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RadiumGu
by RadiumGu

list_experiment_templates

Retrieve all AWS Fault Injection Service experiment templates in a specified region to manage chaos engineering experiments.

Instructions

List all AWS FIS experiment templates in the specified region.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNous-east-1

Implementation Reference

  • The core handler function that lists AWS FIS experiment templates using boto3, formats them into readable JSON, manually serializes datetimes, and handles exceptions.
    def list_experiment_templates(region: str = "us-east-1") -> str:
        """
        List all AWS FIS experiment templates in the specified region.
        
        Args:
            region: AWS region to query (default: us-east-1)
            
        Returns:
            JSON string containing experiment templates information
        """
        try:
            fis = boto3.client('fis', region_name=region)
            response = fis.list_experiment_templates()
            
            if not response.get('experimentTemplates'):
                return "No experiment templates found in region " + region
            
            # Format the response for better readability
            templates = []
            for template in response['experimentTemplates']:
                templates.append({
                    'id': template.get('id'),
                    'name': template.get('experimentTemplateId'),
                    'description': template.get('description'),
                    'creationTime': template.get('creationTime').isoformat() if template.get('creationTime') else None,
                    'lastUpdateTime': template.get('lastUpdateTime').isoformat() if template.get('lastUpdateTime') else None,
                    'tags': template.get('tags', {})
                })
            
            return json.dumps(templates, indent=2)
        except Exception as e:
            return f"Error listing experiment templates: {str(e)}"
  • Registers the list_experiment_templates function as a tool in the FastMCP server.
    app.tool()(list_experiment_templates)
  • Imports the list_experiment_templates handler along with other tools for 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('List') and return type ('JSON string'), but lacks details on permissions, rate limits, pagination, or error handling. This is inadequate for a tool that likely interacts with AWS services, where such context is critical.

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 in the first sentence and parameter/return details in a structured format. It avoids unnecessary verbosity, though the 'Args' and 'Returns' sections could be more integrated into the flow for slightly better readability.

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

Completeness2/5

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

Given the complexity of AWS FIS operations, no annotations, and no output schema, the description is incomplete. It doesn't explain the structure of the returned JSON, error cases, or dependencies like required IAM permissions. For a tool in this context, more behavioral and output details are needed to be fully helpful.

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?

The description adds meaningful context for the single parameter ('region') by specifying its purpose ('AWS region to query') and default value ('us-east-1'), which compensates for the 0% schema description coverage. Since there's only one parameter, this is sufficient to achieve a high score, though it doesn't elaborate on format or constraints.

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 experiment templates') with scope ('in the specified region'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_experiments' or 'list_action_types', 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 such as 'list_experiments' or 'get_experiment_template'. It mentions the region parameter but offers no context about prerequisites, timing, or exclusions, leaving usage decisions ambiguous.

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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