PostgreSQL MCP Server
Connect to the PostgreSQL MCP server to explore schemas, run SQL, modify data, and analyze query plans using AI agents on Gumloop, Claude, or Cursor.
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PostgreSQL
Installation
Get StartedCreate a Gumloop Account
To use this MCP, you need a Gumloop account. If you don't have one yet, you can create one for free.
Copy Your Server URL
Copy your MCP server URL and add it to your client. You'll be prompted to authorize on first use.
Tools (5)
List Schemas
List all schemas with their owners
List Objects
List tables, views, sequences, or extensions in a schema
Get Object Details
Get detailed information about a table, view, sequence, or extension
Execute Sql
Execute any SQL query and return results
Explain Query
Show query execution plan with costs and strategy
What is PostgreSQL MCP?
The PostgreSQL MCP server gives AI agents direct access to your PostgreSQL database. That means agents can list the schemas in your database, browse the tables, views, sequences, and extensions inside a schema, inspect an object down to its columns, data types, constraints, and indexes, run any SQL statement you need, and analyze a query’s execution plan before it runs. It works against any PostgreSQL instance your connection string can reach, from a local dev database to a managed production cluster.
If you spend your day writing the same exploratory queries, hand-checking column types before a migration, or copying result sets out of a SQL client to share them, an AI agent can take a lot of that off your plate. Describe what you need, and your AI agent will handle the querying and schema lookups for you.
MCP stands for Model Context Protocol. It is an open standard that gives AI agents a way to connect to external tools and services. Instead of installing a database driver, wiring up environment variables, and writing code against a connection pool, you add your PostgreSQL connection string to Gumloop once. After that, you can explore and query your database just by chatting with your AI agent.
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What you can do with PostgreSQL MCP on Gumloop
Run any SQL query as an AI database copilot
Execute any SQL statement and get the results back inline. SELECT to read data, INSERT, UPDATE, or DELETE to change it, and CREATE, ALTER, or DROP to manage schema. Your AI agent can write the query for you, run it, and explain the output, so you can work with your data by describing what you want. Statements run with a 30-second query timeout, and add a LIMIT clause yourself when you want to cap a large result set.
Explore your database schemas
List every schema in the database along with its owner, and see which ones are user schemas versus system schemas. Your agent can map out an unfamiliar database before it touches a single table.
Browse tables, views, sequences, and extensions
List the objects inside any schema, filtered by type: tables, views, sequences, or installed extensions. This gives an agent a quick inventory of what lives in a schema without you opening a SQL client.
Inspect object structure in detail
Pull the full definition of a table or view, including its columns, data types, primary keys, foreign keys, unique and check constraints, and indexes. For sequences you get the start value and increment, and for extensions you get the version and relocatable flag. Reading the real structure first keeps the SQL an agent writes grounded in your actual schema instead of guesswork.
Modify data and schema with SQL
Because execute_sql accepts any valid statement, an agent can insert and update rows, clean up bad data, run migrations, or grant and revoke privileges. What an agent is allowed to do is set entirely by the privileges of the database user in your connection string, so a read-only user stays read-only.
Analyze query performance before you run it
Generate an EXPLAIN plan for any query to see its cost, strategy, and join order, with output available as text, JSON, YAML, or XML. JSON output is easy for an agent to parse, so it can flag sequential scans, missing indexes, or expensive joins. Turn on ANALYZE when you want real execution timing, keeping in mind that EXPLAIN ANALYZE actually runs the query.
Inspect installed extensions
List the extensions installed in the database and read the details of any one, including its version. Useful for checking whether something like PostGIS or pgvector is present and current before an agent depends on it.
How to connect the Gumloop PostgreSQL MCP Server
- 1
Create a free Gumloop account
Sign up at gumloop.com. No credit card required.
- 2
Add the PostgreSQL MCP server
Copy your MCP server URL from Gumloop and add it to your preferred client (Claude, Cursor, or Gumloop workflows). You'll authorize on first use.
- 3
Start using PostgreSQL in your AI workflows
That's it. Your AI agent can now explore your schemas, run and explain SQL, and read or modify your data. Use it inside a Gumloop automation, in Claude Desktop, or in Cursor.
PostgreSQL MCP use cases
Natural-question reporting for data analysts
An analyst can ask a Gumloop agent a question in plain terms, and the agent will list the relevant schema, inspect the tables it needs, write a SELECT against the real columns, run it, and drop the results into a Google Sheet or a Slack message. The team gets the numbers without anyone hand-writing the SQL.
Schema documentation for backend engineers
Point an agent at a database and have it walk every user schema, pull each table’s columns, constraints, and indexes with get_object_details, and compile a clean data dictionary in Notion or Google Docs. Engineers get living documentation of a database that nobody has had time to write up.
Query performance triage for DBAs
When a query is slow, a DBA can hand it to an agent that runs explain_query, reads the JSON plan, and points out the sequential scans, missing indexes, or costly joins driving the cost. The agent can suggest an index and, with the right privileges, create it, then re-check the plan to confirm the improvement.
Data cleanup and maintenance for data teams
An agent can find rows that break a business rule, preview them with a SELECT, then run the UPDATE or DELETE to fix them once you approve, all scoped to what the connection-string user is allowed to touch. Routine maintenance that used to mean a careful manual session in psql becomes a guided conversation.
Cross-system data agents
Chain PostgreSQL with Slack, Gmail, Google Sheets, and other MCP integrations in a single agent. Pull fresh figures from your database on a schedule, have an LLM summarize what changed, write the summary to a sheet, and post the highlights to a channel, so your database talks to the rest of your stack automatically.
Why use Gumloop for PostgreSQL MCP
Your connection string, stored securely
Add your PostgreSQL connection string once, stored securely. No env vars, connection-string juggling, or coding against a database driver necessary. Gumloop also obfuscates the password in its logs and error messages, so your credentials don’t leak into agent traces.
Works with multiple MCP clients
Use the PostgreSQL MCP server endpoint in Claude Desktop, Cursor, or directly inside Gumloop agents. Same server URL, works with any MCP client.
Chain PostgreSQL with 100+ other integrations
Combine PostgreSQL with Slack, Gmail, Google Sheets, Notion, and other MCP tools in a single AI agent. An agent can pull rows from your database, process them with an LLM, and write the result back to PostgreSQL or anywhere else.
Enterprise-grade and scalable
Built for teams, with role-based permissions and dedicated support for Pro users. The agent’s database access stays bounded by the privileges of the connection-string user, so it respects your existing PostgreSQL access controls. For details on Gumloop’s security practices, see trust.gumloop.com.
Pricing includes a free plan
You can test the PostgreSQL MCP integration on Gumloop’s free tier before committing. Paid plans start at $37/month.
Frequently asked questions
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