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 of behavioral disclosure but fails to mention critical traits: computational cost, execution duration, whether results are cached, or side effects. While it mentions 'sensitivity analysis' in the returns section, it does not explain what behavioral characteristics the agent should expect (e.g., long-running operation, resource-intensive backtesting).

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 uses a structured Args/Returns format that efficiently conveys parameter information. The first two sentences are slightly redundant ('Find... through systematic testing' vs 'Tests... to identify'), but overall it avoids verbosity while covering the necessary parameter documentation given the schema gaps.

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?

The description adequately covers the input parameters (compensating for the schema) and acknowledges the return value (sensitivity analysis), which is acceptable since an output schema exists. However, for a computationally intensive optimization tool with no annotations, it lacks important contextual details like execution time expectations, error conditions, or differentiation from similar optimization utilities in the same server.

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 the schema has 0% description coverage, the description compensates effectively by documenting all 4 parameters with clear meanings and helpful examples (e.g., 'period', 'threshold' for param_name; '10-100' for param_range). This adds significant value beyond the raw schema, though it could further clarify format constraints for the range parameter.

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 finds optimal parameter values through systematic testing and identifies best settings. It specifies the domain (parameter optimization) and method (systematic testing/range testing). However, it does not explicitly distinguish this from sibling optimization tools like 'optimization_run' or 'optimization_walk_forward', which would help clarify its specific niche.

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 description provides no guidance on when to use this tool versus the numerous sibling optimization tools (optimization_run, optimization_walk_forward, strategy_analyze_optimization_impact, etc.). It lacks 'when-to-use' or 'when-not-to-use' criteria, and does not mention prerequisites like requiring an existing strategy or historical data availability.

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