Trustrails MCP
Resumen
Descripción del creador (original en inglés)
Search UK electronics products across retailers: prices, stock and specs, refreshed twice a day.
Los tres pilares de PopNx MCP
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
- Opinión de usuariosiAún no hay reseñasLas reseñas se abrirán junto con las cuentas en una etapa posterior
- Nuestras revisiones básicasi1 de 1 superadasRevisado con información pública; el código aún no se ha escaneado
- UsoiMuy nuevo↓ 111/sem · ★ –
Qué puedes hacer con él
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
- Conexión: Se ejecuta en tu computadora
- Transporte: stdio
- Categoría: Búsqueda y memoria de IA
- Debes completar: TRUSTRAILS_API_KEY
Instalación
Elige la app de IA que usas y copia la configuración.
Antes de instalar
- Revisiones básicas: 1 de 1 superadas
- Se ejecuta en tu computadora con los permisos de tu usuario (archivos, red, apps).
- Debes completar tú:
TRUSTRAILS_API_KEY
Los valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
- npmNode.js instalado (para el comando npx)
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"server": {
"command": "npx",
"args": [
"-y",
"@trustrails/mcp-server"
],
"env": {
"TRUSTRAILS_API_KEY": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add server --env TRUSTRAILS_API_KEY=your-value -- npx -y @trustrails/mcp-serverCompleta los valores vacíos antes de ejecutarlo.
O copia la configuración tú
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"server": {
"command": "npx",
"args": [
"-y",
"@trustrails/mcp-server"
],
"env": {
"TRUSTRAILS_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
O copia la configuración tú
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"server": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@trustrails/mcp-server"
],
"env": {
"TRUSTRAILS_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Valores que debes completar
- Optional TrustRails API key. If unset, the server uses the shared public key, which is rate-limited.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Qué puede hacer
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisiones básicasi
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 9 nov 2026
- ✓ Superada
El creador coincide con el repo
El nombre en el registro se verificó con el dominio trustrails.app (el registro comprueba quién es dueño del dominio)
- … Aún sin revisar
Sigue en mantenimiento
Todavía no hemos leído este repo en GitHub
- … Aún sin revisar
Tiene licencia
Todavía no hemos leído este repo en GitHub
- – No aplica
El endpoint responde
Este se ejecuta en tu computadora; no hay endpoint en internet
- – No aplica
Inicio de sesión con OAuth
Se ejecuta en tu computadora y usa ajustes locales en su lugar
- … Aún sin revisar
Sin instrucciones ocultas en las descripciones de herramientas
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con otros en Búsqueda y memoria de IAi
- Más estrellas que el 0% de los otros 5732 servidores de esta categoría
- Publicado en el registro más recientemente que el 56% de los otros 5732 servidores de esta categoría
- Revisiones básicas iguales o mejores que el 100% de los otros 5732 servidores de esta categoría
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Preguntas frecuentes
¿Es seguro Trustrails MCP?
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
¿Qué claves o códigos necesita?
Debes completar: TRUSTRAILS_API_KEY
¿Se ejecuta en mi computadora o en la nube?
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
¿Sigue en mantenimiento?
El repo de GitHub cambió por última vez –, y la última versión (1.0.28) llegó al registro hace 30 días.
¿Qué apps pueden usarlo?
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
¿Qué puedo usar en su lugar?
En la misma categoría: Context7, Codebase Memory, Exa
Alternativas
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
README del creador
Abrir el README
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
TrustRails MCP Server
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.
Quick Start
No installation needed — just add TrustRails to your Claude config.
Configuration
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.
What You Can Do
Natural Language Product Search
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:
- Real-time prices & availability
- Purchase links (trustrails.app/go/ redirects to the retailer, through an affiliate link)
- Every known structured spec (
attributes), so products can be compared from the search alone - Then
get_productfor 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'spurchase_url, cheapest in stock)
Available Tools
search_products
Search 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 itsattributes. Shape{name: {op: number}}with opeq,gteorlte; 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 oneqandlte(gteis unchanged); other specs exactly:gte 1024accepts a 1TB drive,eq 22a 21.5" screen,eq 13a 13.6" one,lte 15a 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 inquerycounts 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 plain16GBor65Wisgte; useeqonly when the user says exactly, and for screen size. Always setcategory(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 asgteabovelte); 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_product
Get 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 results
Returns: Complete product with structured attributes, specs (including the retailer's specs.description), offers sorted in stock first then cheapest, and provenance information
Structured specs
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 (checkoffers[].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.
- Tolerance: storage within 3% (1TB = 1000–1024GB) and screen size within 0.5 inch, on every operator. Retailers round sizes down, so a whole-number screen size N also covers up to N+1 on
eqandlte(eq 13ac
Solo se muestra el principio; lee el resto en el repo