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# MCP Usage Guide
Efficient patterns for AWS MCP tools to minimize tokens and maximize accuracy.
## aws\_\_\_search_documentation
### Query Optimization
**Good queries** (specific, 2-5 words):
```
"Lambda cold start optimization"
"S3 bucket policy examples"
"DynamoDB single table design"
"ECS Fargate networking"
```
**Bad queries** (too vague or verbose):
```
"How do I make my Lambda faster" → Too conversational
"AWS" → Too broad
"I need to understand how to configure..." → Too verbose
```
### Topic Selection Matrix
| User Says | Topic | Query Example |
| --------------------- | ------------------------- | ------------------------------ |
| "How do I use SDK..." | `reference_documentation` | "S3 PutObject SDK v3" |
| "What's new in..." | `current_awareness` | "Lambda 2024 features" |
| "Getting error..." | `troubleshooting` | "AccessDenied S3 GetObject" |
| "CDK how to..." | `cdk_docs` | "CDK Lambda Python" |
| "CDK example..." | `cdk_constructs` | "API Gateway Lambda CDK" |
| "CloudFormation..." | `cloudformation` | "DynamoDB table template" |
| "Best practice..." | `general` | "serverless security patterns" |
### Multi-Topic Searches
Use multiple topics (max 3) when query spans areas:
```python
# User: "How do I fix this Lambda timeout and what's the best practice?"
topics=["troubleshooting", "general"]
query="Lambda timeout"
# User: "Show me CDK examples and explain the concepts"
topics=["cdk_constructs", "cdk_docs"]
query="Lambda function CDK"
```
## aws\_\_\_read_documentation
### When to Use
- Search returned snippet isn't enough
- Need complete code examples
- Need to understand full context
### Pagination for Long Docs
```python
# First call
aws___read_documentation(url="...", max_length=5000)
# If truncated, continue
aws___read_documentation(url="...", start_index=5000, max_length=5000)
```
**Stop early** if you found the answer - don't read entire doc.
## aws\_\_\_get_regional_availability
### Query Patterns
**Check service availability**:
```python
resource_type="product"
filters=["Amazon Aurora Serverless v2", "AWS AppSync"]
region="sa-east-1"
```
**Check API availability**:
```python
resource_type="api"
filters=["Lambda+CreateFunction", "S3+PutObject"]
region="eu-west-1"
```
**Check CloudFormation support**:
```python
resource_type="cfn"
filters=["AWS::Lambda::Function", "AWS::DynamoDB::GlobalTable"]
region="ap-southeast-1"
```
### Common Use Cases
1. **Before recommending a service**: Verify it's available in user's region
2. **Multi-region architectures**: Check consistency across regions
3. **New features**: Verify regional rollout status
## aws\_\_\_recommend
### When to Use
- After reading a doc, find related content
- Discover "New" features for a service
- Find commonly-viewed-next pages
```python
# Find new Lambda features
aws___recommend(url="https://docs.aws.amazon.com/lambda/latest/dg/welcome.html")
# Check "New" category in results
```
## AWS Marketplace MCPs
### ask_aws_marketplace
Use for third-party solution discovery:
```python
# Good queries
"monitoring tools for Kubernetes"
"log management SOC2 compliant"
"compare Datadog vs New Relic"
# Bad queries
"AWS Lambda" → Use Knowledge MCP instead
"How do I..." → Not for how-to questions
```
### Polling Pattern
```python
# Initial call
response = ask_aws_marketplace(query="...")
# Poll until complete
while response.next_cursor:
response = ask_aws_marketplace(
last_request_id=response.request_id,
cursor=response.next_cursor
)
# Display each message immediately to user!
# Then get structured report
report = get_aws_marketplace_recommendations_report(last_request_id=response.request_id)
```
## Token Optimization Tips
1. **Search before read**: Search results often have enough context
2. **Specific queries**: Fewer results = less to process
3. **Right topic**: Avoid general when specific topic exists
4. **Stop early**: Don't paginate if answer found
5. **Cache mentally**: Don't re-search same thing in conversation