run_hmm

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

No annotations provided, so description carries full burden. Discloses key behaviors: discovers 'n_states hidden regimes', returns 'decoded state sequence, transition matrix, and per-state Gaussian parameters', and clarifies unsupervised nature. Lacks mention of convergence behavior or computational intensity, but covers core functionality well.

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?

Three efficient sentences with zero waste. First establishes tool identity, second distinguishes from siblings, third describes outputs. Information density is high with no redundancy.

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?

Conceptually complete regarding HMM mechanics and outputs (though output schema exists, making some description redundant). However, the practical invocation is severely underdocumented due to the undocumented 'kwargs' parameter. Adequate but with clear gaps in input specification.

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 has 0% coverage with a single opaque 'kwargs' parameter. Description mentions 'n_states' in passing (as *n_states*), providing minimal semantic context for what arguments might be expected, but fails to document the kwargs wrapper structure or enumerate available parameters. With 0% schema coverage, this compensation is insufficient.

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?

Clear verb 'Fit' with specific resource 'Gaussian Hidden Markov Model'. Explicitly distinguishes from siblings by contrasting with 'regression functions' in the second sentence, establishing this as the unsupervised alternative to the supervised regression 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?

Explicitly states when to use: 'unsupervised — there is no target column'. This directly contrasts with supervised siblings (run_linear, run_random_forest, etc.), providing clear selection criteria. The 'Unlike the regression functions' framing gives perfect when-to-use guidance.

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