What distinguishes this architecture from direct LLM–database interaction?
All data access must pass through the MCP broker as a “tool”, enforcing a strict separation between semantic processing (LLM) and data retrieval (agent).
Documentación del TFG: Interconexión entre Espacios de Datos e I…
@jaimealruiz
Basic information
About Documentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa MCP Server
Diseño e Implementación de interconexión entre LLM y Espacios de Datos mediante Model Context Protocol (MCP)
Documentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa MCP Server config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryDocumentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa MCP Server tools
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Overview
What is Documentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa?
This project documents a Final Degree Project (TFG) that designs and implements a multi-agent architecture interconnecting data spaces and generative AI. It uses a broker based on the Model Context Protocol (MCP) combined with the Google A2A (Agent to Agent) protocol, enabling structured communication between LLM agents and a local data space.
How to use Documentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa?
The system is deployed via Docker Compose. Agents register with the MCP broker on startup using a POST request to /agent/register. A command‑line client script (consola) sends natural‑language questions to the LLM agent, which generates SQL and coordinates the query cycle. Configuration is managed through a .env file with fixed agent IDs.
Key features of Documentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa
- MCP broker acts as a secure, modular hub for all agent communication.
- Agents communicate exclusively through the A2A protocol via the broker.
- Local LLM (TinyLlama) generates SQL from natural‑language questions.
- DuckDB serves as the local Iceberg‑formatted data space.
- Containerized architecture with FastAPI REST interfaces.
- CLI client for terminal interaction.
Use cases of Documentación del TFG: Interconexión entre Espacios de Datos e Inteligencia Artificial Generativa
- Querying sales data (fecha, producto, cantidad, precio) using natural language.
- Demonstrating agent‑to‑agent communication without direct agent‑agent or agent‑database connections.
- Testing an MCP‑based broker for interoperable multi‑agent systems.
- Prototyping a local, reproducible data‑space architecture with generative AI.
Frequently asked questions
What runtime dependencies are required?
The system uses Docker Compose, DuckDB, FastAPI, and Transformer’s TinyLlama-1.1B-Chat-v1.0. No external cloud dependencies are needed.
Where does the data reside?
Data is stored locally in a DuckDB file (`lake.duckdb`) containing the table `iceberg_space.ventas`. It is loaded via the `load_data.py` script.
What are the known limitations?
The current implementation is a local prototype. Future work includes adopting full Google A2A specifications (Agent Card, JSON‑RPC 2.0, SSE), persisting messages and agents to a database, and adding conversation IDs for traceability.
How is agent identity managed?
Agents register at startup with a fixed `agent_id` from the `.env` file (or an auto‑generated one). The broker maintains a registry in memory.
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