The complete guide to composing a Fixter dashboard: the definition format, panel kinds and their roles, how to choose chart forms from the measure, grid layout rules, units, environment scoping, and what mint_dashboard validates. Call this once before composing your first dashboard in a conversation.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the tool's behavior as an informational guide, listing its content areas. The phrase 'complete guide' and the imperative 'Call this once' imply a safe, read-only operation. It could explicitly state it has no side effects, but for a describe-style tool, the behavioral transparency is strong.
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?
The description is concise yet dense, packing relevant information into three sentences. It front-loads the core purpose ('complete guide') and follows with a clear list of covered topics and a usage instruction. Every sentence earns its place with no redundancy.
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?
Given no input schema, no output schema, and no annotations, the description fully compensates by explaining the tool's scope and usage. It covers what the guide contains, when to call it, and its relationship to mint_dashboard. This is complete for a zero-parameter informational tool.
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?
The tool has zero parameters, so the description need not explain parameter usage. Baseline for 0 params is 4. The description focuses entirely on what the tool provides, which 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 clearly identifies the tool as a comprehensive guide to composing Fixter dashboards, listing specific aspects it covers (definition format, panel kinds, chart forms, grid layout, units, environment scoping). It distinguishes itself from siblings like mint_dashboard (which creates) by explicitly mentioning what mint_dashboard validates, making the purpose unambiguous.
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?
Usage guidance is explicit: 'Call this once before composing your first dashboard in a conversation.' This tells the agent exactly when to use it. However, it does not explicitly state when not to use it or name alternative tools, though it implicitly differentiates from mint_dashboard by referencing its validation role.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.