JetBrains AI Assistant with Verdictan
JetBrains AI Assistant supports OpenAI-compatible endpoints. It also supports custom MCP servers, but the two paths use different authentication settings.
Route model requests
- Start the gateway using Gateway Setup for IDEs.
- Open Settings → Tools → AI Assistant → Providers & API keys.
- Add an OpenAI-compatible endpoint.
- Set the endpoint to
http://127.0.0.1:41002/v1. - Add a Verdictan client API token.
- Test the connection.
- Assign a returned model to the AI Assistant feature you intend to use.
JetBrains assigns custom models to feature groups. A feature can be unavailable when the selected model does not have a necessary protocol or capability. Inline completion also has model-specific requirements. Verify that a chat model supports fill-in-the-middle completion before you assign it.
See JetBrains' custom-model documentation for settings and assignment rules.
MCP authentication requirement
The Verdictan MCP endpoint must have an HTTP bearer header. At this time, the public JetBrains MCP documentation documents remote server URLs. It does not document a protected bearer-header field for that remote configuration.
Do not put a raw Verdictan token in a project MCP file. Add the hosted MCP
endpoint only if your installed JetBrains version has a protected bearer
header field. Verify that the field sends the header to
https://<published-hostname>/mcp. MCP tools supplement the selected model.
They do not reroute a JetBrains-hosted model.
Verify
Run verdictan events tail --since 10m --follow. Select the Verdictan model. Send a
chat request. A matching event verifies the model path. If you configured MCP
with a protected header, examine its server status and test a tool separately.