stress_test_scenarios

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions 'real market data integration' but does not disclose side effects, return format, potential network dependencies, latency, or that it does not modify data. The schema hints at some behaviors (e.g., use_market_data), but the description adds no extra behavioral context beyond a basic action statement.

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 a single sentence, which is concise, but it is under-specified for a tool with 10 parameters. It repeats the essence of the tool's name ('stress_test_scenarios' becomes 'stress testing') without adding efficient, unique value. It lacks any structure or breakdown, though it is not verbose.

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?

Given the tool's complexity (10 parameters, no output schema), a one-sentence description is inadequate. It doesn't explain what results are returned, how to interpret stress test outcomes, or the role of default historical scenarios. The schema provides parameter details, but the description fails to provide a complete picture of the tool's behavior and context.

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 all 10 parameters have individual descriptions. The tool description adds no additional parameter insights, examples, or interdependencies. Baseline 3 is appropriate because the schema does the heavy lifting and the description doesn't compensate or enhance.

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 the tool's function: 'Perform stress testing across different market conditions with real market data integration'. It uses a specific verb (perform) and resource (stress testing), and the mention of 'market conditions' and 'real market data' distinguishes it from Monte Carlo simulation or backtesting. However, it doesn't explicitly mention that it's for structured products or financial instruments, which would further differentiate it from similar analysis 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?

No guidance is provided on when to use this tool versus alternatives like run_monte_carlo_simulation or run_backtesting_analysis. The description does not state use cases, prerequisites, or exclusions. It only says what it does, not when to employ it.

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