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Brucedh

AWS‑IReveal‑MCP

cloudtrail_describe_trails

Retrieve details of all AWS CloudTrail trails configured in your account to monitor and audit API activity.

Instructions

Describe all CloudTrail trails configured in the AWS account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:18-29 (handler)
    The main handler function for the 'cloudtrail_describe_trails' tool. It is decorated with @mcp.tool() which registers it in the MCP server. The function uses the AWS boto3 CloudTrail client to call describe_trails() and returns the list of trails or an error message.
    @mcp.tool()
    async def cloudtrail_describe_trails() -> list:
        """
        Describe all CloudTrail trails configured in the AWS account.
        """
        try:
            cloudtrail_client = boto3.client('cloudtrail')
            response = cloudtrail_client.describe_trails()
            trails = response.get('trailList', [])
            return trails
        except Exception as e:
            return f"Error describing trails: {str(e)}"
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 what the tool does but lacks details on behavior: no mention of output format (e.g., JSON list), pagination, rate limits, authentication requirements, or error handling. This is a significant gap for a tool with zero annotation coverage.

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

Conciseness5/5

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

The description is a single, clear sentence that directly states the tool's purpose without any fluff or repetition. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., trail configurations, statuses) or behavioral aspects like permissions or limitations. For a tool that likely returns structured data about AWS resources, more context is needed to guide effective use.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, avoiding redundancy. A baseline score of 4 is applied as it efficiently handles the lack of parameters without adding unnecessary information.

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 action ('Describe') and resource ('all CloudTrail trails configured in the AWS account'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'cloudtrail_lookup_events' or 'athena_query_events', which also involve CloudTrail data but serve different purposes.

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 prerequisites (e.g., AWS permissions), use cases (e.g., auditing, troubleshooting), or comparisons to siblings like 'cloudtrail_lookup_events' for event-level queries or 'athena_query_events' for complex analysis.

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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