"How to configure Azure Speech" matching MCP servers:

The Azure MCP Server, bringing the power of Azure to your agents.

Service that lets AI Agents search, register, and configure domains

Azure FinOps infrastructure cost optimization

Enables AI assistants to generate expressive speech using Hume AI's Octave Text-to-Speech, allowing for natural language-based audio synthesis and voice management.

Enables AI agents to use speech AI capabilities including LLM chat completions, text-to-speech in Uzbek, Russian, and English, speech-to-text transcription with speaker labels, voice isolation for noise removal, and realtime WebSocket streaming.

Provides Azure DevOps tooling for AI agents, enabling interaction with projects, work items, repositories, and pipelines through natural language.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

What is PandaDoc MCP? PandaDoc MCP (Model Context Protocol) server connects AI agents directly to PandaDoc's document workflow — creating, sending, and tracking contracts and agreements without leaving your AI client. How to use PandaDoc MCP? To use PandaDoc MCP, connect your PandaDoc account, provide the required API credentials, and configure the appropriate MCP client.

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 face detection and recognition in Agentic AI workflows using Azure Face API, supporting attribute detection, image comparison, and face recognition.

Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

This MCP server lets AI agents connect to Figma to read, analyze, create, and modify designs, including elements, styles, assets, and code generation. It works with free Figma accounts and supports parallel multi-agent use across MCP-compatible clients.

A remote MCP server (Node.js/TypeScript) deployed on Azure Container Apps that provides todo list management tools with JWT authentication and role-based access control.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

This read-only MCP Server allows you to connect to Azure Data Lake Storage 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 clients to create and configure monitors, subscribe to their evidence streams with durable cursors, and process observations with guaranteed at-least-once delivery.

Enables voice conversations and screen capture for AI agents via MCP-compatible clients, using Pipecat for speech-to-text and text-to-speech, with support for browser, WebRTC, and phone transport.

Enables AI assistants to configure Snoooz email automation in natural language, including creating rules, follow-ups, training, and troubleshooting.

Enables transcript timestamp-aligned speech filler word removal and cutlist generation for automated video editing, with zero-dependency MCP integration.