grafana_query

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

With no annotations, the description fully discloses read-only nature, potential for large raw frames, and default digest format. This covers safety and size risks.

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?

Three sentences with no wasted words. First sentence states purpose and endpoint, second and third provide usage and behavior. Efficient and 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?

Given no output schema, description explains return format (digest vs raw). Covers all required and optional parameters, references another tool, and mentions query language variations. Complete for a query 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?

Schema has 83% description coverage; description adds value by explaining datasource_uid source, expr language types, optional time range, and raw effect. Slight improvement over schema alone.

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 it is a 'Read-only metric/log query' via a specific API endpoint, with verb 'query' and resource 'metric/log'. It distinguishes from sibling tools like grafana_health or grafana_search_dashboards.

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 provides explicit prerequisite (datasource uid from grafana_list_datasources), explains the raw parameter trade-off, and suggests default time range. It does not explicitly say when not to use but gives enough context.

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