"AI-Powered Virtual Medical Consultation Assistant" matching MCP servers:

An MCP server and web application that enables natural language medical queries and patient data analysis powered by Gemini AI. It allows users to track and analyze patient biometrics, including vitals, sleep patterns, and laboratory results, through a comprehensive set of automated medical tools.

Enables chat, document search (RAG), and a declarative SQL tool builder over MCP so any client can use these capabilities.

Enables secure execution of Python code, SQL queries, and metric fetching through MCP with ephemeral Docker sandboxing.

Enables AI-powered interaction with Metabase instances and PostgreSQL databases through natural language. Creates models, SQL queries, metrics, and dashboards using both Metabase API and direct database connections.

Enables read- or write-scoped access to synthetic enterprise orders, inventory, support tickets, and metrics via schema-validated JSON-RPC tools over stdio and HTTP, with permission allow-lists and audit logging. It also exposes enterprise resources and prompt templates for grounded, cited, multi-step assistant workflows.

Exposes a single HTTP endpoint that aggregates tool definitions from GitHub, Kubernetes, and database namespaces, letting clients list available tools and invoke them by name. It provides health-check, tool-discovery, and tool-call routes for local, read-only experimentation without calling hosted models or applying production changes.

Enables LLMs to query real job data and summarize backend job failures using MCP tools backed by a SQLite database.

MCP server with a fictitious clinical database to develop and test agents, providing tools to search patients and retrieve full medical records (inpatient, outpatient, emergency) via HTTP streamable transport.

Enables querying local MySQL databases and managing Docker containers (listing, logs) through natural language via the Model Context Protocol.

Provides read-only EspoCRM access for AI assistants, enabling record retrieval and preparation of signed change sets without direct modification or deletion.

A database operation server based on the MCP protocol, providing database connection, querying, schema exploration, data analysis, and SQL generation tools.

Enables troubleshooting of LDAP directory services through natural language queries, providing health diagnostics, replication status, performance metrics, log analysis, and configuration comparisons across multiple servers.

A Model Context Protocol (MCP) server for SQL Server / Azure SQL that enables querying, monitoring, and analyzing databases directly from Claude.

Enables querying student information such as marks, attendance, pending assignments, and timetables from a MySQL database through MCP tools.

Multi-tenant, stateless MCP server for conversational business assistants, handling business resolution, knowledge search, and data mutations with human confirmation and security guardrails.

Provides MCP tools for searching student profiles, viewing marks/attendance, and finding low-attendance students. Enables AI assistants like Gemini to interact with college student data through natural language.

Connects a local LLM (Ollama) to a SQLite database via the Model Context Protocol, enabling natural language queries and controlled read/write database operations through MCP tools.

Enables a unified AI assistant that combines document retrieval (RAG), database queries via MCP tools, and web search, allowing users to ask complex questions and receive answers from multiple enterprise sources.

Enables AI-assisted chiller troubleshooting by exposing MCP tools to retrieve operational data, alarms, telemetry, and service history from MongoDB Atlas, plus vector and hybrid search over knowledge bases.

Enables governed interaction with synthetic people data through MCP tools, providing per-caller scoped access, semantic-layer metrics, and read-only SQL with authorization checks.