Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@brmarket-mcp search for iPhone 14"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for Brazilian marketplace product search, paid automatically with x402 micropayments (USDC on Base or Solana).
One call searches Mercado Livre, Shopee and AliExpress together and returns a
single normalized shape: BRL prices in integer cents, canonical + affiliate links,
and a per-marketplace sources state so a degraded marketplace is never silent.
A free import-tax calculator turns a price into the real landed price — the
honest "best value with Brazilian import tax" comparison.
Status: not published yet.
brmarket-mcp@0.1.0is built and ready; publishing to npm and the MCP registry is a manual step. See Publishing below.
Tool | Price | What it does |
| $0.01 | Mercado Livre + Shopee + AliExpress in one call. Normalized BRL prices (integer cents), canonical + affiliate links, free shipping, rating, sold count, seller id/name/official-store, |
| $0.05 | Same, but from a product photo. Returns |
| free | One result, canonical link only, no affiliate link. |
| free | Brazilian import tax + landed price of a cross-border purchase (Import Tax 0% ≤ US$50 else 30% − US$20, ICMS por-dentro by state, Bacen PTAX rate). BRL cents or USD in; pass |
sourcesEvery response reports each marketplace as ok / error / timeout /
not_configured. A short result list may mean a marketplace was down, not that
the market is thin. Never present a degraded page as the whole market.
Related MCP server: mcp-brazil-marketplaces
npx brmarket-mcpClaude Desktop / Claude Code (claude_desktop_config.json):
{
"mcpServers": {
"brmarket": {
"command": "npx",
"args": ["-y", "brmarket-mcp"],
"env": {
"EVM_PRIVATE_KEY": "0x…"
}
}
}
}The free preview tool works with no wallet at all.
Variable | Required | Description |
| no |
|
| no | base58 or JSON-array secret key of a dedicated Solana wallet holding USDC. |
| no | RPC override used to build the Solana payment (e.g. a Helius URL). |
| no | Defaults to |
| no |
|
Configure either rail, or both — the x402 layer settles on whichever the server's 402 advertises. Without any key, paid tools fail with HTTP 402.
⚠️ These keys spend real money. Use dedicated wallets with small balances, never a personal or treasury key. Anything that can read this process' environment can spend from them.
npm install
npm run build # tsc -> dist/
npm run typecheck
npm run version:check # all FIVE version sites must agreenpm run version:check # wired into prepublishOnly too
npm publishThen verify the tarball from the registry, not the local build:
npm pack brmarket-mcp@<version> --registry https://registry.npmjs.org
tar -xzOf brmarket-mcp-<version>.tgz package/dist/index.js | grep -o 'version: "[^"]*"'A green build proves nothing about what was published — brdata-mcp 0.3.4 shipped
from a stale source copy and its MCP handshake reported 0.3.2 while npm said
0.3.4. Check the tarball.
For the MCP registry, server.json carries the full environmentVariables block.
Do not drop it: brdata-mcp 0.3.3 published without it and the registry entry lost
every variable.
MIT © Thiago Menzinger
FREE, deterministic. Estimates the Brazilian import tax on a cross-border purchase and the final landed price. Give the product value (and freight) EITHER in BRL integer cents (product_cents / freight_cents) OR in USD (product_usd / freight_usd) — not both — plus an optional buyer state uf. Returns the Import Tax (0% up to US$50, else 30% − US$20), the ICMS por-dentro by state, the total landed price, the USD rate used (Bacen PTAX) and the ruleset version. Rules follow MP 1.357/2026 and may change (~sep/2026); it is an estimate — the marketplace checkout is the source of truth. No payment.
| Name | Required | Description | Default |
|---|---|---|---|
| uf | No | Buyer's 2-letter Brazilian state (e.g. SP). Default: 20% ICMS ceiling. | |
| freight_usd | No | Alternative: freight in USD. | |
| product_usd | No | Alternative: product value in USD (e.g. 59.90). | |
| freight_cents | No | Freight in BRL cents (part of the customs base). | |
| insurance_usd | No | Alternative: insurance in USD. | |
| product_cents | No | Product value in BRL cents (e.g. 30000 = R$300). Use the *_cents fields OR the *_usd fields, not both. | |
| insurance_cents | No | Insurance in BRL cents, if any. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of behavioral disclosure. It transparently states that the tool is free and deterministic, explains the calculation rule (0% up to US$50, else 30% − US$20), notes the ruleset version and potential changes, clarifies it's an estimate with the marketplace as source of truth, and explicitly says 'No payment.' This goes beyond typical descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and efficiently structured. It front-loads key attributes ('FREE, deterministic'), then explains inputs, outputs, and caveats in a few sentences. Every sentence adds essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no output schema, no annotations), the description is remarkably complete. It specifies the return values (Import Tax, ICMS, total landed price, USD rate, ruleset version), the input alternatives, the legal basis, and the estimate's limitations. This fully compensates for the lack of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds meaningful context by clarifying the exclusive grouping of BRL cents vs USD fields and specifically names product_cents/freight_cents and product_usd/freight_usd, complementing the schema descriptions. It also highlights the optional 'uf' state. This adds value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it estimates Brazilian import tax and final landed price for cross-border purchases. The verb 'estimates' is specific, and the resource (import tax/landed price) is clearly identified, distinguishing it from the sibling search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: it explains when to use the tool (for estimating import tax/landed price), how to provide inputs (BRL cents or USD, not both, optional state), and what outputs to expect. It does not explicitly mention alternatives because none are relevant (siblings are search tools), but it gives no misleading exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search from a product PHOTO: a vision model reads the brand, model and attributes off the image and the resulting query runs across all three marketplaces. Returns the same normalized offers as search_products, plus interpreted_query — check it: if the model misread the photo, that field is where you will see it. Paid ($0.05).
| Name | Required | Description | Default |
|---|---|---|---|
| uf | No | Buyer's 2-letter Brazilian state (e.g. SP), for the ICMS rate in landed-price estimates. Default: 20% ceiling. | |
| sort | No | Default price_asc (cheapest first). | |
| limit | No | Results in the merged page. Default 10, max 20. | |
| image_url | Yes | Public https URL of the product photo. JPEG/PNG/WebP, max 5MB. | |
| max_price | No | Maximum price in BRL. | |
| min_price | No | Minimum price in BRL (e.g. 199.90). | |
| marketplace | No | Restrict the search to these marketplaces. Default: all three. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it discloses the cost ('Paid ($0.05)'), the reliance on a vision model, the possibility of misreading ('if the model misread the photo'), and the behavior of returning interpreted_query. It could add more about failure modes or rate limits, but the disclosed traits are substantive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loads the core purpose in the first sentence, and every sentence adds value: what the tool does, how the output relates to search_products, a caveat about interpreted_query, and pricing. There is zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is moderately complex (vision model, paid, merged marketplaces) and has no output schema, so the description compensates by clarifying the return value (same as search_products plus interpreted_query) and the potential misread pitfall. It doesn't explain error handling or all return fields, but it is sufficient for a competent agent given the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description does not need to repeat parameter details. It adds some semantic context by explaining that the vision model reads 'brand, model and attributes' and the query runs across all marketplaces, but no parameter-specific guidance is given. The baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search from a product PHOTO') and the resource ('Brazilian marketplaces'), and distinguishes it from siblings by noting it returns 'the same normalized offers as search_products, plus interpreted_query'. This makes the tool's unique purpose obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when the user has a product photo, and it contrasts with search_products by noting the photo-based input and the added interpreted_query field. It does not explicitly state when not to use it or name an alternative, but the context is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Mercado Livre, Shopee and AliExpress in ONE call. Returns normalized offers (BRL price in integer cents, canonical + affiliate link, imageUrl, free-shipping flag, rating, sold count, seller id/name/official-store flag, dataAsOf). Every response carries per-marketplace sources state and data_as_of — always read it; a short list may mean a marketplace was down, not that the market is thin. Cheapest-first by default (of what we sampled). Paid ($0.01).
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | What to search for, e.g. 'iphone 14 pro'. | |
| uf | No | Buyer's 2-letter Brazilian state (e.g. SP), for the ICMS rate in landed-price estimates. Default: 20% ceiling. | |
| sort | No | Default price_asc (cheapest first). | |
| limit | No | Results in the merged page. Default 10, max 20. | |
| max_price | No | Maximum price in BRL. | |
| min_price | No | Minimum price in BRL (e.g. 199.90). | |
| marketplace | No | Restrict the search to these marketplaces. Default: all three. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses the paid cost ($0.01), the sampling limitation ('of what we sampled'), the need to read `data_as_of` / `sources` for freshness, and unit normalization (BRL integer cents). These go well beyond generic search behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the first sentence states the core capability, followed by output format details and key caveats. It is dense but each sentence earns its place, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters and no output schema, the description compensates by enumerating return fields (price, link, imageUrl, shipping flag, rating, seller info) and warning about data freshness and paid cost. It covers the essential context without needing to explain every parameter, since the schema does that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds no parameter-specific meaning beyond what is already in the schema. The output-focused details (e.g., integer cents) do not clarify parameter semantics. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Search') with a precise resource ('Mercado Livre, Shopee and AliExpress') and clear scope ('in ONE call'). It differentiates from siblings like search_by_image and calculate_import_taxes by emphasizing multi-marketplace search and normalized offer output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as the primary combined search tool, but it does not explicitly state when to use it versus alternatives. It provides contextual cautions (e.g., 'a short list may mean a marketplace was down') but no direct exclusions or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
FREE teaser: one result, canonical link only, no affiliate link. Use it to check that a query returns something before paying for the full page. No payment required.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | What to search for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It clearly states the tool returns one result, provides only a canonical link, excludes affiliate links, and requires no payment. This is useful context, though edge cases and error behavior are not covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences, front-loaded with 'FREE teaser,' and every sentence adds value: cost, output limitation, and use case. There is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter preview tool with no output schema, the description is complete. It covers purpose, usage timing, output constraints, cost, and implicitly contrasts with the paid sibling tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the single parameter q with a description ('What to search for') and minLength. The description adds no parameter-specific meaning, so the baseline of 3 applies due to 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'FREE teaser' that previews a product search, with the specific verb 'Preview' and resource 'product search.' It distinguishes itself from the sibling search_products by specifying one result and canonical link only.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use it to check that a query returns something before paying for the full page,' giving clear context for when to use it. It implies the alternative (full paid search) but does not explicitly name sibling tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0calculate_import_taxessearch_by_imagesearch_productssearch_products_previewEach tool has a clearly distinct purpose: preview vs full search, image-based search, and tax calculation. The overlap between preview and full search is intentional and clearly documented, so agents can easily choose the right one.
All tool names follow a consistent snake_case verb_noun pattern: search_products_preview, search_products, search_by_image, calculate_import_taxes. The naming clearly conveys the action and object.
With 4 tools, the set is well-scoped for the server's purpose: product search (text, image, preview) and import tax estimation. Each tool earns its place without unnecessary bloat.
The tool set covers the core workflows for searching and estimating taxes. Minor gaps exist, such as lack of explicit pagination or sorting controls, but these are workable and the primary use cases are fully supported.
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