MCP Demo — GitHub Copilot + Your Database
An MCP server that enables AI assistants to query and interact with SQLite databases through natural language. It includes built-in security guardrails such as PII redaction, SQL injection blocking, and query rate limiting.
README
MCP Demo — GitHub Copilot + Your Database
What if you could ask Copilot a question and it queried your database to answer it? That's what this demo shows. Clone it, run two commands, and watch it work.
What is MCP?
Model Context Protocol (MCP) is an open standard that lets AI assistants like GitHub Copilot connect directly to your tools and data — in real time, with guardrails you control.
Instead of copy-pasting data into chat, Copilot just asks your server:
| You type in Copilot Chat | What happens |
|---|---|
What tables are in the database? |
Copilot calls get_schema, returns your full schema |
Show me all products under $200 |
Copilot writes and runs the SQL query live |
Which users have the admin role? |
Instant answer — emails auto-redacted |
MCP isn't AI-only. The same server can be called by scripts, pipelines, or any client. The LLM is just the first killer app.
See It In Action
Real responses from this running server:
"Show me all products under $200"
| Product | Price | Stock |
|---|---|---|
| Wireless Mouse | $49.99 | 120 |
| USB-C Hub | $79.99 | 60 |
| External SSD (1TB) | $129.99 | 35 |
| Mechanical Keyboard | $149.99 | 45 |
| Webcam (4K) | $199.99 | 12 |
Guardrail in action — PII is auto-redacted:
{
"rows": [{"id": 1, "name": "Alice Johnson", "email": "[REDACTED]", "role": "admin"}],
"pii_notice": "The following field(s) were redacted to protect PII: email"
}
Dangerous query? Blocked before it touches the database:
{ "success": false, "error": "Query contains a blocked pattern and was rejected for security reasons." }
Get Running in 3 Steps
Prerequisites: Python 3.12+, VS Code, GitHub Copilot extension
# 1. Install
git clone https://github.com/coderkanasu/mcp-demo && cd mcp-demo
pip install -r requirements.txt # simple install
# or: pip install -e . # editable install (for development)
# 2. Set up the database
python mcp_server/db/init_db.py
# 3. Verify it works
python mcp_server/tools/sql_query_tool.py
Connect to Copilot: A .vscode/mcp.json is already included — open this repo in VS Code and Copilot Agent mode picks it up automatically. No manual config needed.
{
"servers": {
"demo-sql-server": {
"type": "stdio",
"command": "python3",
"args": ["-m", "mcp_server.server"],
"env": { "PYTHONPATH": "${workspaceFolder}" }
}
}
}
Then open Copilot Chat → Agent mode and start asking questions.
Built-in Guardrails
This is a demo — but it ships with real security controls, not placeholders:
| Guardrail | Detail |
|---|---|
| PII auto-redaction | email, phone, address, password, and 9 other field patterns scrubbed from all results |
| SQL injection blocking | 17 patterns blocked: UNION SELECT, OR 1=1, stacked queries, DROP, -- comments, and more |
| Allowed operations only | SELECT, INSERT, UPDATE only — DROP, DELETE, TRUNCATE rejected |
| Row cap | 100 rows max per query — prevents bulk extraction |
| Rate limiting | 30 calls per 60-second window |
| Audit logging | Every query logged with timestamp and outcome |
| Sanitised errors | Stack traces never returned to the LLM |
Not included (needed for production): user authentication, role-based access control, write-operation approval gates. See PRODUCTION_READINESS.md.
What's Inside
mcp_server/
├── server.py # MCP server — rate limiting, routing, error handling
├── tools/
│ └── sql_query_tool.py # PII scrubbing, injection blocking, query execution
└── db/
├── init_db.py # Seeds sample e-commerce data
└── demo.db # SQLite database (users, products, orders)
.vscode/
└── mcp.json # Plug-and-play Copilot config
requirements.txt # pip install -r requirements.txt
pyproject.toml # package metadata + dev dependencies
How It Works
You (Copilot Chat)
│ "Show me all products under $200"
▼
GitHub Copilot (Agent mode)
│ calls get_schema, then execute_query
▼
MCP Server ←── validates, rate-limits, scrubs PII
│
▼
SQLite Database ←── returns rows
│
▼
Copilot formats and presents the answer
Want to extend it?
- Add a new tool: create a class in
mcp_server/tools/, register it inserver.py - Use a real database: swap SQLite for PostgreSQL/MySQL in
sql_query_tool.py - Add auth: wrap
execute_querywith a token check
Requirements
- Python 3.12+
- VS Code with GitHub Copilot (Agent mode)
- macOS / Linux / Windows
License
MIT — free to use, fork, and adapt.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.