Brazilayer MCP Server
OfficialRelated Servers
Alternatives to Brazilayer MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceAn MCP server for Brazilian company and public procurement data, enabling CNPJ lookup, company search, tender resolution, and more via paid USDC-based API calls.1544 npmMIT
- AlicenseAqualityDmaintenanceMCP Server for accessing 36 Brazilian public data sources and 1 agent, enabling AI agents to query government data on economy, legislation, transparency, judiciary, elections, environment, health, and more.6MIT
- AlicenseAqualityBmaintenanceDados brasileiros reais (CNPJ, CEP, taxas, CPF) via MCP, pagos em USDC por chamada (x402).5247 npm1MIT
- AlicenseNot gradedqualityDmaintenanceMCP server that connects AI agents to 28 Brazilian public APIs, providing tools to query government data on economy, legislation, transparency, judiciary, elections, environment, health, and more.MIT
- AlicenseAqualityBmaintenanceEnables LLMs to access Brazilian public data through MCP tools for querying CNPJ, CEP, banks, and national holidays.4MIT
- AlicenseNot gradedqualityFmaintenanceProvides 30 pay-per-call APIs (compliance, trade, safety, web, data) for AI agents via MCP with automatic USDC payment on Base.MIT
TDQS
Scored across 47 tools
The set has many tools with overlapping purposes: multiple CNPJ lookup variants (consultar_empresa, consultar_empresa_completo, situacao, obter_amostra) and several bundles (empresa_enriquecer, commodities_pacote, brasil_pacote) that duplicate individual tools. Detailed descriptions help, but an agent must still choose between granular and bundled options and among similarly named samples.
Most names use consistent snake_case with a domain prefix (cnpj_, mercado_, fundos_, commodities_), which makes grouping clear. However, the convention mixes Portuguese and English (trust_check vs cnpj_consultar) and some names are noun phrases without a verb, so not perfectly uniform.
47 tools is excessive for a single MCP server, even one spanning multiple Brazilian data domains. The large number of sample, bundle, and per-category tools increases selection burden and makes the surface feel heavy.
Coverage across CNPJ, central bank, commodities, market, funds, taxes, deforestation, procurement, integrity, and trust checks is broad, with free samples and bundles for many categories. Minor gaps exist (e.g., no generic CPF or update operations), but for a read-only data marketplace the surface is largely complete.