"Exploring OCR and Advanced Analysis and Reasoning Techniques" matching MCP servers:

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

A Model Context Protocol server that enables large language models like Claude to perform comprehensive interactions with Firebase Firestore databases, supporting full CRUD operations, complex queries, and advanced features like transactions and TTL management.

A server that enables executing and validating SQL queries against Google BigQuery with safety features that prevent data modifications and excessive processing.

Stateless Streamable HTTP MCP endpoint with OAuth 2.1 authentication via Cloudflare Workers, supporting PKCE, DCR, and token introspection.

This read-only MCP Server allows you to connect to Azure Analysis Services data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

Enables AI agents to discover and invoke backend tools over MCP JSON-RPC while enforcing 3-legged OAuth 2.0 identity propagation, role-based access control, and protocol transcoding to REST APIs.

A deployable MCP server template for Cloudflare Workers that provides Google OAuth authentication and comprehensive analytics tracking of tool usage, performance metrics, and user behavior.

Enables safe VPS diagnostics and Docker/Docker Compose management over SSH, providing predefined read-only and mutating tools for system monitoring, container inspection, and Compose orchestration without exposing arbitrary shell execution.

MCP server for the Red Hat Advanced Cluster Security for Kubernetes API, providing LLM access to all 69 API services and 361 endpoints through six meta-tools for discovery, inspection, and direct API calls.

Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.

MCP server for discovering and exploring Azure Verified Modules (AVM) from the Bicep Public Registry, enabling AI agents to search, retrieve module details, and access documentation.

Enables exploring Microsoft Fabric / Power BI workspaces and semantic models, and executing ad-hoc DAX queries.

Enables querying, exploring, and interacting with Azure SQL databases through MCP-compatible clients like Claude Desktop.

An MCP server for managing and monitoring Docker, Docker Compose, and Kubernetes environments alongside Azure Application Insights. It enables advanced log filtering, container lifecycle management, and querying of cloud application traces and metrics.

Enables comprehensive analysis of AWS Lambda performance including cold start tracking, memory utilization monitoring, cost analysis, and optimization recommendations. Provides real-time metrics, comparative analysis across multiple functions, and actionable insights to improve Lambda performance and reduce costs.

Provides secure, permission-controlled access to Firebase Firestore databases with full CRUD operations, advanced queries, batch operations, and transactions through a standardized MCP interface.

Enables AI models to interact with Tencent Cloud COS for cloud storage operations (upload, download, list files) and advanced media processing capabilities (image super-resolution, QR code recognition, video thumbnails, document conversion, and natural language-based file search).

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

Automates the AWS Foundational Technical Review (FTR) partner self-assessment by evaluating SOC 2 and WAFR reports against defined controls, returning structured PASS/FAIL decisions with reasoning.