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RadiumGu
by RadiumGu

list_experiments

Retrieve all AWS Fault Injection Service experiments in a specified region to monitor and manage chaos engineering tests.

Instructions

List all AWS FIS experiments in the specified region.

Args:
    region: AWS region to query (default: us-east-1)
    
Returns:
    JSON string containing experiments information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNous-east-1

Implementation Reference

  • The core handler function for the 'list_experiments' tool. It uses boto3 to call fis.list_experiments(), processes the response by extracting key fields and serializing datetimes to ISO format, then returns a formatted JSON string of the experiments list.
    def list_experiments(region: str = "us-east-1") -> str:
        """
        List all AWS FIS experiments in the specified region.
        
        Args:
            region: AWS region to query (default: us-east-1)
            
        Returns:
            JSON string containing experiments information
        """
        try:
            fis = boto3.client('fis', region_name=region)
            response = fis.list_experiments()
            
            if not response.get('experiments'):
                return "No experiments found in region " + region
            
            # Format the response for better readability
            experiments = []
            for experiment in response['experiments']:
                experiments.append({
                    'id': experiment.get('id'),
                    'experimentTemplateId': experiment.get('experimentTemplateId'),
                    'state': experiment.get('state', {}).get('status'),
                    'startTime': experiment.get('startTime').isoformat() if experiment.get('startTime') else None,
                    'endTime': experiment.get('endTime').isoformat() if experiment.get('endTime') else None,
                    'tags': experiment.get('tags', {})
                })
            
            return json.dumps(experiments, indent=2)
        except Exception as e:
            return f"Error listing experiments: {str(e)}"
  • Registers the list_experiments function as an MCP tool using FastMCP's app.tool() decorator.
    app.tool()(list_experiments)
  • Imports the list_experiments function from tools.py for use in the server registration.
    from aws_fis_mcp.tools import (
        list_experiment_templates,
        get_experiment_template,
        list_experiments,
        get_experiment,
        start_experiment,
        stop_experiment,
        create_experiment_template,
        delete_experiment_template,
        list_action_types,
        generate_template_example,
        set_write_mode,
    )
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the return format ('JSON string') but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, rate limits, pagination, or error handling. For a cloud service tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded with the core purpose. The structured 'Args' and 'Returns' sections are clear, though slightly redundant with the opening sentence. Every sentence adds value, but minor trimming could improve efficiency.

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 1 parameter, no annotations, and no output schema, the description is minimally adequate. It covers the basic operation and parameter but lacks details on authentication, error cases, or output structure beyond 'JSON string'. For a cloud API tool, this leaves gaps in usability for an AI agent.

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

Parameters4/5

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

With only 1 parameter and 0% schema description coverage, the description compensates well by explaining the parameter's purpose ('AWS region to query') and providing a default value. It adds meaningful context beyond the schema's basic type and title, though it doesn't detail format constraints or valid region values.

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 verb ('List') and resource ('AWS FIS experiments') with scope ('in the specified region'), making the purpose specific and understandable. It distinguishes from siblings like 'get_experiment' (single item) and 'list_experiment_templates' (different resource), though not explicitly named. A 5 would require explicit sibling differentiation.

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?

No guidance is provided on when to use this tool versus alternatives like 'list_experiment_templates' or 'get_experiment'. The description implies usage for listing experiments but lacks context on prerequisites, filtering, or exclusions. This leaves the agent with minimal direction for tool selection.

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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