"How to automate workflows in DaVinci Resolve and Fusion" matching MCP servers:

Enables interaction with Microsoft Power Automate cloud flows via Azure CLI, allowing management of flows, triggers, runs, and connections.

A local stdio MCP server providing read-only access to Oracle Fusion/ERP Cloud databases via the BI Publisher SOAP API, enabling object discovery, table descriptions, source reading, and SELECT queries.

Local-first MCP server and Chromium extension for AI-assisted Microsoft Power Automate work, enabling users to inspect, validate, edit, run, review, and revert cloud flows using a browser session.

Enables AI assistants to manage and troubleshoot PingOne DaVinci identity orchestration flows, applications, connectors, variables, and forms through standardized MCP tools.

Enables AI agents to explore Oracle Fusion Cloud schema metadata, run pre-built BI Publisher reports with bind parameters, and validate results against trusted ground truth.

Enables AI agents to discover Azure DevOps repositories, manage pull requests, and interact with PR comments and reviews via the Model Context Protocol.

Hosted workflow discovery, validation, execution, and lifecycle facade for Stromy, with Entra ID role-based scoping and Azure Container Apps integration.

A self-hosted MCP server that enables AI assistants to inspect and manage Microsoft Power Automate cloud flows and Dataverse records locally, using your own Azure identity.

A remote MCP server exposing 36 Fusion 360 modeling tools over HTTPS, enabling secure remote control of Autodesk Fusion 360 with authentication, rate limiting, and audit logging.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Debug, build, and manage Microsoft Power Automate cloud flows with AI agents. Get action-level error details, build flows from natural language, trigger and resubmit runs, and operate across multiple tenants. Requires a Flow Studio MCP subscription — get an API key at https://mcp.flowstudio.app

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

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe a fine-tuning workload once and receive task-aware VRAM/FLOP estimates plus ranked cheapest, fastest, and balanced GPU options across multiple cloud providers. It can be used from Claude Code/Desktop or CLI/API to identify viable hardware options without manually comparing provider pricing.

Enables users to describe an LLM fine-tuning job in natural language and receive ranked GPU recommendations from a dozen cloud providers, based on estimated VRAM and FLOPs requirements. It surfaces the cheapest, fastest, and most balanced hardware options while disclosing the source and freshness of each price.

Enables users to describe an LLM fine-tuning workload once and receive ranked cloud GPU recommendations with estimated VRAM/FLOPs requirements, cost, runtime, and data-source transparency.

Enables users to describe an LLM fine-tuning workload and receive VRAM/FLOPs estimates plus ranked GPU recommendations across multiple cloud providers, optimizing for cost, speed, or balance.