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title Chapter 5: Data, Knowledge, and Agent Workflows
nav_order 5
parent awslabs/mcp Tutorial

Chapter 5: Data, Knowledge, and Agent Workflows

Welcome to Chapter 5: Data, Knowledge, and Agent Workflows. In this part of awslabs/mcp Tutorial: Operating a Large-Scale MCP Server Ecosystem for AWS Workloads, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter explains how documentation and data-oriented servers improve context quality for coding and operations agents.

Learning Goals

  • use documentation/knowledge servers to reduce stale-model assumptions
  • combine data-oriented servers for richer troubleshooting and planning
  • structure workflows that separate retrieval from action execution
  • choose server combinations by task complexity and risk

Workflow Pattern

Use knowledge and documentation servers first to build accurate context, then invoke mutating or operational servers only after intent and constraints are clear.

Source References

Summary

You now have a context-first approach for data and knowledge enriched MCP workflows.

Next: Chapter 6: Security, Credentials, and Risk Controls

Depth Expansion Playbook

Source Code Walkthrough

scripts/verify_package_name.py

The verify_package_name_consistency function in scripts/verify_package_name.py handles a key part of this chapter's functionality:

def verify_package_name_consistency(
    package_name: str, references: List[Tuple[str, int]]
) -> Tuple[bool, List[str]]:
    """Verify that package references match the actual package name."""
    # Extract just the package name part (without version)
    base_package_name = package_name.split('@')[0] if '@' in package_name else package_name

    issues = []

    for ref, line_num in references:
        # Extract package name from reference (remove version if present)
        ref_package = ref.split('@')[0] if '@' in ref else ref

        if ref_package != base_package_name:
            issues.append(
                f"Package name mismatch: found '{ref_package}' but expected '{base_package_name}' (line {line_num})"
            )

    return len(issues) == 0, issues


def main():
    """Main function to verify package name consistency."""
    parser = argparse.ArgumentParser(
        description='Verify that README files correctly reference package names from pyproject.toml'
    )
    parser.add_argument(
        'package_dir', help='Path to the package directory (e.g., src/amazon-neptune-mcp-server)'
    )
    parser.add_argument('--verbose', '-v', action='store_true', help='Enable verbose output')

This function is important because it defines how awslabs/mcp Tutorial: Operating a Large-Scale MCP Server Ecosystem for AWS Workloads implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[verify_package_name_consistency]
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