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Mcp-Brasil

mcp-brasil

by Mcp-Brasil
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Python
Remote

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    • mcp-brasil

      davidlustosa2
      A
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      MCP 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.
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    • mcp-brasil

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      MCP 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.
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    • mcp-brasil

      PkLavc
      A
      license
      Not graded
      quality
      Not graded
      maintenance
      Connects AI agents to 28 Brazilian public APIs, providing over 200 tools to access data on economy, legislation, transparency, and the judiciary. It enables complex queries and cross-referencing of government datasets like IBGE, the Central Bank, and the National Congress through natural language.
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    • mcp-dados-brasil

      marioluciofjr
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      MCP server that provides real-time access to Brazilian public data from 6 official sources via 11 read-only tools, including Pix, IBGE, Câmara dos Deputados, Senado Federal, Diário Oficial da União, and Agência Brasil, with no API key required.
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    • mcp-brasil

      honeyvig
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to connect to 28 Brazilian public APIs covering economic, legislative, transparency, judicial, electoral, environmental, health, and other government data, providing 213 tools, 55 resources, and 45 prompts for natural-language queries and cross-referenced analysis.
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    • agrobr-mcp

      bruno-portfolio
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      An MCP server that provides real-time access to Brazilian agricultural data, including commodity prices, crop estimates, climate information, and deforestation rates. It integrates data from 19 public sources like CEPEA, CONAB, and IBGE to enable LLMs to analyze the Brazilian agribusiness sector.
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.8/5.0

    Scored across 7 tools

    Disambiguation3/5

    There is noticeable overlap between recomendar_tools, planejar_consulta, and search_tools, as all accept natural language queries and return tool recommendations, albeit in different formats. listar_features and listar_datasets_disponiveis are distinct but share a similar prefix. The remaining tools are clearly separated.

    Naming Consistency2/5

    Tool names mix Portuguese (listar, recomendar, planejar, executar) and English (search, call), creating an inconsistent impression. Although most follow a verb_noun pattern, the language switch is jarring and not predictable.

    Tool Count5/5

    Seven tools is well-scoped for a meta-orchestration server. Each tool serves a distinct role in the discovery, planning, and execution workflow without unnecessary redundancy or excessive count.

    Completeness4/5

    The tool set covers the main workflow: listing features, discovering tools, planning queries, and executing them individually or in batch. Minor gaps exist, such as no direct tool to manage datasets or inspect feature details, but the core surface is functional.

    Maintenance

    ActivitySlowing
    ResponsivenessResponsive

    Resources