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mint_dashboard

Check first: list_dashboards, then get_dashboard on anything close. If an existing dashboard answers this question, edit its definition and update_dashboard it instead of minting a second — two dashboards for one question leave no way to tell which is authoritative, and they drift apart. Minting a title that already exists is refused.

Validate, ground, store, and mint a Fixter dashboard link. Call describe_dashboards before first use — it defines the definition JSON this tool accepts.

The dashboard is stored and the link is short and stable: update_dashboard changes what the same link shows, so hand the user both the link and the id. This tool refuses to store definitions that would render broken. It checks the structure (grid rows, panel roles, units, environment scoping), then executes every panel's SQL against your live data — variables resolved, placeholders substituted — and reports empty panels, legend overflows, and dead series. Errors block the link; warnings ship with it and belong in your handover message.

Compose with grounded queries (run_sql) first; this tool verifies, it does not design.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rangeNoOptional range override for the link, <n>m|h|d, e.g. 24h.
refreshNoOptional refresh interval override in ms; 0 disables.
definitionJsonYesThe dashboard definition as a JSON string, schema per describe_dashboards.

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses duplicate-title refusal, structural validation, live SQL execution, empty-panel and dead-series reporting, and the distinction between blocking errors and non-blocking warnings.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense with actionable guidance; most sentences earn their place by explaining behavior, failure modes, or workflow context. It is not perfectly front-loaded since it opens with workflow instructions rather than a crisp one-line purpose, but the structure remains effective for agent use.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of annotations and output schema, the description is remarkably complete. It covers prerequisites, validation behavior, failure semantics, duplicate prevention, link stability, and handover guidance, leaving little ambiguity about what the tool does and how to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds context about definitionJson requiring a valid dashboard definition and that SQL will be executed, but it does not meaningfully expand on range or refresh beyond what the schema already documents.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: 'Validate, ground, store, and mint a Fixter dashboard link,' which is a specific verb+resource pairing. It distinguishes this minting operation from update_dashboard and delete_dashboard by emphasizing creation plus validation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: check list_dashboards first, use get_dashboard on close matches, and update_dashboard instead of creating a duplicate. It also directs the agent to call describe_dashboards before first use and to compose with run_sql first, clearly defining the tool as a verifier rather than a designer.

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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TDQS

A3.8/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness5/5

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.

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