"AWS" matching MCP servers:

An MCP server that enables users to retrieve information from AWS Knowledge Bases using RAG (Retrieval-Augmented Generation) via Bedrock Agent Runtime.

MCP server that provides secure access to a serverless Markdown wiki on AWS, enabling AI assistants to read, search, and edit pages with space-level permission checks and Bedrock-powered semantic search.

🤖 Enable AI assistants (Claude, Cursor) to monitor, diagnose, and query Amazon Redshift using this MCP server and the AWS Data API.

Enables interaction with MySQL databases (including AWS RDS and cloud instances) through natural language. Supports database connections, query execution, schema inspection, and comprehensive database management operations.

A server that enables LLMs like Claude to query AWS DynamoDB databases through natural language requests, supporting table management, data querying, and schema analysis.

Enables MCP-compatible clients to search a shared ChromaDB-backed PDF document store and generate grounded, citation-aware answers through AWS Bedrock, without calling the REST API.

Provides hybrid (BM25 + vector) search over Terraform AWS and Google provider documentation, with tools for ranked search and full document retrieval.

Enables management and invocation of AWS Bedrock managed prompts with support for variable substitution, streaming responses, batch processing, and multiple AI models including Claude, Titan, Llama, and Mistral.

This MCP server enables any MCP client to perform web searches over HTTP using just an endpoint URL and API key, abstracting away AWS SigV4 signing and Bedrock AgentCore protocol translation entirely server-side.

Enables Claude to connect to and interact with SQLite, SQL Server, PostgreSQL, and MySQL databases through natural language. Supports executing queries, managing tables, exporting data, and storing business insights with authentication options including AWS IAM.

Enables AI assistants to search and explore Databricks AWS documentation through semantic search and 10 MCP tools.

Enables cloud and platform engineers to investigate AWS incidents by grounding responses in approved runbooks and operational evidence, with authenticated access, citations, observability, and human approval before any write action.

Provides tools for document search, AWS cost estimation, checking latest AWS updates, and creating budget alerts, with a confirmation gate on the mutating tool.

Enables secure querying of analytics data stored in AWS Athena/S3 through natural language, with multiple security layers including query validation, resource limits, and automatic PII redaction to prevent data breaches and destructive operations.

Enables natural language performance analysis and optimization of AWS Aurora MySQL databases, using AI (Bedrock, Claude) and RAG to generate HTML reports and recommendations.

Enables AI-powered vendor risk assessment using AWS Titan, allowing users to evaluate individual vendors, compare multiple vendors, and get industry risk benchmarks through natural language queries.

Deploys and manages MCP tools on Amazon Bedrock AgentCore Runtime, enabling knowledge base retrieval and AWS infrastructure management through Streamlit applications.

Provides semantic search over AWS Cloudscape Design System documentation using natural language queries, enabling AI assistants to efficiently find and retrieve component documentation with token-efficient responses.

Enables AI coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.

An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.

Provides a SQLite-backed MCP server that records agent memory as history, enabling explicit consolidation, source checks, revisions, narrative versions, and accountable forgetting.

MCP server that gives AI agents persistent, tenant-scoped memory with semantic search and consolidation, using CockroachDB vector indexes and Amazon Bedrock embeddings.

Enables coding agents to query a local, offline memory store of past session traces through hybrid BM25 plus semantic retrieval over extracted memories and chunks. Lets users recall relevant memories, search chunks and sessions, and explore cross-session clusters directly from their agent.

Provides unified access to 100+ AI models from OpenAI, Anthropic, Google, AWS Bedrock and other providers through a single MCP interface. Enables seamless switching between different AI models using LiteLM's unified API without requiring separate integrations for each provider.

Enables read, write, edit, and file transfer operations on TorchV AIS enterprise knowledge bases. Supports document management, repository browsing, and administrative tasks with configurable readonly, write, or admin permission levels.

Enables searching imported AI conversations locally across different providers from an MCP client, with read-only access to the archive.

Enables Claude to intelligently query and analyze your resume using RAG technology. Supports skill matching against job requirements and answering questions about your professional background from locally stored resume files.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

Enables AI agents to run citation-backed retrieval over a read-only, bounded knowledge base, exposing a ranked search tool, document resources, and a grounding prompt that labels retrieved text as untrusted. The corpus can be loaded from local disk or from AWS S3, Azure Blob Storage, or Google Cloud Storage through their official SDKs, with fail-closed startup if a provider cannot be reached.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.