Jesse MCP Server

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

With no annotations provided, the description carries full burden but discloses nothing about computational intensity (10k simulations default), side effects (storage of results), idempotency, or required preprocessing steps for the 'backtest_result' parameter.

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 single sentence is appropriately brief, but suffers from under-specification rather than efficient information density. It wastes the opportunity to front-load critical constraints or prerequisites.

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 complex statistical tool with nested objects and multiple configuration options, the description is inadequate. While an output schema exists (reducing the need for return value documentation), the lack of parameter semantics or behavioral context leaves significant gaps.

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 0%, and the description adds no meaning for any of the 7 parameters (e.g., what 'block_size' controls, valid 'resample_method' values, or the structure of required 'backtest_result'). Complete failure to compensate for schema deficiency.

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 states the core action ('Generate Monte Carlo simulations') and domain ('risk analysis'), but uses vague qualifiers ('comprehensive') and critically fails to distinguish from sibling tool 'backtest_monte_carlo', which likely performs similar functionality on backtest data.

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 provided on when to use this tool versus alternatives like 'backtest_monte_carlo', 'risk_stress_test', or 'risk_var'. Given the overlapping sibling names, explicit selection criteria are essential but absent.

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