeout:15];node[\"natural\"=\"peak\"](47.5,-122.5,47.7,-122.2);out body;" Returns one page of the result set: use limit and offset to page through it, and read totalFound and truncated to see how much the query matched. Validate complex queries at overpass-turbo.eu before use. For simple "what's near X?" or "what's in this area?" queries, use openstreetmap_query_nearby or openstreetmap_query_bbox instead.

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• compute_routeA

  TrustyDataA

  Calcule un itinéraire complet entre une origine et une destination sur le réseau France (OpenStreetMap), au choix en voiture, à pied ou à vélo : distance, durée, étapes de navigation en français, boîte englobante et géométrie encodée (polyline6). Mode de déplacement : `car` (voiture, défaut), `walking` (à pied) ou `cycling` (à vélo). Les valeurs du contrat d'API (`auto`, `pieton`, `velo`) sont acceptées telles quelles. Les consignes suivent le mode — « Conduisez… », « Marchez… », « Pédalez… » — et la réponse renvoie le `mode` utilisé : cite-le à côté de la durée, une durée sans son mode n'est pas interprétable. ⚠️ Chaque mode a sa distance maximale : 5 000 km en voiture, 500 km à vélo, 250 km à pied ; au-delà la réponse est un 422 explicite, pas une panne. Pour de simples distances en masse, préfère route_matrix. Plan minimum : Business. Mention OSM ODbL obligatoire.

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• openstreetmap_reverse_geocodeA

  openstreetmap-mcp-serverA

  Convert latitude/longitude coordinates to the nearest address or place name via Nominatim/OpenStreetMap. Returns the closest matching OSM object at the given coordinates. Note: Nominatim finds the nearest indexed OSM object — in dense areas this may differ from the address at the exact coordinate. Use zoom=18 for building-level accuracy, lower zoom values for coarser resolution (e.g., zoom=10 for city-level). The match is made on proximity and layer, never on an OSM attribute tag: extratags decorates the matched object and cannot select one. To find the objects in an area that carry a given tag, use openstreetmap_query_nearby, openstreetmap_query_bbox, or openstreetmap_query_raw.

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• geo-osmA

  geoagenticA

  Find nearby places on OpenStreetMap (cafes, ATMs, shops, parks, etc.). CRITICAL: The 'tags' argument MUST be passed as a single stringified JSON text block, NOT a nested JSON object. Example: "{\"amenity\":\"cafe\"}" USE FOR: - "Find a cafe near X" - "Are there any ATMs close to Y?" - "Show me supermarkets near Z" NOT FOR: directions, geocoding, Wikipedia, isochrones. EXAMPLE: User: "Find cafes near Koper station" → --lat 45.548 --lon 13.730 --tags '{"amenity":"cafe"}' --dist 300 COMMON TAGS: amenity: cafe, restaurant, atm, pharmacy, parking shop: supermarket, bakery, convenience tourism: hotel, museum, attraction

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• route_matrixA

  TrustyDataA

  Calcule une matrice de distances et durées entre N origines et M destinations (jusqu'à 25×25) sur le réseau France (OpenStreetMap), au choix en voiture, à pied ou à vélo. Sans géométrie ni consignes : pour un itinéraire détaillé, utilise compute_route. Mode de déplacement : `car` (voiture, défaut), `walking` (à pied) ou `cycling` (à vélo). Les valeurs du contrat d'API (`auto`, `pieton`, `velo`) sont acceptées telles quelles. ⚠️ La matrice plafonne à 1 500 km en voiture et à 200 km à pied comme à vélo — des limites plus basses que compute_route (5 000 / 250 / 500 km) : un trajet que compute_route accepte peut être refusé ici, en 422. Plan minimum : Growth. Mention OSM ODbL obligatoire.

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• forcedream_generate_sentimentA

  mcp-serverA

  Real, 14-source sentiment analysis -- not just an LLM's opinion. Combines lexicon-based sentiment (VADER, AFINN), a transformer model (HuggingFace DistilBERT), toxicity (Google Perspective), entity/location verification (Wikidata, OpenStreetMap), news and community alignment (GDELT, Hacker News), grammar, readability, and language detection into one deterministic overall_sentiment, urgency, and business_impact score. Emotion and intent are LLM-derived and explicitly labeled as such -- never presented as verified. SPENDS your balance -- requires authentication (OAuth).

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• focus_areaA

  ShowMeOnMapA

  Refine an existing layer to a NAMED area the server resolves from OpenStreetMap — a street corridor ("Broadway from Arbutus Street to Main Street"), a place ("Mount Pleasant"), or an intersection surroundings. Pass names only, never coordinates; unresolvable names return a clarification with real nearby candidates instead of guessed geometry. Emits ordinary undoable ops (filter / new layer + camera) and a grounded summary like "kept 37 of 150 features". Costs 1 credit when a mutation lands; clarifications are refunded.

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• openstreetmap_query_nearbyA

  openstreetmap-mcp-serverA

  Find OSM features within a radius around a geographic point via the Overpass API. The primary tool for "what's near X?" spatial queries. Use amenity for common POI types (hospital, pharmacy, restaurant, cafe, school, atm, etc.) or tag_key + tag_value for other OSM categories (leisure=park, shop=supermarket, natural=peak). Exactly one of amenity or tag_key/tag_value must be provided. Results include all element types specified (nodes cover standalone POIs, ways cover buildings and areas), each with its full OSM tag set, sorted nearest-first by distance_meters from the center point. The extratags flag is not needed here — it applies only to the Nominatim-backed openstreetmap_search_places, openstreetmap_reverse_geocode, and openstreetmap_lookup_objects tools.

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• geoA

  x402 Checker (AI Nock)B

  Paid $0.01 USDC exact on Base: forward geocode with q= or reverse with lat=&lon= via public Nominatim (OpenStreetMap). Attribution required. payTo 0x3903F05a17676566958B0a3E0c21E0bd49B66ea0.

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• summarize_for_citationA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use right before you QUOTE a DC Hub figure to a human — it returns one paste-ready attribution line for the value you are about to cite, with the CORRECT licence for that layer. Pass what you read off the response you are citing: subject (what the figure is), as_of (the provenance as_of), url (the row's profile_url or dcpi_url), completeness (the completeness flag), and layer. ★ LICENCE IS PER LAYER AND THIS IS THE POINT: DCPI scores, verdicts, band thresholds, methodology and DC Hub's own grid/site analysis are CC-BY-4.0 and yours to quote with attribution; the facility inventory and third-party physical layers are COMPOSITES whose upstream terms DC Hub cannot waive (parts are OpenStreetMap, ODbL 1.0, share-alike), so they carry a pointer to https://dchub.cloud/data-sources instead of a grant. A flat "CC-BY-4.0" over a facility record is an over-claim. Returns {citation_text, cite_as, license, license_basis, source, url, as_of, as_of_basis, completeness, omitted}. Free, no key, no network call — it assembles what you pass and never resolves or invents a value. If you omit as_of the line says RETRIEVED rather than claiming a data date, and tells you which field to pass next time. Do NOT use to look a figure UP (call the data tool first); this cites a figure you already have.

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• pois_nearA

  OverpassA

  Find OpenStreetMap points of interest (shops, amenities, businesses) near a location. Give a location either as latitude+longitude OR as a place name via "place" (e.g. place: "Göttingen, Germany" — auto-geocoded). For the category, pass a plain-English term ("bike rental", "pharmacy", "restaurant", "gas station", "ev charger", "hotel", "atm") or an exact OSM tag ("amenity=cafe", "shop=bakery"). Answers "find bike rental shops in <city>", "pharmacies near me", "restaurants around this point". Returns matching places with names, tags, and coordinates.

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• maps_geocodeA

  Google_mapsA

  "Geocode [address] via Google Maps" / "Google Maps coordinates of [place]" / "lat lng for [address] using Google" — convert an address to lat/lng + formatted address via Google Maps Platform. Premium-quality geocoding (highest accuracy for US addresses); requires Google Maps API key. Use when Mapbox/MapTiler/OpenStreetMap miss the address.

  ConnectorNo auth

• ourairports_find_airportsA

  ourairports-mcp-serverA

  Find airports within a radius of a latitude/longitude, ranked nearest-first by great-circle distance, each with its distance (km) and bearing (degrees true) from the query point. The grounding tool for "nearest airport to here" — pair it with a live aviation server to fetch weather or positions for the result. Takes a coordinate only: no geocoding, so resolve place names to lat/lon upstream first (e.g. an OpenStreetMap or Open-Meteo geocode tool). Closed airports are excluded unless include_closed is set. OurAirports is community-edited — not authoritative for flight operations.

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• find_nearby_pharmaciesA

  FineRxA

  Find the nearest pharmacy locations to a US ZIP code, per chain. ``zip`` is a 5-digit US ZIP. Optional ``chains`` is a comma-separated list of chain codes (e.g. "walgreens,publix"); default is all chains. ``limit`` caps locations per chain. Coordinates are OpenStreetMap-sourced (ODbL).

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• local-searchA

  ToolcallA

  Find local businesses and places by category and location: pharmacies, restaurants, hotels, plumbers, lawyers, gyms and 40+ more categories, or any name keyword. Pass what= plus city= (geocoded automatically) or lat=&lon= with a radius. Returns name, category, address, phone, website, opening hours and coordinates from OpenStreetMap. JSON response. [Paid: $0.02 USDC per call via x402 on Base; the calling client pays automatically.]

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• postcode_reportA

  Agentic House SearchA

  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.

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• usaspending_spending_by_geographyA

  usaspending-mcp-serverA

  Aggregate federal spending by state, county, or congressional district. Useful for per-capita analysis, regional comparisons, and mapping federal investment patterns. Geographic filters accept FIPS codes and 2-letter state abbreviations — NOT place names. Resolve place names to FIPS codes using a geocoding server (Census or OpenStreetMap) before applying location filters. Chain per-capita results with Census population data for meaningful comparisons.

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