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Securely connect AI to PostgreSQL for querying, schema analysis, and controlled writes.
This MCP tool appears to function primarily as a bridge to PostgreSQL databases, with no declared external API endpoints or extra secrets, and no clear high-risk red flags in the provided material. The main concerns are local execution capability, read/write access to the database, and a relatively weak trust profile despite being open source, so it should be used with least privilege and in an isolated environment.
The material states 'required secrets/environment variables: none', and no external API token or cloud credential is declared. Still, connecting to PostgreSQL would typically rely on database connection configuration, but the material does not disclose specific credential handling.
No remote host is declared, and there is no stated transfer of data to third-party services; however, its functionality inherently requires communication with a PostgreSQL instance, so queries and database data will traverse that connection. The material is insufficient to confirm that it is limited to localhost or internal databases only.
The system check indicates this tool has code/process execution capability; as an MCP tool, this typically means it can run a local service process. The material does not show requests for unusual system privileges or execution powers beyond its declared database-bridge role.
The description explicitly supports natural-language querying, schema analysis, and 'controlled write operations', indicating at least read access and potentially write access to the connected PostgreSQL database. If connected to production systems, the database account should be restricted to the minimum necessary scope.
The source is a third_party_registry entry, but it has a public open-source repository, which is a meaningful risk-reducing factor. However, the license is unspecified, community adoption is 0 stars, and maintenance status is unknown, so trust and ongoing maintenance evidence remain limited and warrant caution.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "postgres-mcp-server" yet — see the docs or source repo.
After connecting to the PostgreSQL database, query the last 30 days of order count, revenue, and average order value by product line, sort by revenue descending, and present the results in a table.
A SQL-backed result with key business metrics summarized in a clear table.
Analyze this PostgreSQL database schema, explain the relationships between the main tables, and identify the best tables and fields for user retention analysis.
An overview of the schema, table relationships, and recommendations for retention analysis data.
First generate SQL to mark tickets with status 'pending' and not updated for over 90 days as 'archived'; explain the impact before execution and wait for my confirmation before writing.
Safe update SQL with an estimated affected row count first, then execution only after confirmation.
Connect AI assistants to PostgreSQL for secure querying, management, and analysis.
Securely query and manage PostgreSQL databases for operations and automation.
Query PostgreSQL, inspect schemas, and run SQL safely with natural language.
Safely query and inspect PostgreSQL databases without enabling risky write operations by default.
Query PostgreSQL databases and schemas through natural language with read-only AI access.
Safely access and diagnose PostgreSQL databases through structured MCP tools.