The Lambda Performance MCP Server is a Node.js-based tool that provides comprehensive analysis, monitoring, and optimization of AWS Lambda functions. It enables you to:
Analyze performance metrics including cold starts, duration, memory usage, error rates, and invocation counts for individual functions
Track cold start patterns and frequency to identify optimization opportunities
Get targeted optimization recommendations for cold start performance, memory allocation, execution duration, and cost reduction
Compare performance metrics (duration, cold starts, errors, invocations, cost) across multiple Lambda functions over specified time ranges
List all Lambda functions in your AWS account with optional basic performance metrics
Analyze memory utilization and provide right-sizing recommendations to optimize resource allocation
Perform cost analysis for individual functions or across your entire account to identify cost optimization opportunities
Monitor real-time performance metrics and receive alerts for ongoing Lambda function monitoring
Provides comprehensive performance analysis, cold start tracking, memory utilization monitoring, cost analysis, and optimization recommendations for AWS Lambda functions through CloudWatch metrics and logs
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Lambda Performance MCP Server analyze cold starts for my-api-function over the last week"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A comprehensive Model Context Protocol (MCP) server for analyzing AWS Lambda performance, tracking cold starts, and providing optimization recommendations. Built with Node.js and the AWS SDK v3.
Comprehensive Metrics: Duration, memory usage, error rates, invocation counts
Cold Start Tracking: Detailed analysis of cold start patterns and frequency
Real-time Monitoring: Live performance metrics and alerts
Cost Analysis: Detailed cost breakdown and optimization opportunities
Percentile Analysis: P50, P90, P95, P99 duration metrics
Memory Utilization: Right-sizing recommendations based on actual usage
Error Pattern Analysis: Identify and categorize error types
Trend Analysis: Performance trends over time
Cold Start Optimization: Provisioned concurrency, package size, initialization
Memory Right-sizing: Optimal memory allocation based on usage patterns
Cost Optimization: ARM architecture, duration optimization, resource efficiency
Performance Tuning: Code optimization, connection pooling, caching strategies
Multi-function Comparison: Compare performance across multiple Lambda functions
Benchmarking: Identify best and worst performers
Resource Utilization: Compare memory, duration, and cost metrics
Related MCP server: AWS FinOps MCP Server
Clone the repository:
git clone <repository-url>
cd lambda-performance-mcp-nodejsInstall dependencies:
npm installConfigure environment:
cp .env.example .env
# Edit .env with your AWS credentials and configurationSet up AWS credentials:
# Option 1: Environment variables
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_REGION=us-east-1
# Option 2: AWS CLI profile
aws configure --profile lambda-analyzer
export AWS_PROFILE=lambda-analyzer
# Option 3: IAM roles (for EC2/Lambda execution)The MCP server requires the following AWS permissions:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"lambda:ListFunctions",
"lambda:GetFunction",
"lambda:GetFunctionConfiguration",
"cloudwatch:GetMetricStatistics",
"cloudwatch:GetMetricData",
"logs:FilterLogEvents",
"logs:DescribeLogGroups",
"logs:DescribeLogStreams"
],
"Resource": "*"
}
]
}# Start the server
npm start
# Development mode with auto-reload
npm run dev{
"name": "analyze_lambda_performance",
"arguments": {
"functionName": "my-lambda-function",
"timeRange": "24h",
"includeDetails": true
}
}{
"name": "track_cold_starts",
"arguments": {
"functionName": "my-lambda-function",
"timeRange": "24h"
}
}{
"name": "get_optimization_recommendations",
"arguments": {
"functionName": "my-lambda-function",
"analysisType": "all"
}
}{
"name": "compare_lambda_performance",
"arguments": {
"functionNames": ["function-1", "function-2", "function-3"],
"timeRange": "24h",
"metrics": ["duration", "cold-starts", "errors", "cost"]
}
}{
"name": "list_lambda_functions",
"arguments": {
"runtime": "nodejs18.x",
"includeMetrics": true
}
}{
"name": "analyze_memory_utilization",
"arguments": {
"functionName": "my-lambda-function",
"timeRange": "24h"
}
}{
"name": "get_cost_analysis",
"arguments": {
"functionName": "my-lambda-function",
"timeRange": "30d"
}
}{
"name": "monitor_real_time_performance",
"arguments": {
"functionName": "my-lambda-function",
"duration": 5
}
}To use this MCP server with MCP clients, add it to your MCP configuration:
.mcp/settings/mcp.json){
"mcpServers": {
"lambda-performance": {
"command": "node",
"args": ["path/to/lambda-performance-mcp-nodejs/index.js"],
"env": {
"AWS_REGION": "us-east-1",
"AWS_ACCESS_KEY_ID": "your_access_key",
"AWS_SECRET_ACCESS_KEY": "your_secret_key"
},
"disabled": false,
"autoApprove": [
"list_lambda_functions",
"analyze_lambda_performance",
"track_cold_starts"
]
}
}
}~/.mcp/settings/mcp.json){
"mcpServers": {
"lambda-performance": {
"command": "node",
"args": ["path/to/lambda-performance-mcp-nodejs/index.js"],
"env": {
"AWS_PROFILE": "default"
},
"disabled": false
}
}
}Pattern Detection: Identifies when and why cold starts occur
Duration Analysis: Tracks initialization times and optimization opportunities
Trigger Identification: Determines what causes cold starts (idle time, scaling, deployments)
Timeline Visualization: Shows cold start frequency over time
Memory Right-sizing: Analyzes actual memory usage vs. allocated memory
Duration Optimization: Identifies performance bottlenecks and optimization opportunities
Cost Optimization: Provides recommendations to reduce Lambda costs
Architecture Recommendations: Suggests ARM vs x86 based on workload compatibility
Live Metrics: Current invocation rates, duration, and error rates
Performance Alerts: Automatic detection of performance issues
Activity Tracking: Recent invocation history and patterns
# Lambda Performance Analysis: my-function
## Summary
- **Total Invocations**: 15,432
- **Average Duration**: 245ms
- **Cold Start Rate**: 12.3%
- **Error Rate**: 0.8%
- **Memory Utilization**: 67%
## Performance Metrics
- **P50 Duration**: 180ms
- **P95 Duration**: 450ms
- **P99 Duration**: 890ms
- **Max Duration**: 1,200ms
## Cold Start Analysis
- **Total Cold Starts**: 1,898
- **Average Cold Start Duration**: 1,200ms
- **Cold Start Pattern**: Moderate frequency during low traffic# Optimization Recommendations: my-function
## Priority Recommendations
1. **Right-size Memory Allocation** (Impact: High)
- Current memory is over-provisioned
- Implementation: Reduce memory from 512MB to 256MB
- Expected Improvement: Reduce costs by 25%
2. **Optimize Cold Start Performance** (Impact: High)
- High cold start rate detected
- Implementation: Implement provisioned concurrency for 2-3 instances
- Expected Improvement: Reduce cold starts by 85%Permission Errors
Ensure AWS credentials have required permissions
Check CloudWatch Logs access for cold start analysis
No Data Available
Verify function name is correct
Check if function has been invoked in the specified time range
Ensure CloudWatch logging is enabled
Connection Timeouts
Check AWS region configuration
Verify network connectivity to AWS services
# Enable debug logging
export LOG_LEVEL=debug
npm startFork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request
For issues and questions:
Check the troubleshooting section
Review AWS permissions
Verify environment configuration
Check CloudWatch Logs for detailed error messages
Analyze Lambda function performance metrics including cold starts, duration, and errors
| Name | Required | Description | Default |
|---|---|---|---|
| functionName | Yes | Name of the Lambda function to analyze | |
| timeRange | No | Time range for analysis (default: 24h) | |
| includeDetails | No | Include detailed metrics and logs (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions what metrics are analyzed but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires specific permissions, rate limits, or what the output format looks like. For a performance analysis tool with no annotations, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place by specifying the resource and key metrics without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a performance analysis tool. It doesn't explain what the analysis returns (e.g., aggregated metrics, time-series data, recommendations) or behavioral aspects like permissions or rate limits. With 3 parameters and multiple similar siblings, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain the significance of the timeRange options or what 'includeDetails' entails). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Analyze') and resource ('Lambda function performance metrics') with specific metrics listed (cold starts, duration, errors). It distinguishes from some siblings like 'list_lambda_functions' or 'get_cost_analysis', but doesn't explicitly differentiate from similar tools like 'compare_lambda_performance' or 'monitor_real_time_performance'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With siblings like 'compare_lambda_performance', 'monitor_real_time_performance', and 'track_cold_starts', the description doesn't indicate whether this is for historical analysis, real-time monitoring, comparative analysis, or specialized cold start tracking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze memory utilization and provide right-sizing recommendations
| Name | Required | Description | Default |
|---|---|---|---|
| functionName | Yes | Name of the Lambda function | |
| timeRange | No | Time range for memory analysis (default: 24h) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes and provides recommendations, implying a read-only, non-destructive operation, but doesn't detail aspects like rate limits, authentication needs, error handling, or what the recommendations entail. For a tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analyzing memory utilization with recommendations), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the format or nature of the recommendations, potential side effects, or how it integrates with sibling tools. This leaves significant gaps for an agent to understand the tool's full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting both parameters. The description adds no additional parameter semantics beyond what the schema provides, such as explaining how 'functionName' relates to memory analysis or the implications of 'timeRange' choices. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze memory utilization and provide right-sizing recommendations.' It specifies the action ('analyze') and resource ('memory utilization') with an additional outcome ('right-sizing recommendations'). However, it doesn't explicitly differentiate from siblings like 'analyze_lambda_performance' or 'get_optimization_recommendations,' which might overlap in scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over siblings such as 'analyze_lambda_performance' or 'get_optimization_recommendations,' nor does it specify prerequisites or exclusions. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare performance metrics between multiple Lambda functions
| Name | Required | Description | Default |
|---|---|---|---|
| functionNames | Yes | List of Lambda function names to compare | |
| timeRange | No | Time range for comparison (default: 24h) | |
| metrics | No | Metrics to compare (default: all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'compare performance metrics' implies a read-only operation, it doesn't specify whether this requires specific permissions, what format the comparison results take, whether data is aggregated or real-time, or any rate limits. For a tool with 3 parameters and no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters with full schema coverage but no annotations and no output schema, the description is minimally adequate. However, for a comparison tool that likely returns complex performance data, the lack of output information and behavioral context leaves significant gaps in understanding how to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'compare' and the resource 'performance metrics between multiple Lambda functions', which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_lambda_performance' or 'monitor_real_time_performance', which likely have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools related to Lambda performance analysis (analyze_lambda_performance, monitor_real_time_performance, track_cold_starts), there's no indication of what makes this tool distinct or when it's the appropriate choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze Lambda function costs and identify optimization opportunities
| Name | Required | Description | Default |
|---|---|---|---|
| functionName | No | Name of the Lambda function (optional for account-wide analysis) | |
| timeRange | No | Time range for cost analysis (default: 30d) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions analyzing costs and identifying optimization opportunities, but doesn't describe what the analysis returns (e.g., cost breakdowns, savings estimates), whether it requires specific permissions, or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of cost analysis and optimization identification, the description is insufficient. With no annotations and no output schema, it doesn't explain what the analysis returns or any behavioral constraints. The description should provide more context about the tool's output and usage patterns to be complete for this type of analytical tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, so parameters are well-documented in the structured fields. The description adds no additional parameter semantics beyond what's in the schema, but since schema coverage is high, the baseline score of 3 is appropriate as the description doesn't need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as analyzing Lambda function costs and identifying optimization opportunities, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_optimization_recommendations' or 'analyze_lambda_performance' that might overlap in optimization analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_optimization_recommendations' and 'analyze_lambda_performance' that might handle similar optimization tasks, there's no indication of when this cost-focused tool is preferred or what distinguishes it from other analysis tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get performance optimization recommendations for Lambda functions
| Name | Required | Description | Default |
|---|---|---|---|
| functionName | Yes | Name of the Lambda function | |
| analysisType | No | Type of optimization analysis (default: all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details: it doesn't specify if this is a read-only operation, what permissions are required, whether it triggers any side effects, or how results are returned (e.g., format, pagination). For a tool with no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like safety, permissions, or return format, which are crucial for a tool that likely interacts with cloud resources. For a tool with 2 parameters and no structured safety hints, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly (functionName and analysisType with enum values). The description doesn't add any meaningful parameter semantics beyond what's in the schema, such as explaining the implications of different analysisType values. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'performance optimization recommendations for Lambda functions', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_lambda_performance' or 'get_cost_analysis', which appear to have overlapping domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_lambda_performance' or 'get_cost_analysis'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all Lambda functions in the account with basic performance info
| Name | Required | Description | Default |
|---|---|---|---|
| runtime | No | Filter by runtime (e.g., nodejs18.x, python3.9) | |
| includeMetrics | No | Include basic performance metrics (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'basic performance info' but doesn't disclose behavioral traits such as pagination, rate limits, permissions required, or what 'basic' entails. This is inadequate for a tool with potential complexity in cloud environments.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids waste but could be slightly more structured for clarity, such as separating scope from output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and moderate complexity (listing cloud resources), the description is incomplete. It lacks details on return format, error handling, or behavioral constraints, leaving significant gaps for an AI agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters fully. The description adds no additional meaning beyond implying filtering and metrics inclusion, which the schema covers. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 ('Lambda functions in the account'), specifying scope ('all') and additional data ('basic performance info'). It distinguishes from siblings by focusing on listing rather than analysis, monitoring, or comparison, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus siblings like 'analyze_lambda_performance' or 'monitor_real_time_performance'. The description implies it's for listing with basic info, but lacks explicit context, prerequisites, or exclusions for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get real-time performance metrics and alerts for Lambda functions
| Name | Required | Description | Default |
|---|---|---|---|
| functionName | Yes | Name of the Lambda function | |
| duration | No | Monitoring duration in minutes (default: 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'real-time performance metrics and alerts' but doesn't specify what metrics are included, how alerts are triggered, whether this is a read-only operation, or any rate limits. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of performance monitoring and the lack of annotations and output schema, the description is insufficient. It doesn't explain what metrics or alerts are returned, how the data is formatted, or any prerequisites like permissions. For a tool with no structured behavioral data, this leaves too much ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('functionName' and 'duration') with clear descriptions. The description doesn't add any additional meaning or context beyond what the schema provides, such as explaining the scope of 'real-time' or how 'duration' affects the output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and target ('real-time performance metrics and alerts for Lambda functions'), making the purpose understandable. However, it doesn't differentiate from siblings like 'analyze_lambda_performance' or 'track_cold_starts', which might also involve performance monitoring aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'analyze_lambda_performance', 'track_cold_starts', and 'compare_lambda_performance', there's no indication of what makes this tool distinct or when it's preferred over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Track and analyze cold start patterns for Lambda functions
| Name | Required | Description | Default |
|---|---|---|---|
| functionName | Yes | Name of the Lambda function | |
| timeRange | No | Time range for cold start analysis (default: 24h) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('track and analyze') but lacks details on what the analysis entails, output format, whether it's read-only or mutative, rate limits, or authentication needs. This is a significant gap for a tool with potential complexity in analyzing patterns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It uses clear terminology and avoids redundancy, making it appropriately sized for the tool's scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a tool that involves analysis (implying potential complexity), the description is incomplete. It doesn't explain what 'track and analyze' yields, such as metrics, trends, or actionable insights, leaving gaps in understanding the tool's full behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (functionName and timeRange with enum values). The description adds no additional parameter semantics beyond implying cold start analysis, which is already covered by the tool's purpose. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('track and analyze') and resource ('cold start patterns for Lambda functions'), making the purpose evident. It distinguishes from siblings by focusing specifically on cold starts rather than general performance, memory, cost, or listing functions. However, it doesn't explicitly differentiate from 'analyze_lambda_performance' which might overlap, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, and fails to reference sibling tools like 'analyze_lambda_performance' or 'monitor_real_time_performance' that might be relevant for performance analysis. Usage is implied but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes, such as analyze_lambda_performance for general metrics, analyze_memory_utilization for memory-specific analysis, and compare_lambda_performance for comparisons. However, analyze_lambda_performance and monitor_real_time_performance could be confused, as both involve performance metrics, though one is general analysis and the other is real-time monitoring.
All tool names follow a consistent verb_noun pattern using snake_case, such as analyze_lambda_performance, list_lambda_functions, and track_cold_starts. This uniformity makes the tool set predictable and easy to understand.
With 8 tools, the server is well-scoped for Lambda performance analysis, covering key areas like monitoring, cost, optimization, and specific metrics. Each tool serves a clear purpose without being overwhelming or insufficient.
The tool set covers core aspects of Lambda performance analysis, including listing functions, analyzing metrics, cost, optimization, and cold starts. A minor gap is the lack of tools for configuration updates or direct action implementation, but agents can work around this by using recommendations from existing tools.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Cloud cost visibility and savings recommendations grounded in your actual AWS, GCP and Azure bill.
FinOps MCP: query allocated, correlated cloud and AI cost across AWS, GCP, Azure and Snowflake.
Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Track errors, manage performance alerts, and configure dashboards and monitors
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