Building a Semantic Layer over Brazil’s Official Economic Data

Описание и применение

Создан семантический слой для официальных экономических данных Бразилии, объединяющий различные источники и сохраняя экономический смысл и происхождение данных, позволяя AI‑агентам получать точные ответы.

Задача

Using Brazil’s official economic data programmatically is hard because each source exposes data differently and semantic meaning is lost when normalizing APIs.

Результат

Open Economics unifies disparate Brazilian economic data sources, preserves semantic meaning and provenance, and enables AI agents to retrieve accurate, machine-readable results.

Рабочий процесс

• 1. Define semantic resolution to map user query to economic concept (e.g., "current Selic target rate" → "selic-target").
• 2. Identify available official datasets that represent the concept (e.g., BCB series bcb-sgs:432).
• 3. Retrieve metadata and provenance (institution, dataset ID, unit, frequency).
• 4. Fetch observations via REST or MCP.
• 5. Return structuredContent plus bounded textual representation for AI agents.
• 6. Agents use only Open Economics to answer queries.
• 7. Validate interoperability by testing with Cline agent.

Доказательства

The hardest problem was preserving economic meaning.

Источник

The remote MCP server currently exposes 19 tools.

Источник