{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-awslabs--mcp","slug":"awslabs--mcp","name":"mcp","type":"mcp","url":"https://awslabs.github.io/mcp/","page_url":"https://unfragile.ai/awslabs--mcp","categories":["mcp-servers"],"tags":["aws","mcp","mcp-client","mcp-clients","mcp-host","mcp-server","mcp-servers","mcp-tools","modelcontextprotocol"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-awslabs--mcp__cap_0","uri":"capability://tool.use.integration.aws.service.tool.exposure.via.standardized.mcp.protocol","name":"aws service tool exposure via standardized mcp protocol","description":"Exposes 50+ AWS services (Lambda, DynamoDB, S3, ECS, SageMaker, Bedrock, etc.) as callable tools through the Model Context Protocol, using a unified schema-based function registry that translates AWS SDK operations into LLM-compatible tool definitions. Each MCP server wraps AWS service clients and translates their responses into structured JSON that LLMs can reason about and chain together, enabling AI assistants to orchestrate multi-service AWS workflows without custom integration code.","intents":["I want my AI assistant to invoke AWS Lambda functions, query DynamoDB tables, and manage S3 buckets directly","I need to give Claude or another LLM the ability to provision infrastructure via Terraform or CloudFormation","I want to enable AI-driven cost analysis and CloudWatch monitoring without building custom APIs"],"best_for":["AWS-native development teams building AI-augmented workflows","DevOps engineers integrating LLMs into infrastructure automation","Enterprise teams deploying AI assistants that need AWS service access"],"limitations":["Requires AWS credentials and IAM permissions for each service — no built-in credential abstraction across services","Each MCP server is a separate process — multi-service workflows require coordinating multiple server instances","Response latency depends on AWS API call times; no built-in caching of frequently accessed resources like S3 object listings","Tool schemas are static at server startup — dynamic AWS resource discovery requires custom server implementation"],"requires":["Python 3.9+","AWS credentials (IAM user or role with service-specific permissions)","MCP client implementation (Claude Desktop, custom LLM application, or MCP host)","Individual MCP server packages installed (e.g., aws-lambda-mcp-server, dynamodb-mcp-server)"],"input_types":["tool invocation requests (JSON-RPC 2.0 format with method name and parameters)","AWS resource identifiers (ARNs, function names, table names, bucket names)","structured parameters matching AWS SDK operation signatures"],"output_types":["JSON-serialized AWS API responses","structured tool results with operation status and data payloads","error messages with AWS error codes and diagnostic information"],"categories":["tool-use-integration","aws-services"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_1","uri":"capability://code.generation.editing.infrastructure.as.code.tool.generation.for.terraform.cloudformation.and.cdk","name":"infrastructure-as-code tool generation for terraform, cloudformation, and cdk","description":"Generates specialized MCP servers for Terraform, CloudFormation, and AWS CDK that expose infrastructure-as-code operations as LLM-callable tools. These servers parse IaC configuration files, generate tool schemas for resource creation/modification, and translate LLM tool invocations back into IaC syntax or API calls, enabling AI assistants to author and modify infrastructure definitions without direct file editing.","intents":["I want Claude to generate Terraform modules and apply infrastructure changes based on natural language descriptions","I need an AI assistant to help debug CloudFormation templates and suggest resource configuration improvements","I want to enable AI-driven CDK code generation for AWS infrastructure provisioning"],"best_for":["Infrastructure teams using Terraform or CloudFormation who want AI-assisted IaC authoring","CDK-based development teams integrating LLM-driven infrastructure generation","DevOps engineers building AI-augmented deployment pipelines"],"limitations":["Terraform server requires local Terraform binary and state file access — no remote state abstraction","CloudFormation server is read-only for template inspection; tool invocations require separate AWS API calls to apply changes","CDK server generates TypeScript/Python code but doesn't execute synthesis or deployment — requires separate CDK CLI invocation","No built-in validation of IaC syntax before LLM generation — malformed configurations require manual correction"],"requires":["Python 3.9+","Terraform binary (for Terraform MCP server) or AWS CLI (for CloudFormation server)","AWS credentials with CloudFormation/IaC permissions","Node.js 18+ (for CDK server)","MCP client with tool-calling support"],"input_types":["natural language descriptions of infrastructure changes","existing IaC file paths (Terraform .tf files, CloudFormation JSON/YAML, CDK TypeScript)","tool invocation requests with resource configuration parameters"],"output_types":["generated Terraform HCL code","CloudFormation template JSON/YAML","CDK TypeScript/Python code","deployment plan summaries with resource changes"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_10","uri":"capability://tool.use.integration.multi.server.orchestration.and.client.side.tool.aggregation","name":"multi-server orchestration and client-side tool aggregation","description":"Enables MCP clients (Claude Desktop, custom LLM applications) to connect to multiple MCP servers simultaneously and aggregate their tool definitions into a unified tool registry. The client-side orchestration layer handles server lifecycle management, tool schema merging, request routing to appropriate servers, and error handling across heterogeneous servers, enabling LLMs to seamlessly invoke tools across AWS services without awareness of server boundaries.","intents":["I want my AI assistant to orchestrate multi-service AWS workflows (e.g., create Lambda function, deploy to ECS, monitor with CloudWatch)","I need to aggregate tools from 10+ MCP servers into a single tool registry for my LLM","I want to manage server lifecycle (startup, shutdown, health checks) automatically"],"best_for":["LLM application developers building multi-service AWS orchestration","Enterprise teams deploying AI assistants with access to 50+ AWS services","DevOps teams building AI-augmented infrastructure management platforms"],"limitations":["Tool name collisions across servers require manual disambiguation — no automatic namespace isolation","Server health monitoring is basic — no automatic failover or retry logic","Tool invocation ordering is not enforced — LLM must manage dependencies between tools across servers","No built-in transaction support — multi-server workflows cannot be rolled back atomically","Latency scales with number of servers — each tool invocation requires server lookup and routing"],"requires":["MCP client implementation (Claude Desktop, custom Python/Node.js application)","Multiple MCP server instances running (or configured for auto-startup)","Network connectivity between client and all servers","Configuration file specifying server endpoints and tool schema mappings"],"input_types":["MCP server configuration (endpoints, credentials, tool schema mappings)","tool invocation requests from LLM","server health check parameters"],"output_types":["aggregated tool registry with all server tools","tool invocation results from appropriate server","server health status and diagnostics","error messages with server-specific context"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_11","uri":"capability://memory.knowledge.aws.documentation.and.api.reference.integration.for.context.aware.llm.assistance","name":"aws documentation and api reference integration for context-aware llm assistance","description":"Provides an MCP server that exposes AWS documentation and API reference materials as searchable context, enabling LLMs to retrieve relevant documentation snippets during tool invocation. The server indexes AWS documentation, performs semantic search over documentation content, and returns relevant sections that provide context for tool usage, error messages, and best practices.","intents":["I want Claude to reference AWS documentation when explaining tool usage or error messages","I need an AI assistant to provide best practice recommendations with links to relevant AWS documentation","I want to enable context-aware help within my LLM-powered AWS management tool"],"best_for":["LLM application developers building AWS-native tools with integrated documentation","DevOps teams providing AI-augmented AWS guidance to developers","Enterprise teams building internal AWS knowledge bases for LLM consumption"],"limitations":["Documentation index is static — updates require manual re-indexing","Search is semantic only — keyword-based search requires custom implementation","Documentation snippets may be outdated — no automatic validation against current AWS APIs","No built-in versioning — documentation for multiple AWS API versions requires separate indexes","Search latency depends on index size — large documentation sets may have high query latency"],"requires":["Python 3.9+","AWS documentation corpus (pre-indexed or requires indexing)","Vector database or semantic search engine (e.g., Chroma, Pinecone)","MCP client with tool-calling support"],"input_types":["natural language documentation queries","AWS service names or API operation names","error messages for documentation lookup"],"output_types":["relevant documentation snippets with source URLs","API reference information with parameter descriptions","best practice recommendations with documentation links","troubleshooting guides for common errors"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_2","uri":"capability://data.processing.analysis.database.query.and.schema.introspection.with.multi.database.support","name":"database query and schema introspection with multi-database support","description":"Provides MCP servers for PostgreSQL, DynamoDB, Neptune, and other databases that expose query execution, schema introspection, and data manipulation as LLM-callable tools. Servers parse database schemas, generate tool definitions for common queries and mutations, and translate LLM tool invocations into SQL/query language commands, enabling AI assistants to explore database structure and execute queries without direct database client access.","intents":["I want Claude to query my PostgreSQL database and summarize results in natural language","I need an AI assistant to help design DynamoDB schemas and generate query patterns","I want to enable AI-driven data exploration and anomaly detection in my data warehouse"],"best_for":["Data teams using PostgreSQL or DynamoDB who want AI-assisted data exploration","Analytics engineers integrating LLMs into data pipelines","Database administrators building AI-augmented query optimization tools"],"limitations":["PostgreSQL server requires direct database connection — no connection pooling or multi-tenant isolation","DynamoDB server has limited query expressiveness compared to SQL — complex joins and aggregations require custom tool implementation","No built-in query result caching — repeated queries hit the database each time","Schema introspection is point-in-time — schema changes require server restart to reflect in tool definitions","No transaction support across multiple tool invocations — each query is isolated"],"requires":["Python 3.9+","Database credentials (PostgreSQL connection string, DynamoDB AWS credentials)","Network access to database (PostgreSQL) or AWS credentials (DynamoDB)","MCP client with tool-calling support","Database schema already exists (servers don't create schemas)"],"input_types":["natural language query descriptions","SQL queries (PostgreSQL server)","DynamoDB query parameters (table name, key conditions, filter expressions)","schema exploration requests"],"output_types":["query results as JSON arrays","schema metadata (tables, columns, indexes, constraints)","execution statistics (rows affected, query time)","error messages with database-specific diagnostics"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_3","uri":"capability://automation.workflow.container.and.kubernetes.orchestration.tool.exposure","name":"container and kubernetes orchestration tool exposure","description":"Exposes ECS, EKS, and Kubernetes operations as MCP tools, enabling LLMs to inspect cluster state, deploy containers, manage services, and troubleshoot deployments. Servers integrate with Kubernetes APIs and ECS APIs to translate LLM tool invocations into cluster operations, providing real-time visibility into container workloads and enabling AI-driven deployment automation.","intents":["I want Claude to check the status of my ECS services and restart failing tasks","I need an AI assistant to help debug Kubernetes pod failures and suggest remediation","I want to enable AI-driven container deployment and scaling based on metrics"],"best_for":["Kubernetes and ECS operators who want AI-assisted cluster management","DevOps teams building AI-augmented deployment and troubleshooting workflows","SRE teams integrating LLMs into incident response automation"],"limitations":["EKS server requires kubeconfig file and cluster network access — no built-in multi-cluster management","ECS server is limited to single AWS region — cross-region operations require multiple server instances","No built-in RBAC enforcement — server inherits all permissions from provided credentials","Pod/task logs are fetched on-demand with no caching — high latency for large log volumes","Deployment operations are not transactional — partial failures can leave cluster in inconsistent state"],"requires":["Python 3.9+","Kubernetes kubeconfig file (EKS server) or AWS credentials (ECS server)","Network access to Kubernetes API server or AWS ECS endpoint","kubectl binary installed (for EKS server)","MCP client with tool-calling support"],"input_types":["natural language cluster management requests","Kubernetes resource names (pod, service, deployment names)","ECS cluster/service/task identifiers","deployment manifests (YAML for Kubernetes)"],"output_types":["cluster state summaries (pods, services, nodes)","deployment status and event logs","container logs and metrics","error messages with remediation suggestions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_4","uri":"capability://tool.use.integration.ai.and.machine.learning.service.integration.with.bedrock.sagemaker.and.nova","name":"ai and machine learning service integration with bedrock, sagemaker, and nova","description":"Exposes AWS AI/ML services (Bedrock for foundation models, SageMaker for training/inference, Nova Canvas for image generation) as MCP tools, enabling LLMs to invoke other AI models, retrieve knowledge base documents, generate images, and manage ML workflows. Servers translate LLM tool invocations into Bedrock API calls, SageMaker operations, and image generation requests, enabling multi-model AI orchestration and knowledge retrieval augmentation.","intents":["I want to use Claude to invoke other foundation models via Bedrock and compare their outputs","I need an AI assistant to retrieve documents from my Bedrock knowledge base and synthesize answers","I want to enable AI-driven image generation and editing via Nova Canvas within my LLM workflow"],"best_for":["AI teams building multi-model orchestration systems","Enterprise teams using Bedrock knowledge bases for RAG workflows","Product teams integrating generative AI capabilities into applications"],"limitations":["Bedrock KB Retrieval server requires pre-built knowledge bases — no dynamic document ingestion","Nova Canvas server supports image generation but not fine-grained image editing — complex edits require separate image processing","SageMaker server is read-only for endpoint invocation — training job submission requires separate API calls","No built-in model selection logic — LLM must explicitly choose which Bedrock model to invoke","Knowledge base retrieval has no built-in relevance filtering — all results returned regardless of similarity score"],"requires":["Python 3.9+","AWS credentials with Bedrock, SageMaker, and Nova Canvas permissions","Bedrock knowledge bases already created (for KB Retrieval server)","SageMaker endpoints deployed (for inference server)","MCP client with tool-calling support"],"input_types":["natural language prompts for model invocation","Bedrock model IDs and inference parameters","knowledge base queries and retrieval parameters","image generation prompts and style parameters","SageMaker endpoint names and input data"],"output_types":["model-generated text responses","knowledge base retrieval results with source documents","generated images (base64-encoded or S3 URLs)","SageMaker inference results","model metadata and capability information"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_5","uri":"capability://data.processing.analysis.cost.analysis.and.billing.exploration.with.aws.cost.explorer.integration","name":"cost analysis and billing exploration with aws cost explorer integration","description":"Exposes AWS Cost Explorer and billing APIs as MCP tools, enabling LLMs to analyze cloud spending patterns, identify cost anomalies, and generate cost optimization recommendations. Servers translate natural language cost analysis requests into Cost Explorer queries, aggregate billing data by service/dimension, and present findings in structured formats that LLMs can reason about and summarize.","intents":["I want Claude to analyze my AWS spending trends and identify the top cost drivers","I need an AI assistant to help forecast next month's AWS bill based on current usage patterns","I want to enable AI-driven cost optimization recommendations without manual analysis"],"best_for":["FinOps teams integrating AI into cost analysis workflows","Cloud architects building AI-augmented cost optimization tools","Enterprise teams automating billing analysis and anomaly detection"],"limitations":["Cost Explorer data has 24-hour latency — real-time cost tracking is not possible","Granularity is limited to daily aggregation — sub-hourly cost analysis requires custom billing data export","Cost anomaly detection is rule-based — no machine learning-driven anomaly detection in the MCP server itself","Recommendations are based on historical patterns — forward-looking optimization requires external forecasting models","No built-in cost allocation across shared resources — multi-tenant cost attribution requires manual tag-based filtering"],"requires":["Python 3.9+","AWS credentials with Cost Explorer and Billing permissions","AWS Cost Explorer enabled on the account","MCP client with tool-calling support"],"input_types":["natural language cost analysis queries","time range specifications (date ranges for cost analysis)","dimension filters (service, linked account, region, tag)","granularity preferences (daily, monthly aggregation)"],"output_types":["cost summaries by service, dimension, or time period","cost trend analysis with growth rates","anomaly detection results with flagged periods","optimization recommendations with estimated savings","forecast projections for future periods"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_6","uri":"capability://data.processing.analysis.monitoring.and.observability.tool.exposure.via.cloudwatch.and.aws.x.ray","name":"monitoring and observability tool exposure via cloudwatch and aws x-ray","description":"Exposes CloudWatch metrics, logs, alarms, and AWS X-Ray tracing as MCP tools, enabling LLMs to query monitoring data, analyze application performance, troubleshoot errors, and recommend alerting strategies. Servers translate LLM requests into CloudWatch Insights queries, metric retrievals, and X-Ray trace analysis, providing real-time visibility into application health and enabling AI-driven incident investigation.","intents":["I want Claude to query CloudWatch logs for error patterns and suggest root causes","I need an AI assistant to analyze X-Ray traces and identify performance bottlenecks","I want to enable AI-driven alerting strategy recommendations based on metric patterns"],"best_for":["SRE and DevOps teams building AI-augmented monitoring and troubleshooting workflows","Application teams integrating LLMs into incident response automation","Platform teams automating observability analysis and alerting optimization"],"limitations":["CloudWatch Insights queries have 15-minute latency for log ingestion — real-time log analysis is not possible","X-Ray trace retrieval is limited to 7-day retention — historical trace analysis requires external storage","Metric query granularity is limited to 1-minute resolution — sub-minute performance analysis requires custom metrics","No built-in correlation between metrics, logs, and traces — LLM must manually correlate across data sources","Alarm creation requires separate API calls — MCP server can only query existing alarms, not create new ones"],"requires":["Python 3.9+","AWS credentials with CloudWatch and X-Ray permissions","CloudWatch log groups and metrics already configured","X-Ray service enabled on application (for trace analysis)","MCP client with tool-calling support"],"input_types":["natural language monitoring queries","CloudWatch Insights query syntax (or natural language translated to queries)","metric names and dimensions","time range specifications","X-Ray trace filter expressions"],"output_types":["CloudWatch log query results with matching entries","metric data points with timestamps and values","alarm status and history","X-Ray trace summaries with service dependencies","performance analysis with latency percentiles and error rates"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_7","uri":"capability://safety.moderation.iam.and.security.policy.analysis.with.automated.permission.recommendations","name":"iam and security policy analysis with automated permission recommendations","description":"Exposes AWS IAM APIs as MCP tools, enabling LLMs to analyze IAM policies, identify overly permissive roles, detect security misconfigurations, and recommend least-privilege policy changes. Servers parse IAM policy documents, compare actual permissions against required permissions, and generate policy recommendations in JSON format that LLMs can reason about and present to security teams.","intents":["I want Claude to audit my IAM policies and identify overly permissive roles","I need an AI assistant to help generate least-privilege policies for specific use cases","I want to enable AI-driven security compliance checking against IAM best practices"],"best_for":["Security teams automating IAM policy audits and compliance checks","Cloud architects building AI-augmented least-privilege policy generation","Enterprise teams integrating LLMs into security governance workflows"],"limitations":["Policy analysis is static — no runtime permission tracking or actual permission usage analysis","Recommendations are based on policy syntax — no understanding of application-specific permission requirements","No built-in policy versioning or change tracking — historical policy analysis requires manual comparison","Permission recommendations may be overly conservative — LLM must validate recommendations against actual use cases","No integration with AWS Access Analyzer — manual policy validation required"],"requires":["Python 3.9+","AWS credentials with IAM read permissions (and write permissions for policy recommendations)","MCP client with tool-calling support"],"input_types":["natural language security audit requests","IAM role or policy ARNs","use case descriptions for least-privilege policy generation","IAM policy documents (JSON format)"],"output_types":["IAM policy analysis results with permission inventory","security findings with risk severity levels","least-privilege policy recommendations in JSON format","compliance check results against IAM best practices","role usage analysis with unused permissions"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_8","uri":"capability://automation.workflow.messaging.and.event.driven.workflow.orchestration.with.sns.sqs.and.step.functions","name":"messaging and event-driven workflow orchestration with sns, sqs, and step functions","description":"Exposes SNS, SQS, and AWS Step Functions as MCP tools, enabling LLMs to publish messages, inspect queue contents, trigger workflows, and monitor execution status. Servers translate LLM tool invocations into messaging API calls and Step Functions state machine executions, enabling AI-driven event orchestration and asynchronous workflow management without direct service client access.","intents":["I want Claude to publish messages to SNS topics and trigger downstream workflows","I need an AI assistant to inspect SQS queue contents and help debug message processing issues","I want to enable AI-driven Step Functions workflow execution and monitoring"],"best_for":["Event-driven architecture teams integrating LLMs into workflow orchestration","DevOps teams building AI-augmented async workflow management","Integration teams automating message-based system troubleshooting"],"limitations":["SNS/SQS servers have no built-in message filtering — all messages returned regardless of content","Step Functions server can only monitor execution status — workflow logic is defined separately","No built-in dead-letter queue handling — failed messages require manual investigation","Message visibility timeout is not configurable via MCP tools — requires AWS console access","No built-in message replay or reprocessing — requires manual message republishing"],"requires":["Python 3.9+","AWS credentials with SNS, SQS, and Step Functions permissions","SNS topics, SQS queues, and Step Functions state machines already created","MCP client with tool-calling support"],"input_types":["natural language workflow orchestration requests","SNS topic ARNs and message payloads","SQS queue URLs and message filtering parameters","Step Functions state machine ARNs and execution input"],"output_types":["SNS publish confirmation with message IDs","SQS message listings with message bodies and metadata","Step Functions execution status and output","workflow execution history with state transitions","error messages with failure reasons"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-awslabs--mcp__cap_9","uri":"capability://code.generation.editing.mcp.server.framework.and.standardized.server.template.for.custom.aws.service.integration","name":"mcp server framework and standardized server template for custom aws service integration","description":"Provides a Python-based MCP server framework and standardized design guidelines that enable developers to build custom MCP servers for AWS services not covered by the official servers. The framework handles MCP protocol mechanics (JSON-RPC 2.0 transport, tool schema generation, error handling), allowing developers to focus on AWS service integration logic. Includes testing patterns, CI/CD workflows, and documentation templates to ensure consistency across custom servers.","intents":["I want to build a custom MCP server for an AWS service not covered by the official servers","I need to integrate a proprietary AWS service or custom API into an LLM workflow via MCP","I want to ensure my custom MCP server follows AWS best practices and design patterns"],"best_for":["AWS teams building custom MCP servers for internal or niche services","Developers extending the official MCP server ecosystem with new integrations","Organizations with custom AWS service implementations needing LLM integration"],"limitations":["Framework is Python-only — no support for other languages (Go, Rust, Node.js)","Tool schema generation is manual — developers must define tool schemas explicitly rather than auto-generating from AWS SDK","No built-in authentication abstraction — each server must handle AWS credential management independently","Testing framework is basic — developers must implement comprehensive test coverage manually","Documentation generation is manual — no auto-doc generation from tool schemas"],"requires":["Python 3.9+","Understanding of MCP protocol and JSON-RPC 2.0","AWS SDK knowledge for the target service","Familiarity with the MCP server design guidelines and patterns","Testing framework (pytest) and CI/CD setup (GitHub Actions)"],"input_types":["AWS service API documentation","tool schema definitions (JSON format)","test cases and integration scenarios"],"output_types":["custom MCP server implementation (Python module)","tool schema definitions","test suite and CI/CD workflows","documentation and usage examples"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","AWS credentials (IAM user or role with service-specific permissions)","MCP client implementation (Claude Desktop, custom LLM application, or MCP host)","Individual MCP server packages installed (e.g., aws-lambda-mcp-server, dynamodb-mcp-server)","Terraform binary (for Terraform MCP server) or AWS CLI (for CloudFormation server)","AWS credentials with CloudFormation/IaC permissions","Node.js 18+ (for CDK server)","MCP client with tool-calling support","MCP client implementation (Claude Desktop, custom Python/Node.js application)","Multiple MCP server instances running (or configured for auto-startup)"],"failure_modes":["Requires AWS credentials and IAM permissions for each service — no built-in credential abstraction across services","Each MCP server is a separate process — multi-service workflows require coordinating multiple server instances","Response latency depends on AWS API call times; no built-in caching of frequently accessed resources like S3 object listings","Tool schemas are static at server startup — dynamic AWS resource discovery requires custom server implementation","Terraform server requires local Terraform binary and state file access — no remote state abstraction","CloudFormation server is read-only for template inspection; tool invocations require separate AWS API calls to apply changes","CDK server generates TypeScript/Python code but doesn't execute synthesis or deployment — requires separate CDK CLI invocation","No built-in validation of IaC syntax before LLM generation — malformed configurations require manual correction","Tool name collisions across servers require manual disambiguation — no automatic namespace isolation","Server health monitoring is basic — no automatic failover or retry logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.665965058852528,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-03T01:40:08Z"},"community":{"stars":8937,"forks":1494,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=awslabs--mcp","compare_url":"https://unfragile.ai/compare?artifact=awslabs--mcp"}},"signature":"O6ctDXIHVM8pf31hOoLEPtPqBBPqiyxzIgqkRKom6WgMlaeJXhwlLezhlp173GNEFeaFb8fzouCeuCVFWoFOAg==","signedAt":"2026-06-21T06:33:34.083Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/awslabs--mcp","artifact":"https://unfragile.ai/awslabs--mcp","verify":"https://unfragile.ai/api/v1/verify?slug=awslabs--mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}