FerretDB uses GateRook’s MongoDB connector. FerretDB's MongoDB API on PostgreSQL.
The screenshots show GateRook 2.0.0: the FerretDB form, the endpoint selector, and this connector’s actual tool preview. The form and preview have not been saved. Select a screenshot to open it at full size.
1. Create a connection and enter the settings
In GateRook, open Connections, select Add connection, search for FerretDB, choose its card and select Continue to configure.
Use the MongoDB-compatible address exposed by FerretDB, with a user permitted to read the intended database. The PostgreSQL backend address is not the MongoDB endpoint.
Check the FerretDB version and its authentication configuration. FerretDB 2 uses SCRAM-SHA-256; connection instructions written for the older PLAIN authentication flow do not apply unchanged.
Enter a Name, for example ferretdb-demo. Your AI app uses this name to choose the connection.
Choose Host and port under Connect using and enter the FerretDB Host and Port, or choose Connection string for a credential-free MongoDB URI.
Enter User and Password. In Advanced, set Mechanism to SCRAM-SHA-256 for FerretDB 2 and use the administrator-provided Auth database. Select TLS when the endpoint provides TLS and add a private CA certificate (PEM) when required.
Use Load databases and select only the required Databases. In Advanced, narrow Collections the agent may read with entries such as shop.orders; replace these examples with your own database and collection names.
Choose Development, Staging or Production to match the service. Review the connector’s limit and access settings before continuing.
2. Test the connection and add it to an endpoint
Select Continue to test. GateRook checks the settings from your computer. Review the result and resolve any connection, certificate or permission error before continuing.
If authentication fails after an upgrade, verify the FerretDB version, mechanism and user configuration. If discovery works but a command fails, consult FerretDB's supported MongoDB operations; compatibility does not imply that every MongoDB command is implemented.
After the test passes, select Continue to add to an endpoint. Choose the endpoint your AI app uses, review its tool modes, then select Save connection. GateRook offers default as the starter endpoint.
3. Use the connection from your AI IDE
After saving FerretDB on the endpoint and passing its connection test, ask your AI app to use ferretdb-demo, or the name you chose. If its tools are missing, confirm the endpoint assignment and refresh the MCP connection or start a new chat.
The preview below shows this connector’s default tools before saving. The table explains what each tool can do; connection settings, credentials and endpoint policies can restrict access further.
Start with a small read request. Replace example resource names with existing resources your connection may access. Open Activity after the call to inspect its connection, tool, arguments, policy decision and result metadata.
Ask your AI, for example
- “List the permitted databases and collections without changing anything.”
- “Inspect the fields and indexes of the orders collection, if it exists.”
- “Show at most 10 recent order documents, returning only status and creation time.”
| Tool | What it does | Starts as |
|---|---|---|
mongo_find | Find documents with a filter and a projection. | Read only |
mongo_aggregate | Run an aggregation pipeline. $out and $merge are refused. | Read only |
mongo_update | Update the first document matching a filter. Supports update operators or an update pipeline; upsert is optional. | Approval |
mongo_update_many | Update all documents matching a filter. The read document limit does not cap updates. Supports update operators or an update pipeline; upsert is optional. | Approval |
You can change each tool’s mode on the endpoint. Every call your AI makes appears in Activity.