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io.github.AceDataCloud/mcp-glm

io.github.AceDataCloud/mcp-glm

Official
by AceDataCloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
ACEDATACLOUD_API_TOKENYesYour 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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
glm_chat_completionsB

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

NameDescription
glm_guideGuide for choosing the right GLM tool and model for chat completion tasks.
glm_workflow_examplesCommon workflow examples for GLM tasks.

Resources

Contextual data attached and managed by the client

NameDescription

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

A3.6/5.0

Scored across 3 tools

Disambiguation4/5

The three tools serve clearly different purposes: generating a completion, listing models, and reading documentation. The only mild overlap is between the usage-guide tool and the rich descriptions already embedded in the other two tools, which could make an agent ask 'do I need the guide first?'

Naming Consistency4/5

All tools share a consistent glm_ prefix, and two of three follow a verb_noun pattern (glm_get_usage_guide, glm_list_models). glm_chat_completions breaks the verb-first convention by using a resource path name, but it remains readable and predictable.

Tool Count3/5

Three tools is on the thin side for an API wrapper surface, and one of them (glm_get_usage_guide) is essentially packaged documentation rather than an operational capability. The count is defensible for a narrow single-endpoint wrapper but feels minimal.

Completeness3/5

Chat completion plus model listing covers the core workflow, but there is no embeddings, no streaming/tokenization, and no non-chat GLM endpoint, so agents needing anything beyond chat hit a dead end. The surface is workable but notably incomplete for a 'GLM API' server.

Maintenance

ActivityActive
ResponsivenessOutdated data

Resources