datasource_health

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only offers a generic [READ] label and does not mention response format, side effects like pinging the datasource, or error behavior. This is insufficient for full transparency.

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 and well-structured, with a leading [READ] tag and a bulleted args list. Every sentence provides necessary information with no wasted words.

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?

Parameters are well covered, but the description omits details about the health response format, potential errors, and any special behaviors. Without an output schema, this leaves the agent without clear expectations for return values or failure modes.

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 description explains both parameters clearly: datasource_id as a numeric id from list_datasources and target as a Grafana target name with a default. Since the schema has no descriptions, this fully compensates and adds meaningful context beyond the schema.

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 tool reads the health of one Grafana datasource, with the [READ] tag and specification of a single datasource. This distinguishes it from listing tools like list_datasources, though it does not explicitly name alternatives.

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 implies a workflow by mentioning that datasource_id comes from list_datasources, but it does not explicitly state when to use this tool versus alternatives or any exclusions. Usage guidance is present but only implied.

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