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Brucedh

AWS‑IReveal‑MCP

accessanalyzer_list_findings

Retrieve and filter security findings from AWS IAM Access Analyzer to identify resource access risks and compliance issues.

Instructions

List findings for an analyzer, with optional filter.
filter: {'resourceType': {'eq': ['AWS::S3::Bucket']}, ...}

Parameters:
    aws_region (str): The AWS region - use 'us-east-1' if not specified.
    analyzer_arn (str): The ARN of the analyzer to list findings for.
    filter (dict, optional): Filter criteria for findings.
    max_results (int): Maximum number of findings to return.

Returns:
    str: JSON-formatted list of findings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aws_regionYes
analyzer_arnYes
filterNo
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'accessanalyzer_list_findings' MCP tool. It uses the AWS Access Analyzer client to list findings with pagination support, applies optional filters, and returns JSON-formatted results using a custom DateTimeEncoder.
    @mcp.tool()
    async def accessanalyzer_list_findings(
        aws_region: str,
        analyzer_arn: str,
        filter: dict = None,
        max_results: int = 50
    ) -> str:
        """
        List findings for an analyzer, with optional filter.
        filter: {'resourceType': {'eq': ['AWS::S3::Bucket']}, ...}
    
        Parameters:
            aws_region (str): The AWS region - use 'us-east-1' if not specified.
            analyzer_arn (str): The ARN of the analyzer to list findings for.
            filter (dict, optional): Filter criteria for findings.
            max_results (int): Maximum number of findings to return.
    
        Returns:
            str: JSON-formatted list of findings.
        """
        client = boto3.client('accessanalyzer', region_name=aws_region)
        params = {'analyzerArn': analyzer_arn, 'maxResults': max_results}
        if filter:
            params['filter'] = filter
        findings = []
        next_token = None
        while True:
            if next_token:
                params['nextToken'] = next_token
            response = client.list_findings(**params)
            summaries = response.get('findingSummaries', [])
            findings.extend(summaries)
            next_token = response.get('nextToken')
            if not next_token:
                break
        return json.dumps(findings, indent=2, cls=DateTimeEncoder)
  • Custom JSON encoder used in the tool to serialize datetime objects to ISO format strings.
    class DateTimeEncoder(json.JSONEncoder):
        def default(self, o):
            if isinstance(o, datetime.datetime):
                return o.isoformat()  # Convert datetime to ISO-format string.
            return super().default(o)
  • server.py:968-968 (registration)
    The @mcp.tool() decorator registers the function as an MCP tool with the name matching the function name 'accessanalyzer_list_findings'.
    @mcp.tool()
Behavior3/5

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

With no annotations provided, the description carries the full burden. It mentions optional filtering and a max_results parameter, which hints at pagination/limiting behavior, but lacks details on permissions, rate limits, error handling, or what 'JSON-formatted list' entails. It adds some context but is incomplete for a mutation-like list operation.

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 with a purpose statement, parameter details, and return info in bullet points. Every sentence adds value, though the filter example could be more concise. It's front-loaded with the core functionality.

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

Completeness4/5

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

Given no annotations, 4 parameters with 0% schema coverage, and an output schema (implied by 'Returns'), the description does well by explaining all parameters and the return format. However, it lacks behavioral details like auth needs or error cases, keeping it from a 5.

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?

Schema description coverage is 0%, so the description must compensate. It provides clear semantics for all 4 parameters: aws_region (with a default example), analyzer_arn, filter (with an example structure), and max_results. This adds significant value beyond the bare schema, though it could detail filter syntax more.

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

Purpose5/5

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 ('findings for an analyzer'), and distinguishes it from siblings like 'accessanalyzer_get_finding' (singular) and 'accessanalyzer_list_analyzers' (different resource). The mention of 'optional filter' adds specificity about functionality.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing findings with filtering, but does not explicitly state when to use this vs. alternatives like 'accessanalyzer_get_finding' (for a single finding) or other security tools in the sibling list. No exclusions or prerequisites are mentioned.

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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