calculate_sacr_exposure

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

No annotations are provided, so the description carries the full disclosure burden. It does add real value by disclosing the computation itself, including the EAD formula (1.4 * (RC + PFE)), which tells the agent the tool is a deterministic regulatory calculation. However, it says nothing about side effects, whether results are persisted, validation failures on malformed trades, or how margined vs. unmargined netting sets alter the calculation, leaving notable gaps for a 7-parameter financial model.

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?

A single, front-loaded sentence that begins with the verb and standard, then lists outputs. Every clause carries information and nothing is repeated. It is dense but not padded, though the output enumeration could arguably be split for readability.

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?

With no output schema, the description correctly compensates by naming the five returned metrics, so the agent knows what to expect back. Combined with 100% schema coverage on the inputs, the agent has enough to call the tool. What is missing is usage context — when this standardized approach applies versus the Monte Carlo sibling — which keeps it from a 5.

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 the schema already documents every parameter including threshold, is_margined, collateral_posted, and MTA. The description adds no parameter-level detail beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 names a specific verb (Calculates) and a precise resource (Basel III / BCBS 279 SA-CCR metrics), then enumerates the exact outputs produced: RC, PFE, Supervisory Add-on, Multiplier, and EAD. This is unambiguous about what the tool does. It does not, however, distinguish itself from the siblings simulate_monte_carlo_pfe, compute_portfolio_var, or calculate_portfolio_greeks, so it falls short of a 5.

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 statement of when to use the standardized SA-CCR approach versus the simulation-based sibling simulate_monte_carlo_pfe, nor any prerequisite or eligibility conditions (e.g., which counterparties qualify for the standardized approach). The agent is left to infer usage entirely from the regulatory name.

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