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Trustrails MCP

by james-webdevversion 1.0.28i★ –iRuns on your computer
Registry nameapp.trustrails/server

Install

Overview

Description from the maker (original English)

Search UK electronics products across retailers: prices, stock and specs, refreshed twice a day.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory
  • You need to fill in: TRUSTRAILS_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: TRUSTRAILS_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "server": {
      "command": "npx",
      "args": [
        "-y",
        "@trustrails/mcp-server"
      ],
      "env": {
        "TRUSTRAILS_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add server --env TRUSTRAILS_API_KEY=your-value -- npx -y @trustrails/mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "server": {
      "command": "npx",
      "args": [
        "-y",
        "@trustrails/mcp-server"
      ],
      "env": {
        "TRUSTRAILS_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "server": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@trustrails/mcp-server"
      ],
      "env": {
        "TRUSTRAILS_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • TRUSTRAILS_API_KEY secret env
    Optional TrustRails API key. If unset, the server uses the shared public key, which is rate-limited.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified against the domain trustrails.app (the registry checks domain ownership)

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7234 servers in this category
  • Published to the registry more recently than 57% of the other 7234 servers in this category
  • Basic checks equal to or better than 100% of the other 7234 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Trustrails MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: TRUSTRAILS_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.28) reached the registry 27 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Codebase Memory

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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_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)

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, LG
  • category (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_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:

CallReturns
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 litethe lite fields plus ean, category, product_type, provenance and every attribute with its sources
get_producteverything, 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.

  • 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 eq and lte (eq 13 ac

Only the beginning is shown; read the rest in the repo

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