Test Your MCP Server With Any AI Model — DeepSeek, Claude, GPT, Gemini, Grok, Qwen (Free)

Model family	Vendor	MCP support	In Playground	Run it
Claude	Anthropic	Native — the API accepts MCP servers directly	9 models	Test with Claude →
GPT	OpenAI	Via an MCP client (tool-calling translation)	13 models	Test with GPT →
Gemini & Gemma	Google	Via an MCP client (tool-calling translation)	7 models	Test with Gemini & Gemma →
Grok	xAI	Via an MCP client (tool-calling translation)	5 models	Test with Grok →
DeepSeek	DeepSeek	Via an MCP client (tool-calling translation)	3 models	Test with DeepSeek →
Qwen	Alibaba	Via an MCP client (tool-calling translation)	9 models	Test with Qwen →
GLM	Z.AI	Via an MCP client (tool-calling translation)	5 models	Test with GLM →
Kimi	Moonshot AI	Via an MCP client (tool-calling translation)	5 models	Test with Kimi →
MiniMax	MiniMax	Via an MCP client (tool-calling translation)	3 models	Test with MiniMax →
Nemotron	NVIDIA	Via an MCP client (tool-calling translation)	3 models	Test with Nemotron →
Mistral	Mistral AI	Via an MCP client (tool-calling translation)	1 models	Test with Mistral →

Pick a model to test with

Claude · Anthropic · 9 models

Anthropic authored MCP, and the Claude API is the only one that speaks it natively rather than through a tool-calling shim. Strongest default for long multi-tool chains.

Test your server with Claude →Claude Opus 5 + MCP servers

GPT · OpenAI · 13 models

Reaches MCP through the Agents SDK and ChatGPT connectors. Broadest ecosystem support; tool-selection behaviour differs noticeably from Claude on identical schemas.

Test your server with GPT →Testing MCP with ChatGPT & the OpenAI MCP tool

Gemini & Gemma · Google · 7 models

Large context windows make Gemini forgiving of big tool lists, which is exactly when tool-selection drift is hardest to spot without a side-by-side run.

Test your server with Gemini & Gemma →Testing MCP with Gemini models

Grok · xAI · 5 models

Fast and cheap per call, including a multi-agent variant worth testing against parallel tool use.

Test your server with Grok →Testing MCP with Grok (xAI)

DeepSeek · DeepSeek · 3 models

The cheapest credible tool-caller here. Worth benchmarking before paying for a frontier model on a routine workflow.

Test your server with DeepSeek →Connecting your MCP server to DeepSeek V4

Qwen · Alibaba · 9 models

The widest spread of sizes on this list, from Flash up to Max — useful for finding the smallest model that still calls your tools correctly.

Test your server with Qwen →Testing MCP with Alibaba Qwen models

GLM · Z.AI · 5 models

Several GLM builds are free on the router, which makes them a cheap first pass when you just need to know whether your tools are callable at all.

Test your server with GLM →Testing MCP with Z.AI GLM models

Kimi · Moonshot AI · 5 models

Strong on long agentic runs; the Code variant is the one to try against developer-tool MCP servers.

Test your server with Kimi →Testing MCP with Kimi K2.6

MiniMax · MiniMax · 3 models

A recent entrant to agentic tool use — thin third-party benchmarking so far, which is a reason to test rather than trust a leaderboard.

Test your server with MiniMax →Connecting your MCP server to MiniMax M3

Nemotron · NVIDIA · 3 models

Open-weight models including free-on-router builds. The realistic option if you need to self-host the model behind your MCP server later.

Test your server with Nemotron →

Mistral · Mistral AI · 1 models

European-hosted option; useful where data residency decides the model before capability does.

Test your server with Mistral →

Why the same MCP server behaves differently per model

Only Claude accepts MCP servers directly at the API level. Every other provider reaches your tools through an MCP client that translates your tool definitions into that provider's own function-calling format. Two things fall out of that:

Schema handling differs. Deeply nested arguments survive the translation unevenly — this is where -32602 Invalid params tends to appear on one model and not another.
Tool selection is a judgement, not a lookup. With several plausible tools, models disagree about which one the user meant. Vague descriptions make it worse.

You can reproduce both on demand: the complex-schema mock server exposes four tools with overlapping argument shapes specifically to provoke tool-selection drift.

Per-model guides

Frequently asked questions

Which AI models can I test my MCP server with?

Any model that supports tool calling can drive an MCP server — the protocol sits between the client and the server, not inside the model. In practice that means Claude, GPT, Gemini, Grok, DeepSeek, Qwen, GLM, Kimi, MiniMax, Nemotron and Mistral all work. What differs is how reliably each one picks the right tool and shapes the arguments. MCP Playground runs 63 models against any MCP server so you can see that difference rather than guess at it.

Does Claude support MCP natively?

Yes, and it is the only one that does. Anthropic created MCP, and the Claude API accepts MCP server definitions directly. Every other model reaches MCP through a client that translates tool definitions into that provider's own function-calling format — which is why identical tools can be called differently by different models.

Do Gemini, Grok, DeepSeek and Qwen support MCP?

All of them can call MCP tools through an MCP client, and all are available in MCP Playground. None of them speak MCP natively at the API level the way Claude does; the client does the translation. That translation layer is exactly where behaviour diverges, so testing your own server against each model is more informative than reading a support matrix.

Which model is best for MCP tool calling?

It depends on the shape of your tools, and the honest answer is that you should measure it on your own server. Claude tends to lead on long multi-tool chains; GPT has the broadest ecosystem; DeepSeek and GLM are dramatically cheaper and often sufficient. The per-model guides linked on this page go into each, and the model comparison runs the same prompt across several at once.

Can I test one MCP server against several models at once?

Yes. The model comparison runs the same prompt and the same MCP server against multiple models side by side, so you can see which ones call the right tool with the right arguments. It needs a free account because each run costs credits; the per-model guides and this page do not.

Why do different models call my MCP tools differently?

Because everything except Claude reaches your tools through a translation layer, and because tool selection is a judgement the model makes from your names, descriptions and schemas. Ambiguous descriptions, overlapping tools, and deeply nested arguments are the usual causes. The complex-schema mock server exists to reproduce exactly that failure on demand.