list_model_groups

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

Annotations already declare readOnlyHint=true, and the description aligns by stating 'List all model groups'. It adds valuable behavioral context about the model_group structure (ties portfolio to conditioning set) and the model_type field semantics (null/absent = Moment, flow_generative = Flow), going beyond the annotation.

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 three compact sentences, each earning its place: what the tool lists, the structure/meaning of model groups, and how to use the results. It is front-loaded with the action and resource, and there is no redundant wording.

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 parameterless list tool, the description provides all essential context: what is listed, how results relate to other tools, and how to interpret model_type. An output schema exists to detail return fields, so the description doesn't need to enumerate them, making it complete for its complexity.

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?

The tool has zero parameters, so the baseline is 4. The description correctly focuses on output semantics rather than parameters, which is appropriate for a parameterless list operation.

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 'List all' with a clear resource 'model groups' and states they are created by analyze_quantitative. It also distinguishes itself by explaining the model_group_id is needed for compute_betas and compute_returns, which differentiates it from 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 explicitly says 'Use this to find model_group_ids for compute_betas, compute_returns', providing clear when-to-use guidance. It also mentions checking model_type to identify model kinds, which helps the agent interpret results and decide subsequent actions.

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