Analyze Grafana dashboard

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond annotations by specifying that the tool produces an explanation covering queries, data sources, variables, semantics, and interpretation risks, which tells the agent what kind of analysis to expect. No contradiction with annotations exists.

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 one concise sentence with no filler. The six listed analysis dimensions are dense but all materially describe the tool's behavior, so every word earns its place and the information is front-loaded.

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 single-parameter, read-only analysis tool with an output schema present, the description covers the essential purpose and analysis scope. It could be slightly more complete by mentioning that get_dashboard should be used when raw dashboard JSON is needed, but the combination of annotations, schema, and description is sufficient 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?

The input schema already describes the single required parameter 'uid' as the Grafana dashboard UID at 100% coverage. The description adds no further parameter-level detail, such as how the UID is structured or where it can be found, so the schema carries the full burden.

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 uses the specific verb 'Explain' against the resource 'dashboard' and enumerates the analysis dimensions: structure, queries, data sources, variables, semantics, and interpretation risks. This clearly distinguishes it from fetch-oriented siblings like get_dashboard, which would retrieve raw dashboard data rather than explain it.

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 description states what the tool does but never says when to use it instead of a sibling. It does not mention get_dashboard as the alternative for raw retrieval, nor does it note that this tool is for interpretation and semantic understanding rather than data access.

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