optimize_black_litterman

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

With no annotations, the description carries the full burden for behavioral disclosure, but it only states the high-level model and intended benefit. It does not mention how data is fetched, what outputs are returned, or any assumptions or side effects, such as network calls when use_market_data is true.

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 concise, a single sentence with no fluff. It front-loads the key concept 'Black-Litterman portfolio optimization' and adds relevant context about combining views with equilibrium.

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 tool with 11 parameters and no output schema, the description is too sparse. It lacks any details about required inputs, expected outputs, practical use cases, or limitations, making it insufficient for an agent to understand the full scope of the operation.

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 description coverage is 100%, so parameters are fully documented in the schema. The description adds no extra parameter context, but the baseline of 3 applies because the schema already provides necessary details.

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 identifies the tool as Black-Litterman portfolio optimization, combining market equilibrium with investor views. This distinguishes it from sibling tools like optimize_risk_parity and create_black_litterman_views by specifying the exact model and purpose.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any exclusions or refer to sibling tools, leaving the agent to infer usage solely from the model name.

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