{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-aws-core","slug":"aws-core","name":"AWS Core","type":"mcp","url":"https://github.com/awslabs/mcp/tree/main/src/core-mcp-server","page_url":"https://unfragile.ai/aws-core","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-aws-core__cap_0","uri":"capability://tool.use.integration.mcp.server.lifecycle.management.and.initialization","name":"mcp server lifecycle management and initialization","description":"Manages the complete lifecycle of MCP server instances including startup, configuration loading, capability registration, and graceful shutdown. Implements standardized server initialization patterns that allow AI clients to discover and negotiate protocol versions, supported features, and resource constraints before executing operations. Uses a state machine approach to track server readiness and handle concurrent client connections.","intents":["I need to spin up an MCP server that can handle multiple concurrent AI client connections","I want to ensure my MCP server advertises its capabilities correctly to clients during handshake","I need to gracefully shutdown my server while draining in-flight requests"],"best_for":["AWS service teams building domain-specific MCP servers","DevOps engineers deploying MCP servers in Lambda or containerized environments","AI application developers integrating multiple MCP servers into their client"],"limitations":["Server initialization is synchronous — no async pre-warming of resources before client connections","Capability negotiation happens per-client, not globally — can lead to repeated discovery overhead with many clients","No built-in load balancing across multiple server instances — requires external orchestration"],"requires":["Python 3.9+","MCP protocol SDK (included in awslabs/mcp)","AWS credentials if server needs to access AWS services"],"input_types":["server configuration (JSON/YAML)","client protocol negotiation messages"],"output_types":["server capability advertisement","protocol version agreement","initialization status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_1","uri":"capability://planning.reasoning.prompt.understanding.and.semantic.routing","name":"prompt understanding and semantic routing","description":"Analyzes incoming prompts from AI clients to understand intent and route requests to appropriate MCP server handlers or tool implementations. Uses semantic analysis to map natural language requests to specific AWS service operations, handling ambiguous or multi-step prompts by decomposing them into discrete tool calls. Maintains context across multi-turn conversations to resolve references and maintain state.","intents":["I want my AI assistant to understand when a user is asking for AWS infrastructure operations vs data queries","I need to route complex multi-step requests (e.g., 'create a Lambda function and deploy it') to the right sequence of MCP servers","I want to handle follow-up questions that reference previous context without re-specifying all parameters"],"best_for":["AI application developers building AWS-aware assistants","Teams implementing agentic workflows that orchestrate multiple AWS services","Enterprise users who need intelligent request routing across heterogeneous MCP server implementations"],"limitations":["Semantic routing relies on prompt clarity — ambiguous or vague requests may route incorrectly without clarification","Context window is limited by client's conversation history — long-running sessions may lose earlier context","No explicit intent confidence scoring — routing decisions are binary rather than probabilistic"],"requires":["MCP client with prompt submission capability","Access to Core MCP Server instance","Conversation history (for multi-turn context)"],"input_types":["natural language prompts","conversation history","optional structured hints about intent"],"output_types":["routed tool calls","clarification requests","structured operation parameters"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_10","uri":"capability://automation.workflow.tool.parameter.templating.and.variable.substitution","name":"tool parameter templating and variable substitution","description":"Supports templating and variable substitution in tool parameters, enabling parameterized operations that can be reused across different contexts. Implements template syntax for referencing previous operation results, environment variables, and user inputs. Validates template syntax and resolves variables at execution time.","intents":["I want to create reusable operation templates that can be parameterized with different values","I need to reference results from previous operations in subsequent operations without manual copying","I want to use environment variables and secrets in tool parameters without hardcoding them"],"best_for":["Teams building reusable MCP operation libraries","AI agents that need to compose operations dynamically","DevOps automation that requires parameterized infrastructure operations"],"limitations":["Template resolution adds ~20-50ms latency per operation","Complex template expressions can be difficult to debug","No type checking for template variables — type mismatches only caught at execution time"],"requires":["Template syntax definition (e.g., Jinja2, handlebars)","Variable resolution context (previous results, environment)","Template validation engine"],"input_types":["parameterized tool definitions with template syntax","variable values (from previous operations, environment, user input)"],"output_types":["resolved tool parameters","template validation errors"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_11","uri":"capability://safety.moderation.audit.logging.and.compliance.tracking","name":"audit logging and compliance tracking","description":"Records all MCP operations with full audit trails including who performed the operation, what was requested, what was executed, and what the outcome was. Integrates with AWS CloudTrail for compliance tracking and supports immutable audit logs. Implements audit log filtering and querying for compliance investigations.","intents":["I need to maintain audit trails of all AWS operations performed through MCP for compliance","I want to investigate who made a specific change and when","I need to generate compliance reports showing all operations performed in a time period"],"best_for":["Regulated enterprises (finance, healthcare, government) requiring audit trails","Teams implementing least-privilege access with accountability requirements","Organizations with compliance frameworks (SOC 2, HIPAA, PCI-DSS)"],"limitations":["Audit logging adds ~20-100ms latency per operation","Audit logs can grow very large — requires retention policies and archival","CloudTrail integration has eventual consistency — audit logs may not be immediately available"],"requires":["CloudTrail enabled for AWS API logging","CloudWatch Logs for MCP-specific audit events","Optional: S3 for long-term audit log storage"],"input_types":["MCP operations with user context","audit log queries and filters"],"output_types":["audit log entries with full operation context","compliance reports","audit log query results"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_2","uri":"capability://automation.workflow.multi.server.orchestration.and.tool.composition","name":"multi-server orchestration and tool composition","description":"Coordinates execution across multiple specialized MCP servers (e.g., Lambda, DynamoDB, S3) to fulfill complex requests that span multiple AWS services. Implements tool composition patterns that chain outputs from one server as inputs to another, managing data transformation and error handling across service boundaries. Handles dependency resolution when operations must execute in a specific sequence.","intents":["I need to execute a workflow that reads from DynamoDB, processes data with Lambda, and stores results in S3","I want to compose tools from different MCP servers without manually managing data passing between them","I need to handle failures in one service and retry or fallback to alternative approaches"],"best_for":["Platform teams building multi-service AWS workflows","AI agents that need to orchestrate complex infrastructure operations","DevOps automation tools that combine multiple AWS service interactions"],"limitations":["Orchestration overhead adds latency (~50-200ms per service hop) due to inter-server communication","No built-in distributed transaction support — partial failures can leave infrastructure in inconsistent states","Tool composition requires explicit dependency declaration — implicit dependencies may cause silent failures"],"requires":["Multiple MCP servers running and discoverable","Network connectivity between Core MCP Server and all service-specific servers","Shared credential context or per-server authentication"],"input_types":["composite operation requests","tool dependency graphs","service-specific parameters"],"output_types":["aggregated results from multiple services","execution logs with per-service traces","error reports with failure context"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_3","uri":"capability://tool.use.integration.server.capability.discovery.and.schema.advertisement","name":"server capability discovery and schema advertisement","description":"Exposes the complete set of tools, resources, and capabilities available from each MCP server through standardized schema definitions that clients can query and introspect. Implements JSON Schema-based tool definitions that describe input parameters, output formats, and constraints for every operation. Supports dynamic capability updates when servers are added or removed from the ecosystem.","intents":["I want my AI client to automatically discover what AWS operations are available without hardcoding tool lists","I need to validate that a requested operation is supported before attempting to execute it","I want to generate documentation or UI forms from server capability schemas"],"best_for":["AI client developers building dynamic tool discovery systems","Teams implementing self-documenting MCP server ecosystems","Platform builders who need to expose AWS capabilities to non-technical users"],"limitations":["Schema advertisement is static at server startup — runtime capability changes require server restart","Complex nested schemas can be difficult for clients to parse and validate","No versioning mechanism for schema evolution — breaking changes require careful coordination"],"requires":["MCP client with schema introspection support","Access to Core MCP Server or individual service servers","JSON Schema validator (optional, for client-side validation)"],"input_types":["capability discovery requests","optional filtering criteria (by service, operation type)"],"output_types":["JSON Schema definitions","tool metadata (description, examples, constraints)","capability inventory"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_4","uri":"capability://safety.moderation.request.validation.and.constraint.enforcement","name":"request validation and constraint enforcement","description":"Validates incoming requests against tool schemas and AWS service constraints before execution, catching invalid parameters, missing required fields, and constraint violations early. Implements multi-layer validation: schema validation (JSON Schema), AWS service-specific constraints (e.g., Lambda memory limits), and permission checks (IAM policy simulation). Provides detailed error messages that guide users toward valid requests.","intents":["I want to catch invalid AWS parameters before they're sent to the service and fail","I need to validate that a requested operation complies with AWS service limits and constraints","I want clear error messages that explain what's wrong with my request and how to fix it"],"best_for":["AI agents that need to prevent invalid AWS API calls","Teams building user-facing tools that need to provide helpful validation feedback","DevOps automation that must enforce organizational constraints (e.g., max instance size)"],"limitations":["Validation adds ~50-100ms latency per request due to schema checking and constraint evaluation","AWS service constraints are not always fully documented — some edge cases may only be caught at API call time","Permission validation requires IAM policy simulation, which has eventual consistency delays"],"requires":["JSON Schema validator","AWS credentials for IAM policy simulation (optional)","Access to AWS service documentation for constraint definitions"],"input_types":["operation requests with parameters","tool schema definitions","AWS service constraints"],"output_types":["validation pass/fail status","detailed error messages with remediation hints","constraint violation reports"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_5","uri":"capability://safety.moderation.credential.and.authentication.context.management","name":"credential and authentication context management","description":"Manages AWS credentials and authentication context across multiple MCP servers and client connections, supporting various credential sources (IAM roles, temporary credentials, cross-account access). Implements credential injection into tool calls without exposing credentials to clients, and handles credential refresh for long-running operations. Supports credential scoping to limit what each server can access.","intents":["I want to run MCP servers with specific IAM roles that limit what they can access","I need to support temporary credentials that expire and must be refreshed during long operations","I want to enable cross-account AWS access through assumed roles without sharing credentials"],"best_for":["Enterprise teams with strict credential management policies","Multi-tenant SaaS platforms that need to isolate AWS access per customer","DevOps teams managing MCP servers across multiple AWS accounts"],"limitations":["Credential refresh adds latency (~100-500ms) when tokens expire during operations","Cross-account access requires pre-configured trust relationships — not all account combinations are supported","No built-in credential rotation — requires external credential management systems"],"requires":["AWS IAM role or temporary credentials","STS (Security Token Service) access for credential refresh","Optional: external credential provider (e.g., AWS Secrets Manager)"],"input_types":["credential sources (IAM role, temporary tokens, credential files)","credential scope specifications","cross-account role ARNs"],"output_types":["authenticated tool calls","credential validity status","access denial errors with remediation hints"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_6","uri":"capability://automation.workflow.error.handling.and.operation.retry.with.exponential.backoff","name":"error handling and operation retry with exponential backoff","description":"Implements comprehensive error handling for transient failures, rate limiting, and service unavailability across all MCP servers. Uses exponential backoff with jitter to retry failed operations, distinguishing between retryable errors (e.g., throttling, temporary service issues) and permanent failures (e.g., invalid parameters, access denied). Provides detailed error context including AWS error codes, retry counts, and recommendations.","intents":["I want my MCP operations to automatically retry on transient failures without manual intervention","I need to handle AWS service throttling gracefully without overwhelming the service","I want clear error messages that distinguish between temporary issues and permanent failures"],"best_for":["Long-running AI workflows that need resilience to transient AWS service issues","High-volume automation that must respect AWS rate limits","Teams building production-grade MCP integrations that need reliability"],"limitations":["Retry logic adds latency (up to several seconds for exponential backoff) on transient failures","No circuit breaker pattern — repeated failures can still exhaust retry budgets","Retry configuration is global — no per-operation retry tuning without code changes"],"requires":["Configurable retry parameters (max retries, backoff multiplier)","Access to AWS error codes and retry semantics","Optional: CloudWatch metrics for monitoring retry behavior"],"input_types":["failed operation requests","AWS error responses","retry configuration"],"output_types":["retry decisions (retry vs fail)","detailed error reports with retry history","success responses after retries"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_7","uri":"capability://automation.workflow.execution.tracing.and.observability.instrumentation","name":"execution tracing and observability instrumentation","description":"Instruments all MCP operations with detailed execution traces that capture request/response data, latency, errors, and cross-service dependencies. Integrates with AWS CloudWatch and X-Ray for centralized observability, emitting structured logs and traces that enable debugging and performance analysis. Supports sampling and filtering to manage observability overhead.","intents":["I want to trace how a complex multi-service operation executed and where time was spent","I need to debug failures by seeing the exact requests and responses at each step","I want to monitor MCP server performance and identify bottlenecks"],"best_for":["DevOps teams operating MCP servers in production","AI application developers debugging complex orchestration workflows","Platform teams building observability dashboards for MCP ecosystems"],"limitations":["Tracing adds ~10-50ms overhead per operation due to instrumentation and log emission","Detailed traces can generate large volumes of log data, increasing storage costs","Sampling reduces observability — sampled traces may miss rare failure scenarios"],"requires":["CloudWatch Logs access for log emission","Optional: X-Ray daemon for distributed tracing","Structured logging configuration (JSON format)"],"input_types":["MCP operations","tracing configuration (sampling rate, verbosity)","optional correlation IDs"],"output_types":["structured execution logs","distributed traces with service dependencies","performance metrics (latency, throughput)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_8","uri":"capability://safety.moderation.resource.quota.and.rate.limiting.enforcement","name":"resource quota and rate limiting enforcement","description":"Enforces resource quotas and rate limits on MCP operations to prevent resource exhaustion and ensure fair usage across multiple clients. Implements per-client rate limiting, per-service quota tracking, and global resource limits. Provides quota status visibility and graceful degradation when limits are approached.","intents":["I want to prevent a single client from overwhelming the MCP server with requests","I need to enforce AWS service quotas (e.g., max concurrent Lambda executions) across all clients","I want to implement fair-share resource allocation across multiple concurrent users"],"best_for":["Multi-tenant MCP deployments serving multiple AI clients","Teams implementing SaaS platforms with MCP backends","Enterprise environments with strict resource governance requirements"],"limitations":["Rate limiting adds ~5-10ms latency per request for quota checking","Quota enforcement is local to a single server instance — distributed deployments need external quota coordination","No dynamic quota adjustment — quotas must be pre-configured and cannot adapt to load"],"requires":["Configurable rate limit parameters (requests per second, burst size)","Optional: distributed cache (Redis) for quota coordination across instances","Client identification mechanism (API keys, IAM principals)"],"input_types":["incoming MCP requests","quota configuration","client identity"],"output_types":["rate limit decisions (allow vs reject)","quota status (remaining quota, reset time)","rate limit exceeded errors"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-core__cap_9","uri":"capability://memory.knowledge.conversation.state.persistence.and.context.management","name":"conversation state persistence and context management","description":"Manages conversation state and context across multiple MCP interactions, enabling multi-turn workflows where later operations reference earlier results. Implements state storage with optional persistence to external backends (DynamoDB, S3), supporting state snapshots and rollback. Handles state cleanup and garbage collection for completed conversations.","intents":["I want my AI assistant to remember the results of previous operations in a conversation","I need to support long-running workflows that span multiple user interactions","I want to enable rollback to previous conversation states if something goes wrong"],"best_for":["Conversational AI applications that need multi-turn AWS workflows","Interactive DevOps tools that maintain state across multiple user actions","Agentic systems that need to track progress through complex operations"],"limitations":["State persistence adds latency (~50-200ms per operation) for storage operations","In-memory state is lost on server restart — requires external persistence for durability","State size is unbounded — large conversations can consume significant memory"],"requires":["Optional: DynamoDB or S3 for persistent state storage","Conversation ID generation and tracking","State serialization format (JSON)"],"input_types":["MCP operations with conversation context","state snapshots","rollback requests"],"output_types":["updated conversation state","state snapshots","state query results"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","MCP protocol SDK (included in awslabs/mcp)","AWS credentials if server needs to access AWS services","MCP client with prompt submission capability","Access to Core MCP Server instance","Conversation history (for multi-turn context)","Template syntax definition (e.g., Jinja2, handlebars)","Variable resolution context (previous results, environment)","Template validation engine","CloudTrail enabled for AWS API logging"],"failure_modes":["Server initialization is synchronous — no async pre-warming of resources before client connections","Capability negotiation happens per-client, not globally — can lead to repeated discovery overhead with many clients","No built-in load balancing across multiple server instances — requires external orchestration","Semantic routing relies on prompt clarity — ambiguous or vague requests may route incorrectly without clarification","Context window is limited by client's conversation history — long-running sessions may lose earlier context","No explicit intent confidence scoring — routing decisions are binary rather than probabilistic","Template resolution adds ~20-50ms latency per operation","Complex template expressions can be difficult to debug","No type checking for template variables — type mismatches only caught at execution time","Audit logging adds ~20-100ms latency per operation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=aws-core","compare_url":"https://unfragile.ai/compare?artifact=aws-core"}},"signature":"GyJ8A8kikiTWwLvBHkm2b72zmiFSfgI4yYUrwx2IEvOcEeiHDk5OMRJDLiKIbJ/JMW4EGUVKfLrtLWukucTtAw==","signedAt":"2026-06-22T16:26:53.786Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aws-core","artifact":"https://unfragile.ai/aws-core","verify":"https://unfragile.ai/api/v1/verify?slug=aws-core","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"}}