# Bolt Logs Tool

The Bolt Logs Tool allows DinoAI to access and analyze execution logs from your Bolt scheduled jobs, bringing run history, error details, and performance data directly into your development workflow.

This tool connects DinoAI to your Bolt scheduler, enabling it to inspect dbt run outputs, diagnose failures, and help you resolve issues based on real log data — without leaving the Code IDE.

#### Capabilities

| Capability                | What It Does                                                               |
| ------------------------- | -------------------------------------------------------------------------- |
| **Fetch Run Logs**        | Retrieve execution logs for any Bolt schedule run by ID or schedule name   |
| **Diagnose Failures**     | Identify the root cause of failed dbt models or tests from log output      |
| **Analyse Run History**   | Review recent runs for a schedule to spot patterns or regressions          |
| **Surface Error Details** | Extract and explain compiler errors, runtime exceptions, and test failures |
| **Compare Runs**          | Contrast a passing run against a failing one to isolate what changed       |

#### Using the Bolt Logs Tool

1. Open DinoAI in the right panel of the Code IDE
2. Reference the Bolt schedule or run you want to inspect (e.g., `"the last run of the nightly schedule"` or a specific run ID)
3. Add your prompt describing what you want DinoAI to investigate
4. Grant permission when DinoAI asks to access Bolt logs
5. Review DinoAI's analysis and follow the suggested fixes

#### Example Use Cases

**Diagnosing a Failed Run**

**Prompt**

{% code overflow="wrap" %}

```
The nightly Bolt schedule failed this morning. Can you check the logs and tell me what went wrong?
```

{% endcode %}

**Result:** DinoAI fetches the latest run logs for the schedule, identifies the failing models or tests, extracts the relevant error messages, and suggests the most likely fixes — pointing you directly to the affected files.

**Investigating a Slow Run**

**Prompt**

{% code overflow="wrap" %}

```
The weekly Bolt job is taking much longer than usual. Can you look at the recent run logs and identify what's slowing it down?
```

{% endcode %}

**Result:** DinoAI reviews the run timing across models, highlights the slowest nodes, and suggests optimisations such as incremental model adjustments, test pruning, or upstream source query improvements.

**Comparing a Passing and Failing Run**

**Prompt**

{% code overflow="wrap" %}

```
Yesterday's run passed but today's failed. Can you compare both logs and tell me what changed?
```

{% endcode %}

**Result:** DinoAI retrieves both runs, diffs the outputs, identifies which models started failing and when, and correlates the failure with recent code or schema changes.

#### Working with Other Tools

The Bolt Logs Tool works well alongside DinoAI's other capabilities for a complete debugging workflow:

* Combine with the **File System Tool** to apply fixes to the models identified as failing directly from the log analysis
* Combine with the **Terminal Tool** to re-run specific dbt models or tests after a fix has been applied
* Use alongside the **SQL Execution Tool** to validate model logic against live warehouse data after diagnosing a data quality failure
* Pair with the **Snowflake** or **BigQuery Query Analysis Tools** to investigate upstream performance issues that caused a Bolt job to time out

#### Best Practices

* **Be specific about which schedule or run** — Reference the exact schedule name or run ID in your prompt so DinoAI retrieves the correct logs without ambiguity
* **Provide context about what you expected** — Telling DinoAI what the job should produce helps it identify deviations faster
* **Use it iteratively** — After applying a fix, ask DinoAI to check the next run's logs to confirm the issue is resolved
* **Review full error chains** — dbt failures often cascade; ask DinoAI to trace the root model failure rather than fixing every downstream symptom individually


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