Fetch SONA learning statistics for every worker or one worker. Use these insights to assess performance and target improvements.
Return SONA learning statistics for all workers or a specific worker.
| Name | Required | Description | Default |
|---|---|---|---|
| workerId | No |
| Name | Required | Description | Default |
|---|---|---|---|
| sona | Yes |
Changes observed during successful MCP inspections.
v0.2.8Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. 'Return' strongly implies a read-only operation with no side effects, and 'all workers or a specific worker' clarifies input behavior. However, it does not disclose auth requirements, rate limits, or other behavioral traits.
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 a single, front-loaded sentence with no filler. Every word contributes to clarifying the tool's purpose and scope.
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 optional parameter and an output schema, the description covers the essential purpose and scope. It is slightly thin on explicit usage guidance and behavioral caveats, but the tool's simplicity and the presence of an output schema mitigate those gaps.
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 coverage is 0%, but the description compensates by indicating that results can cover all workers or be narrowed to a specific worker. This adds meaning beyond the bare 'workerId' string property, though it does not name the parameter directly or specify its format.
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 a specific verb ('Return'), names the resource ('SONA learning statistics'), and specifies the scope ('all workers or a specific worker'). This clearly distinguishes it from the code-generation and worker-management sibling tools.
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 when to use the tool: whenever SONA learning statistics are needed, optionally filtered by a worker. However, it gives no explicit guidance about when not to use it or which alternative might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.