decision_analysis

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

With no annotations, the description fully covers behavior: returns a markdown report with ranked options, per-option breakdown (score × weight contribution, strengths, weaknesses), and a recommendation. It also discloses that weights are normalized to sum to 1, providing clear expectations.

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 sentences: first states purpose, second details output, third explains weights. No redundant information, front-loaded with the core action, and efficiently organized.

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 output format (markdown report with rankings, breakdown, recommendation) since no output schema exists. It explains the weight normalization and default behavior. Schema covers input constraints (maxItems 100, score range). The tool's purpose is fully addressed without 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 100%, so baseline is 3. The description adds value by explaining the score matrix structure ('one row per option, one score (0-10) per criterion') and the behavior of weights (normalization, equal weighting if omitted), which goes beyond the schema descriptions.

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 'Rank options against weighted criteria with a weighted-sum decision matrix', specifying the verb (Rank), resource (options against criteria), and method (weighted-sum matrix). It distinguishes from sibling statistical analysis tools by focusing on decision ranking with criteria weights.

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 indicates when to use: for ranking options against weighted criteria, with a note on omitting weights for equal weighting. It does not explicitly exclude alternatives or mention when not to use, but the context of sibling tools provides sufficient differentiation.

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