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Test Your MCP Server With GLM

Free9 GLM models

Paste a server URL and watch Z.AI’s GLM models call your tools. Several GLM builds run free on the router, so this is the cheapest first pass on a server you have just deployed.

Paste a server URL, pick a model, and watch it call your tools in a real conversation. No install.

No server of your own? Leave it blank and use one of the public mock servers.

In your browser

MCP Agent Studio

Server has a public URL? Chat with GLM on it, watch every tool call, and compare models side by side.

Open MCP Agent Studio →

On your computer

MCP Playground Desktop

Server runs on localhost or over stdio? The browser can’t reach it. The desktop app can, with GLM in Agent Studio. Download free

Mac (Apple Silicon)Mac (Intel)Windowsor Microsoft Store

Why test with GLM?

GLM’s value here is economic rather than technical: GLM 5.1 and GLM 5 Turbo are free on the router, so a smoke test costs nothing. That makes GLM the right first model after a deploy, when the question is simply "does anything call my tools correctly?" rather than "which model is best". If GLM can drive your server end to end, your schemas are sound and any failure on a stronger model is about judgement, not structure.

How GLM reaches MCP tools

  • An MCP client translating tool definitions into GLM’s function-calling format — no key needed here
  • Z.AI’s hosted API for production use
  • OpenAI-compatible routing, which most existing MCP client code can target directly

GLM's conversion is standard OpenAI-style function calling; what makes it distinctive is how little slack the smaller builds leave you. Frontier models infer intent from a mediocre description. GLM largely does not, so the translation is effectively lossless and your descriptions carry the full weight of tool selection.

What goes wrong with GLM specifically

  • The free-on-router builds are not the strongest GLM variants. A failure on GLM 5 Turbo is not proof your server is broken — re-run on GLM 5.2 before concluding anything.
  • GLM is more sensitive than frontier models to tool descriptions that describe implementation rather than intent. "Queries the orders table" selects worse than "finds a customer’s past orders".
  • The vision-capable Turbo variant behaves differently on tool selection than the text builds; pick deliberately.

Run the complex-schema mock on the free GLM 5 Turbo build first. If it picks the wrong tool among four, your descriptions are ambiguous — that is a finding about your server, not about GLM. All the mock servers →

GLM models available here

ModelModel IDCredits / run
GLM 5.3z-ai/glm-5.34
GLM 5.3 Primez-ai/glm-5.3-prime8
GLM 5.3 Flashz-ai/glm-5.3-flash2
GLM 5.3 FlashXz-ai/glm-5.3-flashx2
GLM 5V Turboz-ai/glm-5v-turbo6
GLM 5.2z-ai/glm-5.24
GLM 5.1z-ai/glm-5.12
GLM 5 Turboz-ai/glm-5-turbo2
GLM 4.7 Flashz-ai/glm-4.7-flash2

Go deeper

Test with another model

Frequently asked questions

Does GLM support MCP?
Yes, through an MCP client that translates your tools into GLM’s function-calling format. No changes to your server are required — paste its URL and GLM will call the tools it exposes.
Is GLM free to test MCP with?
Several GLM builds — GLM 5.1 and GLM 5 Turbo among them — are free on the router, which is why GLM is the cheapest model here for a first smoke test. Stronger builds like GLM 5.2 cost a small number of credits.
My server works with Claude but fails with GLM. Which is wrong?
Usually neither. Smaller models lean harder on your tool descriptions, so ambiguity that Claude reasons past will trip GLM. That is useful signal: descriptions that only work on frontier models are fragile. Rewrite them to describe intent rather than implementation and re-run.
Which GLM model should I use for real work?
Test on the free builds, ship on GLM 5.2 or the Turbo variant depending on whether you need vision. Whatever you plan to deploy is the one worth benchmarking against your actual schemas.