Skip to main content
Glama
oldcoder01
by oldcoder01

run_checks

Execute security and operational checks on AWS resources to identify compliance issues and generate actionable reports with cost analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
snapshot_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'run_checks' MCP tool, decorated with @mcp.tool for automatic registration. It loads the snapshot JSON, runs multiple imported check functions to generate findings, saves the findings, and returns a summary with finding_set_id (matching snapshot_id) and the count of findings.
    @mcp.tool
    def run_checks(snapshot_id: str) -> Dict[str, Any]:
        p = os.path.join(snapshot_dir(DATA_DIR, snapshot_id), "snapshot.json")
        snap = read_json(p)
    
        findings: List[Dict[str, Any]] = []
        # Exposure
        findings.extend([f.__dict__ for f in check_sg_world_open(snap)])
        findings.extend([f.__dict__ for f in check_public_instances(snap)])
        findings.extend([f.__dict__ for f in check_unassociated_eips(snap)])
        findings.extend([f.__dict__ for f in check_unattached_ebs(snap)])
        # Telemetry signals
        findings.extend([f.__dict__ for f in check_cloudtrail_present(snap)])
        findings.extend([f.__dict__ for f in check_cloudwatch_alarm_signal(snap)])
        # Data protection
        findings.extend([f.__dict__ for f in check_unencrypted_ebs(snap)])
        findings.extend([f.__dict__ for f in check_rds_public_or_low_backup(snap)])
        # Health
        findings.extend([f.__dict__ for f in check_unhealthy_targets(snap)])
    
        save_findings(DATA_DIR, snapshot_id, findings)
        # v1: 1:1 mapping of finding_set_id to snapshot_id
        return {"finding_set_id": snapshot_id, "count": len(findings)}
  • The @mcp.tool decorator registers the run_checks function as an MCP tool.
    @mcp.tool
Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/oldcoder01/aws-mcp-audit'

If you have feedback or need assistance with the MCP directory API, please join our Discord server