Jesse MCP Server

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

With no annotations provided, the description carries the full burden. It successfully discloses the intellectual purpose (detecting luck/overfitting) and references return values ('Statistical significance assessment'), but omits operational details like computational cost, caching behavior, or whether this modifies existing backtest records.

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 docstring format with Args/Returns sections is structurally clear and appropriately sized. The content is front-loaded with the core purpose in the first sentence, though the Args/Returns sections in the description text are somewhat redundant given the structured schema fields.

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 2-parameter analysis tool with an output schema present, the description provides adequate context. It covers the validation purpose, parameter meanings, and return type. Missing only prerequisite context (e.g., requires existing backtest results) to be fully complete.

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?

Given 0% schema description coverage, the description compensates by providing basic semantics: 'strategy_name: Strategy to validate' and 'pair: Trading pair.' While functional, it lacks format examples, constraints, or enumeration details that would make parameter usage unambiguous.

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 validates 'backtest results are statistically significant,' specifying the statistical focus. However, it doesn't explicitly differentiate from similar siblings like 'backtesting_validate' or 'backtest_monte_carlo,' leaving some ambiguity about when to choose this specific validation method.

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 phrase 'Ensures results are real, not luck or overfitting' implies when to use this tool (when concerned about overfitting), but lacks explicit guidance on prerequisites (e.g., requires completed backtests) or comparison to alternative validation approaches available in siblings.

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