# MCP Database Intelligence Server Architecture

## System Overview

The system exposes database capabilities to AI agents using the **Model Context Protocol (MCP)**.

The MCP server acts as a bridge between:

* AI assistants
* database metadata
* stored procedures
* database exploration tools



# High Level Architecture


AI Agent / MCP Client
        │
        ▼
    MCP Server
   (FastMCP)
        │
        ├── Discovery Tools
        │       (database exploration)
        │
        ├── Dynamic Procedure Tools
        │       (generated from database procedures)
        │
        ▼
   Intelligence Engine
   (procedure metadata + categorization)
        │
        ▼
   Database Access Layer
        │
        ▼
      MySQL Database




# Core Components

## 1. MCP Server (main.py)

The MCP server is created using **FastMCP**.

Responsibilities:

* initialize the MCP server
* register discovery tools
* register dynamic procedure tools



## 2. Database Access Layer (db.py)

This module manages all database communication.

Responsibilities:

* database connection management
* stored procedure execution
* metadata retrieval
* enforcing result limits

All database interactions pass through this layer.



## 3. Tool Generator (tool_generator.py)

This module dynamically converts database stored procedures into MCP tools.

Steps performed:

1. load procedure metadata from database
2. detect procedure parameters
3. generate MCP tool functions dynamically
4. register tools with the MCP server

This allows the system to automatically support **new procedures added to the database**.



## 4. Intelligence Engine (intelligence_engine.py)

The intelligence engine analyzes stored procedures to generate metadata.

Capabilities include:

* procedure categorization
* description generation
* parameter analysis

Example categories:

| Procedure Pattern | Category            |
| ------------------| ------------------- |
| GetLinkCount      | Network Analytics   |
| GetWeekData       | Time Analytics      |
| InterfaceLinkType | Interface Analytics |

This metadata helps AI agents understand the purpose of each tool.



## 5. Discovery Tools (discovery_tools.py)

Discovery tools allow AI agents to explore database structure.

Implemented tools:


list_databases
list_tables
describe_table


These tools rely on database metadata from:


INFORMATION_SCHEMA




# Security Architecture

Several safeguards are implemented to ensure safe database interaction.

## Keyword Restrictions

Procedures containing dangerous keywords are blocked.

Examples:


DELETE
DROP
TRUNCATE
UPDATE
INSERT
ALTER




## Query Result Limits

The system limits result size using a configurable value:


MAX_QUERY_ROWS


This prevents large data extraction.



## Rate Limiting

The MCP server enforces request rate limits to prevent excessive usage.



## Restricted Database User

The database user used by the MCP server should only have:


SELECT
EXECUTE


permissions.

This prevents modification of production data.



# Metadata Sources

The system retrieves metadata from MySQL system tables.

Examples:


INFORMATION_SCHEMA.ROUTINES
INFORMATION_SCHEMA.PARAMETERS
INFORMATION_SCHEMA.TABLES
INFORMATION_SCHEMA.COLUMNS


These tables allow the MCP server to automatically understand database structure.



# Example AI Interaction Flow

Example interaction between an AI agent and the database:


list_databases()
        ↓
list_tables()
        ↓
describe_table("interface_stats")
        ↓
GetLinkCount("NodeA")


This allows the AI to explore database structure before executing analytical procedures.



# Extensibility

The system is designed to support additional database objects.

Future extensions may include:


list_views
list_functions
list_triggers
search_tables
explain_query


The modular architecture allows new tools to be added easily.



# Conclusion

This architecture demonstrates how MCP servers can safely expose database capabilities to AI systems.

Key design principles:

• modular architecture
• dynamic tool generation
• metadata driven discovery
• secure database access

This approach enables safe and scalable **AI-database integration**.



Detailed system diagrams are available in the docs/ directory.