Universal Database Connectivity Agent (UDCA)

This Model Context Protocol (MCP) agent furnishes the necessary mechanisms for interfacing with and manipulating various backend data persistence layers.

Core Capabilities

• Polyglot DB Support: Seamless connection to SQLite, Postgres, MySQL/MariaDB, and SQL Server instances.
• Standardized Abstraction: Presents a consistent set of operations irrespective of the underlying database technology.
• Platform-Specific Overrides: Allows invocation of proprietary database features when necessary.
• Structure Definition Control: Tools for defining, amending, and erasing database constructs (tables, indexes).
• Statement Dispatch: Capabilities for submitting both native SQL commands and utilizing abstracted data manipulation methods.
• Transactional Control: Features to demarcate, finalize (commit), or undo (rollback) multi-step operations.

Deployment Prerequisites

Necessary Components

• Python interpreter version 3.8 or newer is mandatory.
• Required Python libraries (installable via pip):
• SQLAlchemy (core dependency)
• Database-specific drivers:
  • SQLite: Bundled with Python.
  • PostgreSQL: psycopg2-binary
  • MySQL/MariaDB: mysql-connector-python
  • SQL Server: pyodbc

Source Code Installation

bash

Obtain source repository

git clone

Install in editable mode

pip install -e .

Configuration Regimen

The agent's behavior is tunable via environment variables, a configuration file, or direct runtime specification of connection parameters.

Environment Variables Utilization

• DB_CONFIG_PATH: Specifies the filesystem location of a JSON configuration document.
• DB_CONNECTIONS: A list of connection identifiers or a JSON payload defining connection metadata.

Configuration File Structure (JSON)

{ "connections": { "local_sqla": { "type": "sqlite", "db_path": "/absolute/path/to/data.db" }, "remote_pg": { "type": "postgres", "host": "db.example.com", "port": 5432, "database": "production_data", "user": "app_user", "password": "secure_pwd" } } }

Operational Guidance

Launching the Agent

As an MCP Handler for a Target System

bash

Launch with default setup

python -m db_mcp_server

Reference an external configuration file

python -m db_mcp_server --config /path/to/settings.json

Adjust logging verbosity

python -m db_mcp_server --log-level INFO

As a Networked Data Service (For any consuming LLM)

bash

Initiate the HTTP endpoint service

python -m db_mcp_server.web_server

Bind to a specific network interface and port

python -m db_mcp_server.web_server --host 127.0.0.1 --port 8888

Specify configuration source and diagnostic output level

python -m db_mcp_server.web_server --config /path/to/settings.json --log-level WARNING

Exposed Toolset

Connection Lifecycle Administration

• add_connection: Registers a new connection profile.
• test_connection: Validates connectivity to a configured data source.
• list_connections: Retrieves a roster of active connection identifiers.
• remove_connection: Deactivates and discards a connection profile.

Data Manipulation & Retrieval

• execute_query: Runs arbitrary SQL statements.
• get_records: Fetches result sets from a specified table.
• insert_record: Adds a new row of data.
• update_record: Modifies existing data entries.
• delete_record: Erases specified data entries.

Metadata and Structure Definition

• list_tables: Enumerates all accessible tables within the selected database context.
• get_table_schema: Retrieves the column structure definition for a given table.
• create_table: Provisions a new table structure.
• drop_table: Deletes an entire table.
• create_index: Establishes an index for optimized lookups.
• drop_index: Removes an existing index.
• alter_table: Changes the structure (e.g., adding columns) of an existing table.

Transactional Integrity

• begin_transaction: Initiates a new atomic unit of work.
• commit_transaction: Permanently saves changes within the current unit.
• rollback_transaction: Undoes all operations since the transaction began.

Operational Demonstrations

Registering a New Link

{ "connection_id": "local_sqla", "type": "sqlite", "db_path": "/data/system.db" }

Executing a Query Statement

{ "connection_id": "local_sqla", "query": "SELECT username, created_at FROM users WHERE status = ?;", "params": ["active"] }

Instantiating a Table

{ "connection_id": "local_sqla", "table": "audit_log", "columns": [ { "name": "log_id", "type": "BIGINT", "primary_key": true, "nullable": false }, { "name": "event_type", "type": "VARCHAR(50)", "nullable": false }, { "name": "details", "type": "TEXT", "nullable": true } ] }

Inputting Data Records

{ "connection_id": "local_sqla", "table": "audit_log", "data": { "event_type": "USER_LOGIN", "details": "Successful authentication for user ID 42" } }

Development Context

Automated Validation

bash

Execute the complete test suite

python -m unittest discover

Target a specific validation file

python -m unittest tests.test_postgres_integration

External LLM Interfacing

When operating in networked web service mode, external agents (like alternative LLMs) interact via standard HTTP calls. The agent exposes the following interaction points:

Network Interface Endpoints

• /list_tools - HTTP GET or POST: Returns a structured manifest of all callable tools and their input expectations.
• /call_tool - HTTP POST only: Used to dispatch a command to the database agent.

Example Invocation Schema (For a client LLM)

Instructing another language model (e.g., Llama 3) on how to use this database proxy:

Your interface to the database system is an HTTP service located at http://localhost:8000. Use the following methods:

1. To enumerate available functions: Issue a POST request to: http://localhost:8000/list_tools

2. To execute a defined operation: Issue a POST request to: http://localhost:8000/call_tool The body must be JSON, structured as: { "name": "tool_identifier", "arguments": { "parameter_a": "input_for_a", "parameter_b": "input_for_b" } }

For instance, to retrieve records from the 'products' table using connection 'store_db': POST http://localhost:8000/call_tool Content-Type: application/json

{ "name": "get_records", "arguments": { "connection_id": "store_db", "table": "products", "limit": 10 } }

Client Integration Snippet (Python Reference)

python import requests import json

Service Root URI

SERVICE_ROOT = "http://localhost:8000"

Obtain tool manifest

def fetch_tool_definitions(): response = requests.post(f"{SERVICE_ROOT}/list_tools") return response.json()

Dispatch a function call

def invoke_agent_function(function_name, parameters_dict): request_body = { "name": function_name, "arguments": parameters_dict } response = requests.post(f"{SERVICE_ROOT}/call_tool", json=request_body) return response.json()

Example: Listing tables

def check_table_inventory(conn_id): return invoke_agent_function("list_tables", {"connection_id": conn_id})

Example: Executing complex SQL

def run_custom_sql(conn_id, sql_text, input_vars=None): return invoke_agent_function("execute_query", { "connection_id": conn_id, "query": sql_text, "params": input_vars or [] })

Example: Provisioning a database link

def establish_link(conn_id, db_dialect, settings): payload = {"connection_id": conn_id, "type": db_dialect} payload.update(settings) return invoke_agent_function("add_connection", payload)

Licensing

This software is released under the terms of the MIT License

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