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AceDataCloud

AiChat MCP Server

Official
by AceDataCloud

aichat_create_conversation

Create an AI conversation by sending a question to models like GPT-4, DeepSeek, Grok, or GLM, and get the answer. Provide a conversation ID to continue an existing dialogue.

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
Behavior4/5

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

The description explains that the tool sends a question to an AI model and returns a conversation ID and answer. It also mentions statefulness in parameter descriptions. However, since no annotations are provided, the description could better disclose side effects like cost or rate limits, but overall the core behavior is clear.

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

Conciseness5/5

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

The description is very concise, with three short paragraphs that front-load the purpose, followed by usage scenarios and return values. Every sentence adds value without redundancy.

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 tool's 6 parameters and output schema, the description covers the main use cases and return format. However, it lacks error handling details and does not differentiate from the v2 sibling, which could leave an agent uncertain about which version to use.

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 baseline is 3. The description adds context by listing model families and reiterating the purpose of conversation_id, but does not significantly enhance understanding beyond what the 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, and returns an answer. It mentions supported models, but does not differentiate from the sibling tool aichat_create_conversation_v2, which is a potential point of confusion.

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 lists three clear scenarios: asking a question, continuing an existing conversation, and using specific models. However, it does not provide explicit guidance on when not to use this tool or mention alternatives like aichat_list_models for listing models.

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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