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Query PostgreSQL databases, inspect schemas, and analyze performance with natural language.
The materials indicate this tool mainly connects to PostgreSQL databases and executes queries, so the main exposure is database access and local tool execution—both common MCP capabilities. No clear red flags were found for unrelated data exfiltration or extra third-party secret collection; however, despite being auditable open source under MIT, weak adoption and unknown maintenance still warrant caution around production data.
The materials state that no extra secrets or environment variables are required, and no third-party API token is requested. Still, if it connects to PostgreSQL in practice, database credentials would remain sensitive, though that is not presented here as additional credential harvesting.
The description indicates it connects to PostgreSQL databases for schema exploration and query execution, so normal network traffic to a user-configured database endpoint should be expected. There is no stated behavior suggesting data is sent to unrelated third-party endpoints.
The system flags this tool as executes-code, meaning it runs locally as an MCP tool and executes its implementation logic. That is a normal tool capability; the provided materials do not show requests for system privileges beyond its stated database-query purpose.
Its stated features—schema exploration, query execution, and performance analysis—imply access to schema metadata and query results in the target PostgreSQL database; if the database account is over-privileged, sensitive data could also be read or modified. The materials do not show a need for local file access or unrelated data permissions.
The project has a public GitHub repository and an MIT open-source license, which are meaningful risk-reducing factors because the code is auditable. However, it comes from a third-party registry, has 0 stars, and its maintenance status is unknown, so supply-chain maturity and upkeep signals are weak and version/dependency review is advisable.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-postgres-query" yet — see the docs or source repo.
After connecting to this PostgreSQL database, list all schemas, tables, and key columns, and explain the relationships among the users, orders, and products tables.
A schema overview with key column descriptions and relationship mapping between the main tables.
Query the daily order count, total revenue, and average order value for the last 30 days, sorted by date ascending; if column names are unclear, inspect the schema first before writing SQL.
Executable SQL, a summary of query results, and brief metric definitions if needed.
This PostgreSQL query is running slowly. Analyze possible causes, inspect the execution plan, and suggest optimizations for indexing, SQL rewriting, or table design: SELECT * FROM orders o JOIN order_items i ON o.id = i.order_id WHERE o.created_at >= NOW() - INTERVAL '90 days';
A performance diagnosis, execution plan interpretation, and actionable optimization recommendations with example SQL.
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