{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-aws-bedrock-kb-retrieval","slug":"aws-bedrock-kb-retrieval","name":"AWS Bedrock KB Retrieval","type":"mcp","url":"https://github.com/awslabs/mcp/tree/main/src/bedrock-kb-retrieval-mcp-server","page_url":"https://unfragile.ai/aws-bedrock-kb-retrieval","categories":["mcp-servers","rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-aws-bedrock-kb-retrieval__cap_0","uri":"capability://memory.knowledge.natural.language.knowledge.base.querying.with.semantic.retrieval","name":"natural-language knowledge base querying with semantic retrieval","description":"Accepts free-form natural language queries and translates them into semantic search operations against Amazon Bedrock Knowledge Bases using the Bedrock Agents API. The MCP server acts as a bridge that converts client tool calls into RetrieveAndGenerate API invocations, handling query embedding, vector similarity matching, and result ranking through Bedrock's managed retrieval pipeline without requiring clients to manage embedding models or vector indices directly.","intents":["I want to ask questions about my company's internal documentation and get relevant answers without writing SQL or structured queries","I need to retrieve specific information from a large knowledge base using conversational language","I want to integrate knowledge base retrieval into an AI agent workflow without managing embeddings infrastructure"],"best_for":["AI agent builders integrating enterprise knowledge bases into Claude or other LLM workflows","Teams using MCP-compatible clients (Claude Desktop, IDEs with MCP support) who need RAG capabilities","Organizations with existing Bedrock Knowledge Bases seeking standardized access patterns"],"limitations":["Requires pre-configured Bedrock Knowledge Base with indexed data sources — no inline document ingestion","Query latency depends on Bedrock KB indexing quality and vector model performance; typically 1-5 seconds per query","No built-in query rewriting or multi-turn context management — each query is independent unless client maintains conversation state","Limited to Bedrock's supported data source types (S3, web crawl, Confluence, Salesforce, SharePoint); custom data sources require pre-indexing"],"requires":["AWS account with Bedrock service access and at least one configured Knowledge Base","IAM permissions for bedrock:Retrieve and bedrock:RetrieveAndGenerate actions","MCP client implementation (Claude Desktop, custom MCP host, or compatible IDE)","Python 3.9+ for running the MCP server process"],"input_types":["text (natural language query string)","structured metadata (optional filters for knowledge base selection)"],"output_types":["text (retrieved document excerpts with source attribution)","structured JSON (metadata including source document, relevance score, retrieval confidence)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_1","uri":"capability://tool.use.integration.mcp.tool.registration.for.knowledge.base.access","name":"mcp tool registration for knowledge base access","description":"Registers Bedrock KB retrieval as a callable tool within the MCP protocol using the tools/list and tools/call message types, enabling LLM clients to discover the retrieval capability and invoke it with structured arguments. The server implements the MCP tool schema with input validation, error handling, and response formatting that conforms to MCP's tool response envelope, allowing seamless integration into agent decision-making loops without custom client code.","intents":["I want my AI agent to automatically decide when to query the knowledge base based on user intent","I need the knowledge base retrieval to appear as a native tool option in my MCP client","I want structured error handling and response validation when knowledge base queries fail"],"best_for":["Developers building multi-tool AI agents using MCP protocol","Teams deploying Claude with tool use enabled who want knowledge base access","Organizations standardizing on MCP for AWS service integration"],"limitations":["Tool discovery is static at server startup — cannot dynamically register new knowledge bases without server restart","MCP tool calling adds ~50-100ms overhead per invocation due to JSON serialization and protocol framing","No built-in tool use caching — repeated identical queries trigger full Bedrock KB calls","Tool response size limited by MCP protocol constraints; very large result sets may require pagination"],"requires":["MCP-compatible client with tool use support (Claude 3.5+, custom MCP host)","Bedrock Knowledge Base already created and indexed","IAM credentials with bedrock:Retrieve permissions"],"input_types":["JSON object with query string and optional knowledge base ID"],"output_types":["MCP tool response envelope containing retrieved documents, metadata, and status"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_2","uri":"capability://memory.knowledge.multi.knowledge.base.routing.and.selection","name":"multi-knowledge-base routing and selection","description":"Supports querying across multiple Bedrock Knowledge Bases by accepting a knowledge base ID parameter in tool calls, allowing clients to specify which KB to query or implement routing logic. The server maintains a registry of available knowledge bases (discovered via Bedrock API or configuration) and routes each query to the appropriate KB, enabling use cases where different data sources are organized by domain, team, or data classification level.","intents":["I want to query different knowledge bases (e.g., product docs vs. internal policies) from a single agent interface","I need to route queries to the most relevant knowledge base based on query content or user context","I want to manage access control by restricting which knowledge bases different users can query"],"best_for":["Enterprise organizations with multiple Bedrock Knowledge Bases organized by domain or team","Multi-tenant applications where different customers have separate knowledge bases","Agents that need to intelligently select between multiple data sources"],"limitations":["No automatic KB selection — client must explicitly specify KB ID or implement selection logic externally","No cross-KB search or federated queries — each query targets a single KB","KB discovery requires IAM permissions to list Bedrock KBs; no built-in caching of KB metadata","Routing logic is client-side; server does not implement intelligent KB selection based on query semantics"],"requires":["Multiple Bedrock Knowledge Bases configured in the same AWS account/region","IAM permissions for bedrock:ListKnowledgeBases and bedrock:Retrieve on all target KBs","Client logic to determine which KB to query (or accept KB ID as parameter)"],"input_types":["JSON object with query string and knowledge_base_id parameter"],"output_types":["Retrieved documents from specified knowledge base with source attribution"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_3","uri":"capability://memory.knowledge.source.attribution.and.metadata.extraction","name":"source attribution and metadata extraction","description":"Extracts and returns source document metadata (document name, location, retrieval confidence score, chunk ID) alongside retrieved content, enabling clients to trace answers back to original sources and assess retrieval quality. The server parses Bedrock KB response envelopes to surface metadata fields that clients can use for citation, audit trails, or relevance filtering, without requiring additional API calls to fetch source information.","intents":["I want to cite the source document when presenting retrieved information to users","I need to assess confidence in retrieved results and filter low-confidence matches","I want to audit which documents were used to generate agent responses"],"best_for":["Applications requiring source attribution for compliance or transparency","Agents that need to assess retrieval quality before using results","Enterprise deployments with audit and traceability requirements"],"limitations":["Metadata richness depends on Bedrock KB indexing configuration — some source types provide limited metadata","Confidence scores are Bedrock-generated and may not align with semantic relevance in all domains","No built-in deduplication of sources — same document may appear multiple times in results","Source URLs may be relative paths or S3 URIs that require additional resolution to be user-readable"],"requires":["Bedrock Knowledge Base with metadata extraction enabled","Client code to parse and display metadata alongside retrieved content"],"input_types":["natural language query"],"output_types":["JSON with document content, source name, document ID, retrieval score, chunk metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_4","uri":"capability://safety.moderation.error.handling.and.graceful.degradation.for.retrieval.failures","name":"error handling and graceful degradation for retrieval failures","description":"Implements MCP-compliant error handling that catches Bedrock API failures (throttling, invalid KB ID, permissions errors) and returns structured error responses with diagnostic information, allowing clients to implement retry logic or fallback strategies. The server distinguishes between transient errors (throttling, temporary service issues) and permanent errors (invalid KB, permission denied) to guide client behavior, and includes error context that helps developers debug integration issues.","intents":["I want my agent to handle knowledge base query failures gracefully without crashing","I need to distinguish between transient errors (retry) and permanent errors (fallback to other sources)","I want diagnostic information when knowledge base queries fail to help debug integration issues"],"best_for":["Production agents that need resilience to Bedrock service disruptions","Multi-source agents that can fall back to other tools when KB retrieval fails","Teams debugging integration issues between MCP clients and Bedrock"],"limitations":["Error classification is heuristic-based on Bedrock error codes; some edge cases may be misclassified","No built-in retry logic — clients must implement exponential backoff if desired","Error messages may expose sensitive information (KB IDs, AWS account details) if not sanitized","Timeout handling depends on MCP client implementation; server cannot force timeout behavior"],"requires":["MCP client that properly handles error responses","Client-side retry and fallback logic for production resilience"],"input_types":["natural language query"],"output_types":["MCP error response with error code, message, and diagnostic context"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_5","uri":"capability://safety.moderation.query.parameter.validation.and.sanitization","name":"query parameter validation and sanitization","description":"Validates incoming MCP tool call parameters (query string length, knowledge base ID format, optional filters) before sending to Bedrock API, preventing malformed requests and reducing unnecessary API calls. The server implements input validation rules (max query length, KB ID pattern matching, filter syntax) and returns validation errors to clients before attempting Bedrock calls, reducing latency and API costs for invalid requests.","intents":["I want to catch invalid queries early without wasting API calls to Bedrock","I need to enforce query length limits to prevent abuse or excessive costs","I want to validate knowledge base IDs to catch typos before they reach the API"],"best_for":["Public-facing agents where input validation is critical for security and cost control","Multi-tenant deployments where input validation prevents cross-tenant data leakage","Cost-conscious deployments where reducing invalid API calls saves money"],"limitations":["Validation rules are static and cannot be dynamically updated without server restart","No semantic validation — cannot detect nonsensical queries, only structural issues","Overly strict validation may reject valid edge cases (e.g., queries with special characters)","Validation adds ~5-10ms latency per request"],"requires":["Configuration of validation rules (max query length, allowed KB ID patterns)","Client code to handle validation error responses"],"input_types":["JSON object with query and optional parameters"],"output_types":["Validation error response or pass-through to Bedrock"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_6","uri":"capability://automation.workflow.mcp.server.lifecycle.management.and.configuration","name":"mcp server lifecycle management and configuration","description":"Manages server initialization, configuration loading from environment variables or config files, and graceful shutdown. The server implements MCP server initialization protocol (capabilities negotiation, resource listing) and loads Bedrock credentials and KB configuration at startup, enabling deployment in containerized environments (Docker, Lambda, ECS) with standard configuration patterns. Supports environment-based configuration for AWS region, credentials, and KB metadata.","intents":["I want to deploy this MCP server in a Docker container with environment variable configuration","I need to configure which Bedrock Knowledge Bases are accessible without modifying code","I want the server to properly initialize and advertise its capabilities to MCP clients"],"best_for":["DevOps teams deploying MCP servers in containerized environments","Organizations using Infrastructure-as-Code (Terraform, CloudFormation) to manage MCP servers","Teams running MCP servers in AWS Lambda or ECS for serverless deployments"],"limitations":["Configuration is loaded at startup; changes require server restart","No built-in configuration hot-reload or dynamic KB discovery","Credentials must be provided via environment variables or IAM role; no built-in secrets management","Server does not implement MCP resource discovery — KB list must be pre-configured or discovered via separate API call"],"requires":["Python 3.9+ runtime","AWS credentials (IAM role or environment variables)","MCP client that supports server initialization protocol"],"input_types":["environment variables, configuration files"],"output_types":["server initialization response with capabilities"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-aws-bedrock-kb-retrieval__cap_7","uri":"capability://memory.knowledge.streaming.response.support.for.large.result.sets","name":"streaming response support for large result sets","description":"Implements MCP streaming protocol to return large knowledge base results in chunks rather than buffering entire responses, enabling clients to process results incrementally and reducing memory overhead. The server streams document chunks and metadata as they arrive from Bedrock, allowing clients to display results progressively and handle large result sets without loading everything into memory at once.","intents":["I want to display search results progressively as they arrive rather than waiting for all results","I need to handle large result sets without loading everything into memory","I want to stream results to a user interface for real-time feedback"],"best_for":["Interactive applications where progressive result display improves UX","Memory-constrained environments (mobile, edge devices) where buffering is expensive","Real-time agent applications where latency to first result matters"],"limitations":["Streaming support depends on MCP client implementation — not all clients support streaming","Bedrock KB API may not support streaming; results may be buffered server-side before streaming to client","Error handling is more complex with streaming — errors mid-stream may result in partial results","Streaming adds complexity to client code for handling incremental results"],"requires":["MCP client with streaming support","Bedrock KB configured to return results in chunks"],"input_types":["natural language query"],"output_types":["streamed JSON chunks containing document content and metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["AWS account with Bedrock service access and at least one configured Knowledge Base","IAM permissions for bedrock:Retrieve and bedrock:RetrieveAndGenerate actions","MCP client implementation (Claude Desktop, custom MCP host, or compatible IDE)","Python 3.9+ for running the MCP server process","MCP-compatible client with tool use support (Claude 3.5+, custom MCP host)","Bedrock Knowledge Base already created and indexed","IAM credentials with bedrock:Retrieve permissions","Multiple Bedrock Knowledge Bases configured in the same AWS account/region","IAM permissions for bedrock:ListKnowledgeBases and bedrock:Retrieve on all target KBs","Client logic to determine which KB to query (or accept KB ID as parameter)"],"failure_modes":["Requires pre-configured Bedrock Knowledge Base with indexed data sources — no inline document ingestion","Query latency depends on Bedrock KB indexing quality and vector model performance; typically 1-5 seconds per query","No built-in query rewriting or multi-turn context management — each query is independent unless client maintains conversation state","Limited to Bedrock's supported data source types (S3, web crawl, Confluence, Salesforce, SharePoint); custom data sources require pre-indexing","Tool discovery is static at server startup — cannot dynamically register new knowledge bases without server restart","MCP tool calling adds ~50-100ms overhead per invocation due to JSON serialization and protocol framing","No built-in tool use caching — repeated identical queries trigger full Bedrock KB calls","Tool response size limited by MCP protocol constraints; very large result sets may require pagination","No automatic KB selection — client must explicitly specify KB ID or implement selection logic externally","No cross-KB search or federated queries — each query targets a single KB","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.49999999999999994,"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-bedrock-kb-retrieval","compare_url":"https://unfragile.ai/compare?artifact=aws-bedrock-kb-retrieval"}},"signature":"WlIDZfPvwkFeghzUcUaZkcPaNpmBLToB1D4a+XgWYMmYwntow2PiIVFItyP9YKuO6y5apehYbKNoJ0boOLJDAA==","signedAt":"2026-06-22T12:41:50.644Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aws-bedrock-kb-retrieval","artifact":"https://unfragile.ai/aws-bedrock-kb-retrieval","verify":"https://unfragile.ai/api/v1/verify?slug=aws-bedrock-kb-retrieval","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"}}