mcp_servers
by manish6007
Overview
A production-grade Model Context Protocol (MCP) server combining Amazon Redshift SQL query capabilities and a Knowledgebase vector store for AI agents.
Installation
mcp-server-httpEnvironment Variables
- REDSHIFT_CLUSTER_ID
- REDSHIFT_DATABASE
- REDSHIFT_HOST
- REDSHIFT_RESULTS_BUCKET
- KNOWLEDGEBASE_S3_BUCKET
- POSTGRES_SECRET_NAME
- POSTGRES_HOST
- POSTGRES_DATABASE
- POSTGRES_USER
- POSTGRES_PASSWORD
- AWS_REGION
- AWS_ACCESS_KEY_ID
- AWS_SECRET_ACCESS_KEY
- AWS_ENDPOINT_URL
- BEDROCK_EMBEDDING_MODEL
- BEDROCK_MODEL_ID
- MCP_TRANSPORT
- MCP_HTTP_HOST
- MCP_HTTP_PORT
- MCP_SERVER_URL
- VECTORSTORE_TABLE_NAME
- VECTORSTORE_EMBEDDING_DIMENSION
- HYBRID_SEMANTIC_WEIGHT
- HYBRID_KEYWORD_WEIGHT
- HYBRID_RRF_K
- QUERY_CACHE_SIZE
- HEALTH_CHECK_PORT
- HEALTH_CHECK_HOST
- LOG_LEVEL
- LOG_FORMAT
Security Notes
The Redshift metadata tools (`list_tables`, `describe_table`) in `src/combined_mcp_server/redshift/tools.py` construct SQL queries using f-strings with schema/table names. If these inputs are derived from untrusted user input without proper validation or sanitization, this could lead to SQL injection vulnerabilities. The `run_query` tool itself uses parameterized queries for the main SQL input, which is safer.
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