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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds context beyond annotations by clarifying this lists all supported datasources (a catalog) versus account-connected ones, and specifies the return fields. This is useful behavioral context, though the output schema likely covers the fields, so the description doesn't need to elaborate further.

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?

Two sentences, zero waste. The purpose and returned fields are front-loaded, followed by clear usage guidance and the sibling alternative. Every sentence earns its place.

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 no-parameter list tool with an output schema, the description is complete. It tells the agent what it returns, when to call it, and how to proceed next (describe_datasource), plus the alternative for connected data. Nothing essential is missing.

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 tool has zero parameters, and schema coverage is trivially 100%. Per the rubric, a 0-parameter tool gets a baseline of 4. The description doesn't add parameter meaning (none exist) but doesn't need to.

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 ('List every datasource Oviond supports') and the resource (datasources), and specifies the returned fields (id, name, category, data views). It distinguishes itself from siblings like describe_datasource and connected_datasources, making its purpose unambiguous.

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?

Provides explicit guidance: 'Call this FIRST to find the datasource_id, then describe_datasource for that datasource's views, metrics and dimensions.' It also names the alternative for account-specific data: 'For what this account has actually connected, use connected_datasources.' No ambiguity remains.

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