run_bayesian_ridge

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

No annotations provided, so description carries full burden. Adequately discloses return behavior (standard outputs + Bayesian parameters), but omits computational characteristics, convergence criteria, memory requirements, or failure modes. Mentions alpha/lambda as estimated precision parameters, adding useful Bayesian context.

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?

Perfectly structured with two high-density sentences: action (line 1) and return value specification (line 2). No filler words; every term earns its place. Front-loaded with the operation type immediately stated.

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?

Despite having output schema, the description appropriately summarizes returns. However, severely incomplete due to undocumented 'kwargs' parameter—with 0% schema coverage, the description must specify input structure or regression arguments, leaving users without necessary invocation details.

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?

Critical failure: With 0% schema coverage and a single opaque 'kwargs' parameter, the description must explain expected arguments (features, targets, hyperparameters), but provides zero input guidance. Mentioning alpha/lambda as outputs doesn't help with inputs.

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?

Excellent specificity: 'Fit a Bayesian Ridge Regression' provides clear verb+algorithm. Crucially distinguishes from siblings (run_linear, run_svr, etc.) by explicitly mentioning Bayesian-specific outputs (posterior standard deviations, precision parameters alpha/lambda) that unique identify this tool's probabilistic nature.

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?

No explicit when-to-use guidance or comparisons to alternatives. While the mention of Bayesian-specific outputs implies use for uncertainty quantification, there's no explicit guidance like 'use when you need probabilistic estimates vs point estimates from run_linear' or prerequisites.

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