get_dashboard

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

With no annotations, the description carries the full burden. It discloses the read-only nature implicitly with 'Fetch' and specifies exactly what is returned, including panels, datasource, raw query languages, and template variables, which is sufficient for a simple read operation.

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?

A single sentence that front-loads the core action ('Fetch a dashboard by uid') and then adds valuable details about the return payload and use case. It is efficient with no fluff, though slightly long with the parenthetical and appositive.

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 simple tool with one parameter and no output schema, the description covers the key aspects: input, output, and purpose. It says what the dashboard fetch returns, and the schema covers the parameter source, making it complete enough for an agent to invoke it correctly.

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%, with 'uid' already described as 'Dashboard uid from search_dashboards'. The description mentions 'by uid' but adds no new meaning beyond the schema, so the baseline of 3 applies.

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 states the action 'Fetch a dashboard by uid' and the resource, then details the output: panels, datasource, raw queries, and template variables. It distinguishes itself from siblings like query_sql and search_dashboards by emphasizing query reuse rather than execution or searching.

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?

The phrase 'the fastest way to reuse a query someone already wrote' provides clear context for when to use this tool. It implicitly excludes query execution tools, though it does not explicitly name alternatives or state when not to use it, giving clear context without exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.