Return the stored definition for an existing dashboard, so it can be edited and handed straight back to update_dashboard. Call this before update_dashboard rather than rebuilding a definition from memory — a rebuilt definition silently drops panels and drifts the SQL.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The id of the dashboard to read, as returned by mint_dashboard. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the safety burden. 'Return the stored definition' clearly indicates a read-only operation, and the compatibility with update_dashboard conveys the output's shape and purpose. It does not disclose possible error cases or any side effects, but for a simple read of one resource this is largely sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: the core behavior is front-loaded, and the second sentence justifies the workflow with a concrete consequence. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read tool with full schema coverage and no output schema, the description explains what is returned, why it is useful, and how it connects to update_dashboard. No critical information for calling it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the id parameter and its source. The description adds only that the dashboard must exist, which is marginal over the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the exact action (return the stored definition), the resource (an existing dashboard), and the purpose (editing and handing to update_dashboard). This distinguishes it from list_dashboards/describe_dashboards, which return summaries rather than the full editable definition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says to call this before update_dashboard and contrasts it with rebuilding a definition from memory. This gives the agent a clear decision rule and warns against a costly alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.
The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.
With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.
The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.