What gets logged
Each request captures:| Field | Description |
|---|---|
| Tool | Which tool was called |
| Parameters | Request payload |
| Response | Full response data |
| User | Who made the request |
| Team | Which team (workspace) |
| Timestamp | When it happened |
| Latency | How long it took |
| Status | Success or error |
| MCP Server | Which upstream server |
View logs
Go to Logs in the dashboard. Filter by:- MCP server
- Team
- User
- Tool name
- Time range
- Status (success/error)
Usage analytics
The dashboard shows aggregate metrics:- Requests over time
- Requests by server
- Requests by team
- Requests by user
- Error rates
- Latency percentiles
- Most used tools
Debugging
When something breaks, logs tell you exactly what happened. Tool returning errors? Check the response data. The MCP server’s error message is captured. Wrong results? Check the parameters. Maybe the agent sent incorrect arguments. Slow responses? Check the latency breakdown. Is it the MCP server or the network? Access denied? Check the user and team. Do they have permission for this tool? A typical debugging flow:- Filter logs by the user who reported the issue
- Find the failing request
- Check parameters and response
- Identify the root cause
Metadata
Each request includes rich context from authentication, available for both API key and OAuth flows:| Field | Description |
|---|---|
organisation_id | Organization identifier |
organisation_name | Organization display name |
workspace_id | Workspace identifier |
workspace_name | Workspace display name |
workspace_slug | Workspace slug |
user_id | User identifier |
api_key_id | API key identifier (if using API key auth) |
mcp_server_id | Which MCP server handled the request |
mcp.auth.type | Authentication method (api_key or Bearer) |
- Per-user usage tracking
- Per-team cost allocation
- Compliance auditing
- Chargeback reporting
- Authentication flow analysis
Forward metadata to MCP servers
Use Identity Forwarding to send user context to MCP servers. The MCP server receives information about who made the request. It can then:- Log with Portkey context
- Enforce its own access controls
- Personalize responses
- Attribute actions to users
Integration with AI Gateway
For Portkey AI Gateway users, MCP logs integrate with existing observability:- Traces span LLM calls and tool use
- Single dashboard for agent activity
- Correlate tool calls with model outputs
- See the full picture of what your agents are doing