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Feature / AI

AI Log Analysis With Fluxtail

Use AI log analysis in Fluxtail to group repeated failures, summarize a bounded time window, check receiver health, and jump back into the raw log rows that support the answer.

MCP Built-in AI chat Exception clustering Raw rows
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Two ways to query logs with AI

Fluxtail supports AI-assisted investigation through MCP-connected clients and built-in AI chat inside the product.

Still anchored in raw logs

You can jump from summaries back into the stream so AI remains an accelerator rather than a detached black box.

Works with existing scopes

Account-scoped access tokens and hosted MCP auth keep AI access tied to the same security and account boundaries as the core product.

Best when paired with good streams

AI becomes much more useful once streams, receivers, and live viewing already make the logs easy to navigate.

Scope

Start with a bounded log slice

AI log analysis works best when the question includes a stream, service, receiver, severity, or short time window.

01

Name the stream or service

Ask about one stream, service, host, namespace, receiver, or filtered result instead of the entire account.

02

Set a time window

Use a recent bounded range such as ten minutes or one deployment window so the summary can point back to exact rows.

03

Keep raw rows nearby

Treat the answer as a shortcut to the evidence, not as a replacement for reading the rows.

AI proof

AI summaries are useful when they stay tied to the same stream and time window

Fluxtail’s AI tools work on the same streams and time windows you already use in the product.

Real MCP-style error summary request
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}

Uses the real Fluxtail MCP tool name and real input fields from the hosted/local adapter.

Bounded summary that still points back to the evidence
text
1example output:
2summary:
3- 2 timeout clusters from api-gateway
4- 1 retry-limit cluster from queue-worker
5- busiest window: 07:20-07:30 UTC
6 
7next step:
8- open checkout-api + api-gateway rows for 07:20-07:30 UTC

The useful output is not just a summary. It also tells you which raw rows to read next.

MCP

Use MCP diagnostics for known tasks

Fluxtail MCP tools are useful when the task is specific: find exceptions, summarize errors, check receiver health, or explain why logs are missing.

01

Summarize repeated errors

Use summarize_errors on a bounded service and time range when the same failure is repeated across many rows.

Ask for an error summary
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}
Example summary shape
output
2 timeout clusters from api-gateway
1 retry-limit cluster from queue-worker
busiest window: 07:20-07:30 UTC

The output should reduce the rows you need to read next, not make the final call for you.

02

Check missing-log cases

Use why_no_logs or check_receiver_health when the issue is that a source stopped sending records or a receiver looks quiet.

03

Find exception clusters

Use find_exceptions when the stream has stack traces or repeated exception names that need grouping.

AI proof

AI summaries are useful when they stay tied to the same stream and time window

Fluxtail’s AI tools work on the same streams and time windows you already use in the product.

Real MCP-style error summary request
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}

Uses the real Fluxtail MCP tool name and real input fields from the hosted/local adapter.

Bounded summary that still points back to the evidence
text
1example output:
2summary:
3- 2 timeout clusters from api-gateway
4- 1 retry-limit cluster from queue-worker
5- busiest window: 07:20-07:30 UTC
6 
7next step:
8- open checkout-api + api-gateway rows for 07:20-07:30 UTC

The useful output is not just a summary. It also tells you which raw rows to read next.

Chat

Use built-in AI chat from the same stream

Built-in AI chat should work from the stream and filters already visible in Fluxtail, so the answer stays connected to the rows being read.

01

Ask from the filtered view

Filter to a service, severity, source, or time window first, then ask the question from that view.

02

Ask for the rows behind the answer

Useful answers should include the repeated message, time window, service, receiver, or raw row pattern that supports the summary.

03

Keep sensitive access scoped

Use account-bound access and scoped tokens for external MCP clients, and keep in-product chat tied to the same account context.

AI proof

AI summaries are useful when they stay tied to the same stream and time window

Fluxtail’s AI tools work on the same streams and time windows you already use in the product.

Real MCP-style error summary request
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}

Uses the real Fluxtail MCP tool name and real input fields from the hosted/local adapter.

Bounded summary that still points back to the evidence
text
1example output:
2summary:
3- 2 timeout clusters from api-gateway
4- 1 retry-limit cluster from queue-worker
5- busiest window: 07:20-07:30 UTC
6 
7next step:
8- open checkout-api + api-gateway rows for 07:20-07:30 UTC

The useful output is not just a summary. It also tells you which raw rows to read next.

Evidence

Verify summaries against raw rows

The safest AI log analysis flow always returns to the raw event stream.

01

Read the row cluster

Open the rows behind the summary and confirm the message, severity, source, and time range match the answer.

Rows behind the summary
output
2026-04-24T07:22:14Z ERROR api-gateway route=/checkout upstream timeout request_id=req-91ae
2026-04-24T07:22:15Z ERROR checkout-api payment retry budget exhausted order_id=4921 request_id=req-91ae
2026-04-24T07:22:17Z WARN  queue-worker retry limit reached job_id=job-7789 order_id=4921

AI log analysis is trustworthy only if a human can confirm the same conclusion from the rows underneath it.

02

Compare with facets and histograms

Use facets and histograms to confirm whether the same service, severity, host, or namespace is actually responsible for the spike.

03

Save the useful filter

When the answer identifies a repeatable pattern, save the filter or alert so the next occurrence is easier to catch.

AI proof

AI summaries are useful when they stay tied to the same stream and time window

Fluxtail’s AI tools work on the same streams and time windows you already use in the product.

Real MCP-style error summary request
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}

Uses the real Fluxtail MCP tool name and real input fields from the hosted/local adapter.

Bounded summary that still points back to the evidence
text
1example output:
2summary:
3- 2 timeout clusters from api-gateway
4- 1 retry-limit cluster from queue-worker
5- busiest window: 07:20-07:30 UTC
6 
7next step:
8- open checkout-api + api-gateway rows for 07:20-07:30 UTC

The useful output is not just a summary. It also tells you which raw rows to read next.

Signals

Use alerts, facets, and histograms with AI

AI is more useful when it works alongside the normal log-reading controls instead of replacing them.

01

Use alerts for repeated patterns

Convert known repeated errors, missing logs, and receiver health problems into alerts instead of asking the same AI question every time.

02

Use histograms to find the window

Find the busiest time slice first, then ask AI to summarize that smaller slice.

03

Use facets to pick the source

Find the service, host, namespace, receiver, or level that changed, then ask about that source specifically.

AI proof

AI summaries are useful when they stay tied to the same stream and time window

Fluxtail’s AI tools work on the same streams and time windows you already use in the product.

Real MCP-style error summary request
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}

Uses the real Fluxtail MCP tool name and real input fields from the hosted/local adapter.

Bounded summary that still points back to the evidence
text
1example output:
2summary:
3- 2 timeout clusters from api-gateway
4- 1 retry-limit cluster from queue-worker
5- busiest window: 07:20-07:30 UTC
6 
7next step:
8- open checkout-api + api-gateway rows for 07:20-07:30 UTC

The useful output is not just a summary. It also tells you which raw rows to read next.

AI proof

AI summaries are useful when they stay tied to the same stream and time window

Fluxtail’s AI tools work on the same streams and time windows you already use in the product.

Real MCP-style error summary request
json
1{
2  "name": "summarize_errors",
3  "arguments": {
4    "service_name": "checkout-api",
5    "since_time": "2026-04-24T07:20:00Z",
6    "until_time": "2026-04-24T07:30:00Z",
7    "limit": 200
8  }
9}

Uses the real Fluxtail MCP tool name and real input fields from the hosted/local adapter.

Bounded summary that still points back to the evidence
text
1example output:
2summary:
3- 2 timeout clusters from api-gateway
4- 1 retry-limit cluster from queue-worker
5- busiest window: 07:20-07:30 UTC
6 
7next step:
8- open checkout-api + api-gateway rows for 07:20-07:30 UTC

The useful output is not just a summary. It also tells you which raw rows to read next.

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Pricing

Pricing is coming soon while Fluxtail plan names, limits, retention, and usage tiers are finalized.
Next step

Send one real source and read the logs

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Point one real source at Fluxtail.

Create a receiver, send one source, and inspect the first stream.

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