How do I use Combined MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Combined MCP Server Search the knowledgebase for instructions on how to reset user passwords" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Combined MCP Server

A production-grade MCP (Model Context Protocol) server combining Redshift query capabilities and Knowledgebase vector store features.

Features

Redshift Tools

run_query - Execute SQL with IAM authentication via get_cluster_credentials
list_schemas - List database schemas
list_tables - List tables in a schema
describe_table - Get table structure

Large results (>100 rows) are automatically stored in S3 with 20 sample rows returned.

Knowledgebase Tools

build_vectorstore - Build vector store from S3 markdown files
query_vectorstore - Hybrid search (semantic + keyword) with RRF reranking
get_vectorstore_status - Check build status and cache stats

Quick Start

Local Development

Install uv (if not already installed):
curl -LsSf https://astral.sh/uv/install.sh | sh # Or on Windows: powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
Start infrastructure:
docker-compose up -d postgres localstack
Install dependencies:
uv pip install -e ".[dev]"
Configure environment:
cp .env.example .env.local # Edit .env.local with your settings
Run the server:
# With MCP Inspector mcp dev src/combined_mcp_server/main.py # Or directly python -m combined_mcp_server.main

ECS Deployment

# Build container docker build -t combined-mcp-server . # Run with health checks docker run -p 8080:8080 --env-file .env combined-mcp-server

Health endpoints:

GET /health - Liveness probe
GET /ready - Readiness probe
GET /status - Detailed status

Configuration

See .env.example for all configuration options. Key settings:

Variable	Description
`REDSHIFT_CLUSTER_ID`	Redshift cluster identifier
`POSTGRES_SECRET_NAME`	Secrets Manager secret for pgvector DB
`KNOWLEDGEBASE_S3_BUCKET`	S3 bucket with markdown files
`BEDROCK_EMBEDDING_MODEL`	Titan embedding model ID

Architecture

┌─────────────────────────────────────────────────────┐ │ Combined MCP Server │ ├─────────────────────┬───────────────────────────────┤ │ Redshift Tools │ Knowledgebase Tools │ │ ───────────────── │ ─────────────────────────── │ │ • run_query │ • build_vectorstore │ │ • list_schemas │ • query_vectorstore │ │ • list_tables │ • get_vectorstore_status │ │ • describe_table │ │ ├─────────────────────┴───────────────────────────────┤ │ Core Services │ │ AWS (Secrets Manager, S3, Bedrock, Redshift) │ │ PostgreSQL + pgvector │ └─────────────────────────────────────────────────────┘

Testing

# Unit tests pytest tests/ -v # With coverage pytest tests/ -v --cov=combined_mcp_server # Integration tests (requires Docker) docker-compose up -d pytest tests/ -v -m integration