aws-knowledge-tool 0.1.0

CLI tool for querying AWS Knowledge MCP Server - search, read, and discover AWS documentation

aws-knowledge-tool

CLI tool for querying AWS Knowledge MCP Server - search, read, and discover AWS documentation

Table of Contents

• About
• Features
• Installation
• Quick Start
• Commands
• Library Usage
• Development
• Known Issues
• Resources

About

aws-knowledge-tool is a CLI-first tool that provides programmatic access to AWS documentation through the AWS Knowledge MCP Server. It features an agent-friendly design with composable commands, structured output, and pipeline support.

What is AWS Knowledge MCP Server?

The AWS Knowledge MCP Server is a remote MCP (Model Context Protocol) server that provides access to:

• AWS Documentation
• AWS Blogs
• AWS Solutions Library
• AWS Architecture Center
• AWS Prescriptive Guidance

Server: https://knowledge-mcp.global.api.aws
Protocol: MCP (JSON-RPC 2.0 over HTTP)
Authentication: None (public, rate-limited)

Why CLI-First?

• Agent-Friendly: Structured commands and error messages enable AI agents (Claude Code, etc.) to reason and act effectively
• Composable: JSON output and stderr separation allow easy piping and integration
• Reusable: Commands serve as building blocks for skills, automation, and workflows
• Reliable: Type-safe, tested, and predictable behavior

Features

• 🔍 Search - Search AWS documentation with pagination
• 📖 Read - Fetch and convert AWS docs to markdown
• 💡 Recommend - Discover related documentation
• 📊 Multi-Level Verbosity - Progressive logging detail (-v/-vv/-vvv)
• 🐚 Shell Completion - Tab completion for bash, zsh, and fish
• 🔒 Security Scanning - Automated secret detection, code security, and dependency checks
• 🤖 Agent-Friendly - Structured JSON output, clear error messages
• 🔗 Composable - Stdin support for pipeline workflows
• 📋 Multiple Formats - JSON and markdown output
• 🎯 Type-Safe - Strict mypy checks, comprehensive type hints
• ✅ Well-Tested - Pytest suite with quality checks

Installation

Prerequisites

• Python 3.14+
• uv package manager

Install Globally

# Clone and install
git clone https://github.com/dnvriend/aws-knowledge-tool.git
cd aws-knowledge-tool
uv tool install .

# Verify installation
aws-knowledge-tool --version

# Optional: Install shell completion
eval "$(aws-knowledge-tool completion bash)"  # For bash
eval "$(aws-knowledge-tool completion zsh)"   # For zsh

Quick Start

# Search AWS documentation
aws-knowledge-tool search "Lambda function URLs"

# Read a documentation page  
aws-knowledge-tool read "https://docs.aws.amazon.com/lambda/latest/dg/welcome.html"

# Get recommendations
aws-knowledge-tool recommend "https://docs.aws.amazon.com/lambda/latest/dg/welcome.html" --type new

# Pipeline composition
aws-knowledge-tool search "Lambda" --json | jq -r '.[0].url' | aws-knowledge-tool read --stdin

Commands

search - Search AWS Documentation

aws-knowledge-tool search QUERY [OPTIONS]

Options:
  -l, --limit INTEGER    Maximum results (default: 10)
  -o, --offset INTEGER   Skip first N results (default: 0)
  --json                 Output JSON format
  --stdin                Read query from stdin
  -v, --verbose          Enable verbose output (use -v for INFO, -vv for DEBUG, -vvv for TRACE)
  -q, --quiet            Suppress non-essential output

Examples:
  # Basic search
  aws-knowledge-tool search "S3 versioning" --limit 5

  # With INFO logging (-v)
  aws-knowledge-tool search "DynamoDB" -v

  # With DEBUG logging and full tracebacks (-vv)
  aws-knowledge-tool search "Lambda" -vv

  # With TRACE logging (includes library internals) (-vvv)
  aws-knowledge-tool search "RDS" -vvv

  # JSON output for pipelines
  aws-knowledge-tool search "Lambda" --json

  # Read from stdin
  echo "Lambda" | aws-knowledge-tool search --stdin

read - Read AWS Documentation

aws-knowledge-tool read URL [OPTIONS]

Options:
  -s, --start-index INT  Starting character index
  -m, --max-length INT   Maximum characters to fetch
  --json                 Output JSON format
  --stdin                Read URL from stdin
  -v, --verbose          Enable verbose output (use -v for INFO, -vv for DEBUG, -vvv for TRACE)
  -q, --quiet            Suppress non-essential output

Examples:
  # Read full document
  aws-knowledge-tool read "https://docs.aws.amazon.com/lambda/latest/dg/welcome.html"

  # Read with pagination
  aws-knowledge-tool read "https://docs.aws.amazon.com/..." --start-index 5000 --max-length 2000

  # Pipeline from search
  aws-knowledge-tool search "Lambda" --json | jq -r '.[0].url' | aws-knowledge-tool read --stdin

  # With DEBUG logging
  aws-knowledge-tool read "https://docs.aws.amazon.com/..." -vv

recommend - Get Documentation Recommendations

aws-knowledge-tool recommend URL [OPTIONS]

Options:
  -t, --type TYPE        Filter by type (highly_rated, new, similar, journey)
  -l, --limit INTEGER    Max results per category (default: 5)
  -o, --offset INTEGER   Skip first N per category (default: 0)
  --json                 Output JSON format
  --stdin                Read URL from stdin
  -v, --verbose          Enable verbose output (use -v for INFO, -vv for DEBUG, -vvv for TRACE)
  -q, --quiet            Suppress non-essential output

Examples:
  # Get all recommendations
  aws-knowledge-tool recommend "https://docs.aws.amazon.com/lambda/latest/dg/welcome.html"

  # Filter by type (find new features)
  aws-knowledge-tool recommend "https://docs.aws.amazon.com/..." --type new

  # Limit results and JSON output
  aws-knowledge-tool recommend "https://docs.aws.amazon.com/..." --json --limit 3

  # With INFO logging
  aws-knowledge-tool recommend "https://docs.aws.amazon.com/..." -v

Multi-Level Verbosity Logging

Control logging detail with progressive verbosity flags. All logs output to stderr, keeping stdout clean for data.

Logging Levels

Flag	Level	Output	Use Case
(none)	WARNING	Errors and warnings only	Production, quiet mode
-v	INFO	+ High-level operations	Normal debugging
-vv	DEBUG	+ Detailed info, full tracebacks	Development, troubleshooting
-vvv	TRACE	+ Library internals (MCP, httpx)	Deep debugging

Examples

# Quiet mode - only errors and warnings
aws-knowledge-tool search "Lambda"

# INFO - see operations and progress
aws-knowledge-tool search "Lambda" -v
# Output:
# [INFO] Search command started
# [INFO] Searching for: Lambda
# [INFO] Found 10 results

# DEBUG - see detailed information and full tracebacks
aws-knowledge-tool search "Lambda" -vv
# Output:
# [INFO] Search command started
# [INFO] Searching for: Lambda
# [DEBUG] Parameters: limit=10, offset=0
# [DEBUG] Connecting to AWS Knowledge MCP Server...
# [DEBUG] Connected. Searching...
# [DEBUG] Search API call complete
# [INFO] Found 10 results

# TRACE - see library internals (MCP, httpx, httpcore, anyio logs)
aws-knowledge-tool search "Lambda" -vvv
# Output: Same as -vv plus httpx request/response logs

Integration with Pipelines

Logging to stderr allows clean pipeline composition:

# Data to stdout, logs to stderr - perfect for pipelines
aws-knowledge-tool search "Lambda" --json -v | jq -r '.[0].url' | aws-knowledge-tool read --stdin -v

Shell Completion

The tool provides native shell completion for bash, zsh, and fish shells, following the same pattern as popular tools like kubectl and docker.

Supported Shells

Shell	Version Requirement	Status
Bash	≥ 4.4	✅ Supported
Zsh	Any recent version	✅ Supported
Fish	≥ 3.0	✅ Supported
PowerShell	Any version	❌ Not Supported

Installation

Quick Setup (Temporary)

# Bash - active for current session only
eval "$(aws-knowledge-tool completion bash)"

# Zsh - active for current session only
eval "$(aws-knowledge-tool completion zsh)"

# Fish - active for current session only
aws-knowledge-tool completion fish | source

Permanent Setup (Recommended)

# Bash - add to ~/.bashrc
echo 'eval "$(aws-knowledge-tool completion bash)"' >> ~/.bashrc
source ~/.bashrc

# Zsh - add to ~/.zshrc
echo 'eval "$(aws-knowledge-tool completion zsh)"' >> ~/.zshrc
source ~/.zshrc

# Fish - save to completions directory
mkdir -p ~/.config/fish/completions
aws-knowledge-tool completion fish > ~/.config/fish/completions/aws-knowledge-tool.fish

File-based Installation (Better Performance)

For better shell startup performance, generate completion scripts to files:

# Bash
aws-knowledge-tool completion bash > ~/.aws-knowledge-tool-complete.bash
echo 'source ~/.aws-knowledge-tool-complete.bash' >> ~/.bashrc

# Zsh
aws-knowledge-tool completion zsh > ~/.aws-knowledge-tool-complete.zsh
echo 'source ~/.aws-knowledge-tool-complete.zsh' >> ~/.zshrc

# Fish (automatic loading from completions directory)
mkdir -p ~/.config/fish/completions
aws-knowledge-tool completion fish > ~/.config/fish/completions/aws-knowledge-tool.fish

Usage

Once installed, completion works automatically:

# Tab completion for commands
aws-knowledge-tool <TAB>
# Shows: search read recommend completion

# Tab completion for options
aws-knowledge-tool search --<TAB>
# Shows: --limit --offset --json --stdin --verbose --quiet --help

# Tab completion for shell types
aws-knowledge-tool completion <TAB>
# Shows: bash zsh fish

Getting Help

# View completion installation instructions
aws-knowledge-tool completion --help

Library Usage

Use aws-knowledge-tool as a Python library:

import asyncio
from aws_knowledge_tool import MCPClient

async def search_docs():
    async with MCPClient() as client:
        results = await client.search_documentation("Lambda functions", limit=5)
        for result in results:
            print(f"{result['title']}: {result['url']}")

asyncio.run(search_docs())

Development

# Clone repository
git clone https://github.com/dnvriend/aws-knowledge-tool.git
cd aws-knowledge-tool

# Install dependencies
make install

# Run quality checks
make format      # Format with ruff
make lint        # Lint with ruff
make typecheck   # Type check with mypy
make test        # Run pytest
make check       # Run all checks (lint + typecheck + test + security)

# Security scanning
make security-bandit      # Python security linter
make security-pip-audit   # Dependency vulnerability scanner
make security-gitleaks    # Secret and API key detection
make security             # Run all security checks

# Build and install
make pipeline    # format + lint + typecheck + test + security + build + install-global

Security Scanning

The project includes three lightweight security tools that provide 80%+ coverage:

Tool	Purpose	Speed	Coverage
bandit	Python code security linting	⚡⚡ Fast	SQL injection, hardcoded secrets, unsafe functions
pip-audit	Dependency vulnerability scanning	⚡⚡ Fast	Known CVEs in dependencies
gitleaks	Secret and API key detection	⚡⚡⚡ Very Fast	Secrets in code and git history

Prerequisites for gitleaks:

# macOS
brew install gitleaks

# Linux
# See: https://github.com/gitleaks/gitleaks#installation

All security checks run automatically in make check and make pipeline.

Known Issues

MCP HTTP Transport

Issue: The current MCP Python SDK implementation uses stdio-based transport (read_stream/write_stream) rather than HTTP transport. The tool currently uses a stub implementation with memory streams for type checking.

Impact: The tool will not connect to the AWS Knowledge MCP Server until proper HTTP transport is implemented.

Workaround: Implementation needs the MCP SDK's HTTP client wrapper or custom HTTP-to-stream adapter.

TODO:

• Investigate MCP Python SDK HTTP transport documentation
• Implement proper HTTP client using anyio streams or httpx adapter
• See: https://github.com/modelcontextprotocol/python-sdk

Code Location: aws_knowledge_tool/core/mcp_client.py:39-63

Resources

• AWS Knowledge MCP Server: https://knowledge-mcp.global.api.aws
• MCP Specification: https://modelcontextprotocol.io
• MCP Python SDK: https://github.com/modelcontextprotocol/python-sdk
• AWS Documentation: https://docs.aws.amazon.com

License

MIT License - see LICENSE file

Author

Dennis Vriend
GitHub: @dnvriend

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🤖 Built with Claude Code

This project was developed using Claude Code, an AI-powered development tool by Anthropic.