"An example MCP for client prototyping in a business context with a public API call" matching MCP servers:

An MCP server that enables interaction with Google's My Business Account Management API, allowing users to perform account management operations through natural language interactions.

Enables LLMs to create, onboard, and diagnose Cisco SD-WAN branch edges using MCP tools that interact with a FastAPI automation backend, with guardrails for safe operations.

A basic example of developing and running serverless MCP using Netlify Functions.

A basic example of a Model Context Protocol (MCP) server deployed as a Netlify Function, demonstrating how to serve MCP endpoints over HTTP.

Enables AI assistants to read and modify Microsoft Dynamics 365 Business Central data through the API v2.0, supporting CRUD operations on resources like customers, contacts, and sales orders with Azure CLI or client credentials authentication.

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.

Enables MCP clients to interact with Microsoft Dynamics 365 Business Central entities, providing tools to get schemas, list, create, update, and delete records.

Connects AI assistants to Axcient for real-time data retrieval and actions through natural conversation.

Demonstrates how to secure an MCP server with OAuth 2.1 using AWS Cognito, with support for dynamic client registration and client ID metadata documents.

Example MCP server for Autodesk Platform Services that demonstrates proper OAuth authorization with Auth0 as the first layer and APS 3-legged OAuth as the second layer. Enables querying APS projects and issues through natural language after authenticating via Auth0 and APS.

Exposes Dynamics 365 Business Central data to MCP clients via standard v2.0 API or custom AL APIs, supporting read, write, and destructive operations with multiple authentication modes.

A VMware ESXi/vCenter management server based on MCP (Machine Control Protocol), providing simple REST API interfaces for virtual machine management.

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.