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AceDataCloud

AiChat MCP Server

Official
by AceDataCloud

aichat_create_conversation

Ask an AI model a question and receive its generated answer, while optionally passing a conversation ID to continue prior context. Works with models like GPT, DeepSeek, Grok, and GLM.

Instructions

Create an AI conversation using the AiChat API.

Sends a question to the specified AI model and returns the generated answer.
Supports a wide range of models including GPT-4, GPT-5, o-series, DeepSeek, Grok, and GLM.

Use this when:
- You need to ask a question to an AI model
- You want to continue an existing conversation (provide conversation_id)
- You need answers from specific AI models like DeepSeek, Grok, or GLM

Returns:
    JSON response containing the conversation ID and the generated answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoThe model to use for generating the answer. Options include gpt-4.1, gpt-4o, gpt-5, o1, o3, o4-mini, deepseek-r1, deepseek-v3, grok-3, glm-4.7, and many more. Default is gpt-4.1.gpt-4.1
presetNoAn optional preset model configuration to apply for this conversation.
questionYesThe prompt or question to be answered by the AI model. Required.
statefulNoWhether to use stateful conversation mode. When True, the server tracks conversation history. Default is False (stateless).
referencesNoOptional list of reference sources or context to include when generating the answer.
conversation_idNoThe unique identifier of an existing conversation to continue. If provided, the AI will respond in the context of the prior conversation. Leave empty to start a new conversation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the disclosure burden. It states that the tool sends a question, returns an answer, and can continue a conversation via conversation_id, plus the JSON response shape. It does not disclose side effects, cost/auth implications, or error behavior, though some stateful semantics are covered in the schema.

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

Conciseness4/5

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

The description is well-structured, front-loaded with the core purpose, and uses clear bullets for usage guidance. It is slightly repetitive in places, such as 'sends a question' followed by 'you need to ask a question to an AI model,' but overall it remains appropriately sized and scannable.

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?

With a rich input schema, full parameter descriptions, and an output schema, the description is largely complete for basic invocation. It covers primary use cases and return shape, but it could be more complete by addressing how to choose between this tool and aichat_create_conversation_v2.

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 the schema already documents all six parameters well. The description adds model family examples and conversation-continuation context, but it does not add substantive parameter meaning beyond what the input schema already provides.

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

Purpose4/5

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

The description clearly states that the tool creates an AI conversation, sends a question to a model, and returns the generated answer. It distinguishes itself from aichat_list_models and aichat_get_usage_guide, but it does not differentiate itself from the sibling tool aichat_create_conversation_v2.

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 description provides explicit 'Use this when' bullets covering asking a question, continuing an existing conversation, and requesting specific model families. However, it does not mention when to avoid this tool or when aichat_create_conversation_v2 would be a better choice, so it lacks explicit alternatives.

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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