Example workflows

Three investigation patterns you can run entirely from your AI client once the Rius MCP server is connected. Each starts from a plain-language prompt; the tool sequence shows what your client does with it.

Investigate a slow agent run

My agent felt slow this afternoon. Find the slowest runs from the last 6 hours and show me where the time went in the worst one.

What the client does:

1. list_agent_traces with hours: 6, then sorts by the dur_ms column.
2. get_agent_trace with the slowest run’s trace_id.

The waterfall shows every step indented by depth with its duration, so the bottleneck is visible at a glance: one LLM call dominating the total is a model latency problem, many sequential tool calls of similar size is a fan-out you could parallelize, and a gap between spans points at uninstrumented work.

Follow-ups that work well in the same conversation:

Is that latency normal? Compare against the p95 for the last week.

(The client calls agent_traces_summary with hours: 168 and compares the percentiles against the slow run.)

Find failed runs this week

Which of my agent runs failed this week, and what actually went wrong?

What the client does:

1. list_agent_traces with hours: 168, status: "Error".
2. get_agent_trace on one or more failing runs.

In the waterfall, the failed span carries the error status. Failed tool calls show up as error-status TOOL steps inside otherwise healthy runs, so this same flow answers “find failed tool calls” too. If the list is long, narrow it:

Only the checkout-agent service, and group the failures by root span name.

(service: "checkout-agent" on the list call; the grouping happens in the client from the returned table.)

Watch cost and compare models

What did my agents cost this week, and which service is spending the most?

What the client does:

1. agent_traces_summary with hours: 168 for the total.
2. agent_traces_summary per service (service: ...) for the split.

Because list_agent_traces includes a model and cost column per trace, model-level comparisons work from the same data:

List this week’s traces and compare average cost per run between the gpt-4o runs and the gpt-4o-mini runs.

For a recurring version of this, ask the same question tomorrow; the tools always answer from a lookback window ending now, so “this week” stays current.

Writing your own

The pattern behind all three: start wide with agent_traces_summary (is anything wrong? how much?), narrow with list_agent_traces filters (which runs?), finish with get_agent_trace (why?). Any question you can phrase in those three steps works, and the tool reference lists every filter available at each step.

Next steps

• Tool reference: every argument available at each step.
• Viewing traces: the same investigations in the console, when you want the full waterfall UI.