Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Search UK electronics products across retailers: prices, stock and specs, refreshed twice a day.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
TRUSTRAILS_API_KEYAls geheim markierte Werte sind Schlüssel zu deinem Konto. Gib sie nie weiter und füge sie nie in einen Chat ein.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"server": {
"command": "npx",
"args": [
"-y",
"@trustrails/mcp-server"
],
"env": {
"TRUSTRAILS_API_KEY": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add server --env TRUSTRAILS_API_KEY=your-value -- npx -y @trustrails/mcp-serverTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"server": {
"command": "npx",
"args": [
"-y",
"@trustrails/mcp-server"
],
"env": {
"TRUSTRAILS_API_KEY": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"server": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@trustrails/mcp-server"
],
"env": {
"TRUSTRAILS_API_KEY": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde über die Domain trustrails.app bestätigt (die Registry prüft, wem die Domain gehört)
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Das musst du eintragen: TRUSTRAILS_API_KEY
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (1.0.28) kam vor 27 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
Search UK electronics products - compare prices, find deals, and discover products across multiple retailers.
Built for the Model Context Protocol (MCP) - works with Claude Desktop, Claude Code, and other MCP-compatible AI assistants.
No installation needed — just add TrustRails to your Claude config.
For Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": { "trustrails": { "command": "npx", "args": ["-y", "@trustrails/mcp-server"], "env": { "TRUSTRAILS_API_KEY": "mcp-public-2026" } } } }
For Claude Code (~/.config/claude/config.json):
{
"mcpServers": { "trustrails": { "command": "npx", "args": ["-y", "@trustrails/mcp-server"], "env": { "TRUSTRAILS_API_KEY": "mcp-public-2026" } } } }
That's it! Restart Claude and start searching.
Just ask Claude naturally — it will decompose your request into the right query and filters:
"Find me a gaming laptop under £1000"
"I need Sony noise cancelling headphones" "What HP laptops are available between £500-£700?" "Show me Anker chargers"
Claude will search across multiple UK retailers and show you:
attributes), so products can be compared from the search aloneget_product for your final 1-3 picks, only when you need what search lacks: the retailer's description, or every retailer's offer and buy link (search carries only the best offer's purchase_url, cheapest in stock)search_productsSearch 26,000+ UK electronics products across 7 retailers with price comparison. Prices are in GBP. The tool text tells the assistant to split the request into filters first: brand, category and price go in their own arguments, RAM, storage, screen size, resolution, refresh rate, wattage and Wi-Fi generation go in constraints, and only what is left over goes in query.
Parameters:
query (string, optional) - Refinement terms ONLY: model lines, series, variants, model numbers (e.g. 'neo', 'ultra', 'oled', 'WH-1000XM5', 's25 ultra'). Never a category name (BAD query='tablet', query='smartwatch', query='laptop': set the category filter instead), never a brand name (BAD query='Sony': set the brand filter instead), never a price. Omit entirely when browsing a category or brand: 'show me tablets' = category='Tablets', no query. Query words must appear in the title, except words naming the category ('router' in Networking) and bare numbers or specs ('4070', '16GB'), which are ignored when finding products and only rank them. Put a model number with its prefix ('RTX 4070', not '4070') and check each title for it. Leave out use-case words like gaming, cheap or best.min_price (number, optional) - Minimum price in GBP.max_price (number, optional) - Maximum price in GBP.brand (string, optional) - Filter by brand name (exact match, case-insensitive). Examples: Apple, Samsung, Sony, HP, Dell, Lenovo, Anker, Bose, LGcategory (string, optional) - Filter by product category. Use ONLY these exact values: Laptops, Desktops, Tablets, Phones, TVs, Monitors, Headphones, Speakers, Cameras, Keyboards, Mice, Printers, Networking, Storage, Gaming, Wearables, Drones, Audio, Cables & Chargers. 'Smartphones' is not valid (use 'Phones'), nor is 'Televisions' (use 'TVs'). Gaming headsets are category='Headphones', query='gaming headset': the Gaming category is consoles, controllers and accessories only.constraints (object, optional) - Hard spec requirements, checked per product against its attributes. Shape {name: {op: number}} with op eq, gte or lte; a range is {gte, lte}. On every operator, storage matches within 3% and screen size within 0.5 inch; a whole-number screen size N also covers up to N+1 on eq and lte (gte is unchanged); other specs exactly: gte 1024 accepts a 1TB drive, eq 22 a 21.5" screen, eq 13 a 13.6" one, lte 15 a 15.6" one. Example: {"memory_gb": {"gte": 24}, "storage_gb": {"gte": 1000}, "screen_in": {"eq": 15}}. Names and units: memory_gb (RAM, GB), storage_gb (GB, 1TB = 1000), screen_in (inches), resolution_p (pixels high: 4K = 2160, QHD = 1440, Full HD = 1080), refresh_hz (Hz), power_w (W), wifi_gen (Wi-Fi generation: 6, 6E = 6.5, 7). A spec written in query counts only when it says what it is ('24GB RAM', '1TB', '144Hz', '55"') and means at least, except screen size (that size); a bare '24GB' stays a search word. Explicit constraints win over it. A plain 16GB or 65W is gte; use eq only when the user says exactly, and for screen size. Always set category (and brand if known) with constraints: a search of only specs is rejected, as is a bad request (unknown name or operator, negative value, a combination nothing can meet such as gte above lte); the error lists the valid ones. See Structured specs.lite (boolean, optional) - Return trimmed product objects with only essential fields (id, title, brand, price, currency, availability, image_url, purchase_url, offer_count, attributes (every known spec as {status, value}, without sources) and, with constraints, constraint_status). Always set to true; it still carries attributes. Use false only for ean, category, provenance or all sources at once.limit (number, optional) - Maximum number of products to return (default 50, max 100)sort (string, optional) - Sort order: 'relevance' (default), 'price_asc' (in stock first, then cheapest), 'price_desc' (in stock first, then most expensive). With constraints, matched products still come first.Returns: products and a total. Each product carries every known attributes spec (a product with no attributes key has none of these seven specs known: say so only if the user asked about one): compare specs from these. State the value of a confirmed or inferred spec; only conflicting (retailers disagree) or a missing name (unknown) needs a caveat. With constraints, each product has constraint_status per name (matched: a retailer's title states a value that meets it; unverified: not known to meet it, so never treat it as a match), total counts the products that match every constraint, unverified_total the unverified ones that passed the other filters, and excluded_by_constraints the products whose stated value fails. candidates_truncated: true means the first 2,000 candidates in the chosen sort order were checked and more exist: add a brand or category, or a narrower query, and search again. availability is in_stock, low_stock, out_of_stock or unknown (the retailer gave no stock signal), and with purchase_url and price it comes from the product's best offer: in stock first, then cheapest. If offer_count is above 1, call get_product for your final 1-3 picks to compare retailers. Before claiming a saving, read offers[].title (from get_product): they must name the same model; if they differ, don't claim one. Before recommending any pick, check its title is the product type asked for (not a cable, accessory or adapter).
get_productGet full details for a single product by ID. Returns structured attributes (see Structured specs), specs.description (the retailer's own prose, for processor, GPU, ports, weight, battery and features that attributes does not cover), pricing, availability, delivery time, and all retailer offers with per-retailer pricing (offers[].stock is a unit count only when the retailer supplies one, otherwise null). Accepts both canonical product IDs and original retailer offer IDs. Use after search_products only for your final 1-3 picks, and only when you need what search lacks: details only the description has (if it doesn't give a detail, tell the user it isn't listed), or every retailer's offer and buy link (search carries only the best offer's purchase_url, cheapest in stock). Search results already carry attributes to compare specs. A pick with offer_count 1 needs no call unless the user asked for a description-only detail.
Parameters:
product_id (string) - The unique product ID from search resultsReturns: Complete product with structured attributes, specs (including the retailer's specs.description), offers sorted in stock first then cheapest, and provenance information
Search results and get_product return attributes: structured specs (RAM, storage, screen size, resolution, refresh rate, power, Wi-Fi generation) read from retailer titles when the catalogue is imported. Each has a status, and a spec nobody states is absent (unknown); a product with no attributes key has none of these seven specs known (say so only if the user asked about one). What each call returns:
| Call | Returns |
|---|---|
search_products with lite=true (what agents should always set) | id, title, brand, price, currency, availability, image_url, purchase_url, offer_count, every known attribute as {status, value} (or {status, values} when conflicting) without sources, and constraint_status when constrained |
search_products without lite | the lite fields plus ean, category, product_type, provenance and every attribute with its sources |
get_product | everything, including specs.description and dimensions, offers and delivery_time |
Names and units are as in constraints. State the value for confirmed and inferred; only conflicting or a missing name needs a caveat. The statuses:
confirmed: two or more retailers state the same value.inferred: one retailer's title states it.conflicting: retailers state different values. None is picked; the values are listed with their retailers, and a retailer can appear under two values (check offers[].title).specs.description is the retailer's prose. It can describe another configuration or a maximum ("up to 32GB"), so it never overrides or fills in an attribute: a spec missing from attributes is unknown.
For one Galaxy S26 Ultra, where only JoyBuy states the RAM (12GB) and two retailers disagree about the storage:
{
"memory_gb": { "status": "inferred", "value": 12, "sources": [{ "retailer": "JoyBuy", "field": "title" }] }, "storage_gb": { "status": "conflicting", "values": [ { "value": 256, "sources": [{ "retailer": "Laptops Direct", "field": "title" }] }, { "value": 512, "sources": [{ "retailer": "JoyBuy", "field": "title" }] } ] } }
Searches with constraints return the constraints applied, excluded_by_constraints, unverified_total, and a constraint_status on each product: matched (a retailer's title states a value that meets the constraint) or unverified (not known to meet it: never treat as a match, tell the user it is unconfirmed; check attributes[name], where conflicting means retailers disagree and missing means unknown). Products whose stated value fails a constraint are left out. A lite result carries the status and value of every known spec, without sources, so a 64GB laptop is told apart from a 16GB one and disagreeing from unknown.
eq and lte (eq 13 acNur der Anfang wird gezeigt; den Rest findest du im Repo