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io.github.AceDataCloud/mcp-glm

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by AceDataCloud

glm_chat_completions

Send messages to Zhipu GLM models through AceDataCloud to generate chat completions for conversational AI, Chinese language understanding, and reasoning tasks.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoHow many chat completion choices to generate. Default 1.
seedNoRandom seed for deterministic sampling.
stopNoStop sequences where the API will stop generating tokens.
userNoEnd-user identifier for abuse monitoring.
audioNoParameters for audio output.
modelNoThe GLM model to use. Options: glm-5.3, glm-5.2, glm-5, glm-5-turbo, glm-5.1, glm-4.7, glm-4.6, glm-3-turbo. Default is glm-5.2.glm-5.2
storeNoWhether to store the output of this request. Default False.
toolsNoList of tools the model may call.
top_pNoNucleus sampling probability mass. Default 1.
streamNoWhether to stream partial message deltas. Default False.
logprobsNoWhether to return log probabilities of output tokens. Default False.
messagesYesConversation messages. Each message is a dict with 'role' and 'content' keys. Required.
metadataNoKey-value pairs for storing additional information.
logit_biasNoModify the likelihood of specified tokens appearing in the completion.
max_tokensNoMaximum number of tokens to generate.
modalitiesNoOutput types to generate (e.g. ['text', 'audio']).
predictionNoStatic predicted output content for latency reduction.
temperatureNoSampling temperature between 0 and 2. Higher = more random. Default 1.
tool_choiceNoControls which tool is called. Can be 'none', 'auto', or a dict.
service_tierNoService tier: auto, default, flex, scale, priority. Default auto.
top_logprobsNoNumber of most likely tokens to return at each token position.
stream_optionsNoOptions for streaming response.
response_formatNoResponse format specification (e.g. {"type": "json_object"}).
presence_penaltyNoPresence penalty between -2.0 and 2.0. Positive values increase topic variety. Default 0.
reasoning_effortNoReasoning effort level: minimal, low, medium, high. Default medium.
frequency_penaltyNoFrequency penalty between -2.0 and 2.0. Positive values decrease repetition. Default 0.
web_search_optionsNoOptions for web search tool.
parallel_tool_callsNoEnable parallel function calling. Default True.
max_completion_tokensNoUpper bound for tokens that can be generated for a completion.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral disclosure burden. It states that messages are sent to the model and a JSON response is returned, which covers the basic operation. However, it omits details like authentication needs, rate limits, streaming behavior, or error semantics, leaving gaps for a 29-parameter API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well organized with a purpose statement, use-case bullets, and a returns section. However, some content is redundant: the opening sentence and second sentence overlap, the model list repeats the schema enum, and the first 'Use this when' bullet restates the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the large parameter count, the schema fully documents parameters and an output schema exists, so the description does not need to explain return values in depth. It provides purpose, model scope, and usage scenarios. It could be more complete on operational constraints, but it covers the main decisions an agent needs to make.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter already has documentation. The description adds the model list, but that duplicates the model enum in the schema. It does not meaningfully enrich parameter understanding beyond what the schema already provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Create a GLM chat completion using the AceDataCloud GLM API.' It then explains the core action (sending messages and returning generated responses) and clearly separates this tool from siblings like glm_list_models and glm_get_usage_guide.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Use this when' section gives concrete triggers: chatting with Zhipu GLM, Chinese language tasks, and reasoning capabilities. It does not explicitly name sibling alternatives or exclusion criteria, but the context is clear enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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