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monte_carlo_uncertainty_analysis

Analyze spacecraft trajectory uncertainty using Monte Carlo simulations to quantify risks and variations in aerospace mission planning.

Instructions

Perform Monte Carlo uncertainty analysis on spacecraft trajectory.

Args: nominal_trajectory: Nominal trajectory parameters uncertainty_parameters: Parameters with uncertainty distributions n_samples: Number of Monte Carlo samples analysis_options: Optional analysis settings

Returns: JSON string with uncertainty analysis results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nominal_trajectoryYes
uncertainty_parametersYes
n_samplesNo
analysis_optionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The primary handler function for the 'monte_carlo_uncertainty_analysis' tool. It wraps an optional implementation from trajopt.py, serializes results to JSON, and provides graceful error handling including 'not available' message when dependencies are missing.
    def monte_carlo_uncertainty_analysis(
        nominal_trajectory: dict,
        uncertainty_parameters: dict,
        n_samples: int = 1000,
        analysis_options: dict | None = None,
    ) -> str:
        """Perform Monte Carlo uncertainty analysis on spacecraft trajectory.
    
        Args:
            nominal_trajectory: Nominal trajectory parameters
            uncertainty_parameters: Parameters with uncertainty distributions
            n_samples: Number of Monte Carlo samples
            analysis_options: Optional analysis settings
    
        Returns:
            JSON string with uncertainty analysis results
        """
        try:
            from ..integrations.trajopt import (
                monte_carlo_uncertainty_analysis as _monte_carlo,
            )
    
            result = _monte_carlo(
                nominal_trajectory,
                uncertainty_parameters,
                n_samples,
                analysis_options or {},
            )
    
            return json.dumps(result, indent=2)
    
        except ImportError:
            return "Monte Carlo analysis not available - install optimization packages"
        except Exception as e:
            logger.error(f"Monte Carlo analysis error: {str(e)}", exc_info=True)
            return f"Monte Carlo analysis error: {str(e)}"
  • Registers the monte_carlo_uncertainty_analysis tool with the FastMCP server.
    mcp.tool(monte_carlo_uncertainty_analysis)
  • Imports the monte_carlo_uncertainty_analysis function from tools.optimization for registration.
    from .tools.optimization import (
        genetic_algorithm_optimization,
        monte_carlo_uncertainty_analysis,
        optimize_thrust_profile,
        particle_swarm_optimization,
        porkchop_plot_analysis,
        trajectory_sensitivity_analysis,
    )
Behavior2/5

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. It mentions the tool performs 'uncertainty analysis' and returns 'JSON string with uncertainty analysis results,' but lacks critical details such as computational intensity, runtime expectations, error handling, or any side effects. For a complex analysis tool with 4 parameters, this is insufficient to guide safe and effective usage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized, with a clear purpose statement followed by 'Args' and 'Returns' sections. Each sentence serves a purpose without redundancy. It could be slightly more front-loaded by emphasizing the core action earlier, but overall, it's efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, nested objects) and the presence of an output schema (which handles return values), the description is moderately complete. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines. With no annotations, it should do more to explain computational behavior and constraints, making it adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description lists all 4 parameters with brief explanations (e.g., 'Nominal trajectory parameters'), which adds some meaning beyond the input schema's 0% description coverage. However, it doesn't provide details on data formats, units, or constraints (e.g., what distributions are supported for 'uncertainty_parameters'), leaving significant gaps. This partial compensation justifies a baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Perform Monte Carlo uncertainty analysis on spacecraft trajectory.' It specifies the verb ('Perform Monte Carlo uncertainty analysis') and resource ('spacecraft trajectory'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'trajectory_sensitivity_analysis' or 'rocket_3dof_trajectory,' which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 alternatives. It doesn't mention sibling tools like 'trajectory_sensitivity_analysis' or 'rocket_3dof_trajectory,' nor does it specify prerequisites, constraints, or typical use cases. This lack of contextual guidance makes it harder for an agent to select this tool appropriately in isolation.

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

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