Schema | io.github.AceDataCloud/mcp-glm

io.github.AceDataCloud/mcp-glm

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by AceDataCloud

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Python

Remote

Server Configuration

Describes the environment variables required to run the server.

Name	Required	Description	Default
`ACEDATACLOUD_API_TOKEN`	Yes	Your AceDataCloud API token. Get from https://platform.acedata.cloud

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{ "listChanged": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Tools

Functions exposed to the LLM to take actions

Name Description

Name	Description
glm_chat_completionsA	Create a GLM chat completion using the AceDataCloud GLM API. `Sends messages to the specified GLM model and returns the generated response. Supports all GLM models: glm-5.3, glm-5.2, glm-5, glm-5-turbo, glm-5.1, glm-4.7, glm-4.6, glm-3-turbo. Use this when: - You need to chat with a Zhipu GLM model - You need Chinese language understanding or generation - You want to use GLM's reasoning capabilities Returns: JSON response containing the chat completion result.`
glm_list_modelsA	List all available GLM models for the GLM API. `Returns a list of supported GLM models with descriptions. Returns: Formatted list of available GLM models.`
glm_get_usage_guideA	Get a comprehensive guide for using the GLM tools. `Provides detailed information on how to use the GLM tools effectively, including parameters, examples, and best practices. Returns: Complete usage guide for GLM tools.`

glm_chat_completionsA

Create a GLM chat completion using the AceDataCloud GLM API.

Sends messages to the specified GLM model and returns the generated response.
Supports all GLM models: glm-5.3, glm-5.2, glm-5, glm-5-turbo, glm-5.1, glm-4.7, glm-4.6, glm-3-turbo.

Use this when:
- You need to chat with a Zhipu GLM model
- You need Chinese language understanding or generation
- You want to use GLM's reasoning capabilities

Returns:
    JSON response containing the chat completion result.

glm_list_modelsA

List all available GLM models for the GLM API.

Returns a list of supported GLM models with descriptions.

Returns:
    Formatted list of available GLM models.

glm_get_usage_guideA

Get a comprehensive guide for using the GLM tools.

Provides detailed information on how to use the GLM tools effectively,
including parameters, examples, and best practices.

Returns:
    Complete usage guide for GLM tools.

Prompts

Interactive templates invoked by user choice

Name	Description
`glm_guide`	Guide for choosing the right GLM tool and model for chat completion tasks.
`glm_workflow_examples`	Common workflow examples for GLM tasks.

Resources

Contextual data attached and managed by the client

Name	Description
No resources

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored 2026-08-31 10:10 across 3 tools

Disambiguation5/5

Each tool serves a clearly distinct purpose: usage guidance, chat completion generation, and model listing. There is no overlap or ambiguity between them, so an agent would never struggle to choose the right one.

Naming Consistency5/5

All tool names use the same glm_ prefix and a consistent lower_snake_case style. The verb-noun pattern is uniform (get_usage_guide, chat_completions, list_models), making the set predictable and easy to navigate.

Tool Count5/5

Three tools is well-scoped for a focused GLM chat completions server. Each tool earns its place, and the set feels neither bloated nor too thin.

Completeness4/5

The server covers the core lifecycle of chat completion: listing models and making completions, plus an onboarding guide. It lacks endpoints for embeddings or model-specific detail, but those are likely outside the intended scope.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

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