"Tools for Testing in Python and Java" matching MCP servers:

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 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.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.

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

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs calculations with cheapest, fastest, and balanced GPU recommendations across a dozen cloud providers. It supports CLI, REST API, and natural-language MCP integration without manual price comparison.

Enables users to describe an LLM fine-tuning workload and automatically estimate VRAM/FLOPs to recommend the cheapest, fastest, and balanced GPU options across multiple cloud providers. It supports natural-language requests through MCP, returning structured ranked hardware recommendations without manual price comparison.

Enables coding agents to inspect and change Cloudflare zones, DNS, settings, analytics, Workers, Pages, storage, tunnels, and Email Routing. Read tools run without prompting, while write tools are gated by the host's permission rules.

An MCP server for securely executing Python code, supporting STDIO, Streamable HTTP, and SSE transports, with easy deployment on Heroku.

Enables AI assistants to manage OpenStack cloud resources including compute, images, identity, network, and block storage via the Model Context Protocol.

Talk to Okta IGA like you talk to a coworker. Import access data, discover patterns, and create entitlement bundles—all through natural conversation.

Enables AI agents to query multi-cloud costs, inventory, waste, and commitments across AWS, GCP, Cloudflare, and OVH through read-only MCP tools.

A quickstart template to build and deploy a remote MCP server on Azure Functions using Python, secured with keys and HTTPS.

Enables AI assistants to manage Coolify infrastructure including servers, applications, databases, deployments, and 80+ one-click services through 98 comprehensive tools for both cloud and self-hosted instances.

Enables deploying a minimal Model Context Protocol server on Render with bearer-token authentication, health checks, and an example tool for adding custom MCP tools.

Enables users to deploy a minimal Model Context Protocol server on Render, connect MCP clients with bearer-token authentication, and add custom tools callable through the MCP endpoint.

Enables deploying a minimal Model Context Protocol server on Render with bearer token authentication and a scaffold for adding custom tools.

Enables deployment and connection to a minimal Streamable HTTP MCP server with bearer-token authentication, a health endpoint, and an example tool, while allowing developers to add custom tools.

It enables users to describe an LLM fine-tuning workload once and receive ranked GPU recommendations for cheapest, fastest, or balanced options across many cloud providers by estimating VRAM/FLOPs and aggregating live pricing.

Enables read-only queries against a GCP PostgreSQL database, exposing tools to fetch recent users, user details by ID, and recent user activities without returning password or hash columns.

This template enables developers to quickly scaffold, deploy, and extend a Model Context Protocol server on Render, with bearer-token authentication and a streamable HTTP endpoint, by adding custom tools via Python.

Enables deploying a minimal MCP server on Render with Streamable HTTP transport, bearer-token authentication, a health check endpoint, and a sample tool.

A minimal MCP server template for Python with Streamable HTTP transport, bearer token authentication, and a health check endpoint, designed for one-click deployment on Render.