rca_analysis_ensemble

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

The description goes beyond annotations by detailing the algorithm (step-by-step), return format (JSON with specific fields), and the effect of the 'save' parameter. Annotations indicate readOnlyHint=false and destructiveHint=false, and the description confirms this is a compute operation with optional persistence, with no contradictions.

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 well-structured with sections (overview, algorithm, Args, Returns) and front-loads the purpose. While it is fairly long, every section adds value. It could be slightly more concise, but the structure aids readability.

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?

The description covers the algorithm, inputs, and output format comprehensively. However, it lacks explicit details on error handling, prerequisites (e.g., valid token, model existence), and internal dependencies like dispatch_rca(). Overall, it is sufficient for understanding the tool's core behavior.

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?

Although the input schema has 0% description coverage at the top level, the tool description summarizes the parameters (model_ids, payload, weights, save, token, client_id) and provides context for the EnsembleInput. This compensates well, but more detail on token and client_id would improve clarity.

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's function: 'Run multiple RCA models on the same payload and combine root cause scores via weighted voting.' It specifies the verb (run/combine) and resource (RCA models and payload), distinguishing it from sibling tools like rca_analysis_run which likely runs a single model.

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 provides clear context for when to use this tool (when multiple models are available and want to ensemble results) but does not explicitly state when not to use it or name alternatives. The mention of weighted voting implies usage scenarios, but exclusions are missing.

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