MCP AiChat Server

A Model Context Protocol (MCP) server for AI dialogue via the AceDataCloud platform. Supports a wide range of models including GPT-4/5, o-series, DeepSeek, Grok, and GLM.

Ace Data Cloud in goose

Named provider preset and upstream acceptance status. This independently installed preset is not an official built-in provider.

Features

• Multi-model support: GPT-4.1, GPT-4o, GPT-5, o1, o3, o4-mini, DeepSeek, Grok, GLM, and more
• Multi-turn conversations: Continue conversations using conversation IDs
• Stateful mode: Optional server-side conversation state management
• Reference sources: Include external references for context-aware responses

Connect: hosted OAuth, API token, or local stdio

The hosted endpoint is https://aichat.mcp.acedata.cloud/mcp. Choose one route for the MCP client:

The hosted service advertises OAuth metadata and Dynamic Client Registration (DCR). DCR registers the client application; it is not an API key. OAuth signs you in and the client sends the resulting Bearer token; it may reuse or create an API credential for the account. Browser sign-in still requires an AceDataCloud account. The hosted service can be metered: review current service documentation and displayed pricing before a real operation. Do not configure both an OAuth login and a fixed Authorization header for the same server.

Hosted OAuth examples

• Claude and Claude Desktop chat: Add a remote custom connector in Customize → Connectors → Add custom connector, enter https://aichat.mcp.acedata.cloud/mcp, select sign-in, and choose Register automatically if Claude asks how to register its OAuth client. Complete consent. Claude Desktop's local claude_desktop_config.json is a separate setup. Claude connector guide.
• Claude Code: claude mcp add --transport http --scope user aichat https://aichat.mcp.acedata.cloud/mcp, then claude mcp login aichat. Check /mcp. Claude Code MCP guide.
• Cursor: Add a remote server with only https://aichat.mcp.acedata.cloud/mcp. For a project, merge the entry below into <project>/.cursor/mcp.json; for personal use, use ~/.cursor/mcp.json. Cursor MCP guide.
• VS Code / Copilot: Run MCP: Add Server, select HTTP, enter https://aichat.mcp.acedata.cloud/mcp, then finish the browser sign-in. New portable workspace configs use <project>/.mcp.json; the VS Code-specific format below uses <project>/.vscode/mcp.json or the user profile. Check MCP: List Servers. VS Code MCP setup.
• Codex: codex mcp add aichat --url https://aichat.mcp.acedata.cloud/mcp, then codex mcp login aichat. Its user settings are in ~/.codex/config.toml. Official Codex MCP guide.

Cursor project config (OAuth):

{
  "mcpServers": {
    "aichat": {"url": "https://aichat.mcp.acedata.cloud/mcp"}
  }
}

VS Code-specific workspace config (OAuth):

{
  "servers": {
    "aichat": {"type": "http", "url": "https://aichat.mcp.acedata.cloud/mcp"}
  }
}

Hosted API token

1. Open Ace Data Cloud → Applications. Sign in, or register an account first.
2. Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.
3. Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
4. When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
5. Copy only the token value, without quotation marks, spaces, or Bearer . An API Key / API Token field expects this raw value; only a manually configured HTTP Authorization header needs the Bearer prefix. A platform-... account-management token cannot replace a generation API key.

Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.

A fixed Bearer header bypasses OAuth. If the header is invalid, remove it before trying browser sign-in; keep the saved credential private.

For Claude Code, the shell expands the token when you add the server; treat the saved user MCP config as a secret:

export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
claude mcp add --transport http --scope user aichat https://aichat.mcp.acedata.cloud/mcp \
  --header "Authorization: Bearer $ACEDATACLOUD_API_TOKEN"

For a Claude Code project config, put a variable reference in <project>/.mcp.json and set that variable in the environment that launches Claude Code:

{
  "mcpServers": {
    "aichat": {
      "type": "http",
      "url": "https://aichat.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"}
    }
  }
}

Cursor uses a different environment-variable syntax in ~/.cursor/mcp.json or an uncommitted project config:

{
  "mcpServers": {
    "aichat": {
      "url": "https://aichat.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${env:ACEDATACLOUD_API_TOKEN}"}
    }
  }
}

In VS Code, run MCP: Open User Configuration and merge this server plus its masked input; ${input:...} is for VS Code's user/workspace format and is not portable to the Agent Host .mcp.json format:

{
  "inputs": [
    {"id": "acedata-aichat-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
  ],
  "servers": {
    "aichat": {
      "type": "http",
      "url": "https://aichat.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${input:acedata-aichat-token}"}
    }
  }
}

For Cline, use its MCP configuration UI or CLI file ~/.cline/data/settings/cline_mcp_settings.json; its remote transport value is streamableHttp. For JetBrains AI Assistant, add a remote URL from Settings → Tools → AI Assistant → Model Context Protocol (MCP). For Zed, use a context_servers entry with the URL only for OAuth or add a local Bearer header. These clients have different configuration schemas; follow their current UI rather than copying another client's JSON. Cline · JetBrains · Zed.

Local stdio

Install the package and give the local process an API token:

python -m pip install mcp-aichat
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-aichat

For Claude Desktop local MCP, merge this entry into the file opened by its developer settings (~/Library/Application Support/Claude/claude_desktop_config.json on macOS). uvx requires uv on PATH:

{
  "mcpServers": {
    "aichat": {
      "command": "uvx",
      "args": ["mcp-aichat"],
      "env": {"ACEDATACLOUD_API_TOKEN": "YOUR_API_TOKEN"}
    }
  }
}

Keep this user-level file private. Self-hosted HTTP uses mcp-aichat --transport http --port 8000; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.

Check before using the service

1. https://aichat.mcp.acedata.cloud/health returning {"status":"ok"} checks endpoint reachability only.
2. Confirm that the MCP client loads tools. aichat_list_models is a reference tool; it does not verify downstream API access or balance.
3. If you need a full API check, call aichat_create_conversation with your own valid input after reviewing current service documentation and displayed pricing.

For 401, check which auth route the client used and whether the token or OAuth session is valid. A 403 may mean an account permission or content moderation failure; read the returned error. Insufficient balance and downstream service failures need their own diagnosis. A listed tool or submitted task does not prove a successful result.

Available Tools

AiChat v2 agent iterations

aichat_create_conversation_v2 accepts max_turns from 1 to 500. Omit it to use the Backend default of 500. This limits agent iterations for one request, not conversation-history length; max_turns=1 prevents tool calls. Each model call is billed by actual usage, and execution may end earlier due to timeouts, authorization or other execution conditions.

Supported Models

OpenAI

• GPT-5.6 Sol Fast: gpt-5.6-sol-fast, supported by both aichat_create_conversation and aichat_create_conversation_v2. Existing defaults are unchanged.
• GPT-5 series: gpt-5.5, gpt-5.4, gpt-5.2, gpt-5.1, gpt-5, gpt-5-mini, gpt-5-nano
• GPT-4.1 series: gpt-4.1, gpt-4.1-mini, gpt-4.1-nano
• GPT-4o series: gpt-4o, gpt-4o-mini, gpt-4o-all, gpt-4o-image
• GPT-4 series: gpt-4, gpt-4-turbo, gpt-4-vision-preview
• o-series: o1, o1-mini, o1-pro, o3, o3-mini, o3-pro, o4-mini

DeepSeek

• deepseek-r1, deepseek-r1-0528, deepseek-v3, deepseek-v3-250324, deepseek-v4-flash, deepseek-v4-pro

xAI

• grok-3

Zhipu AI

• glm-5.1, glm-4.7, glm-4.6, glm-3-turbo

Kimi (AiChat v2 only)

Use aichat_create_conversation_v2 with model="kimi-k3.1-pro", the recommended Kimi model. kimi-k3 and existing v2 Kimi aliases remain supported; the default model is still gpt-4.1. These models are not available through the v1 tool.

{"model":"kimi-k3.1-pro","question":"Review this code and identify its key risks."}

Pass these arguments to aichat_create_conversation_v2. The tool calls /aichat2/conversations, not /kimi/chat/completions, and does not expose the latter endpoint's reasoning_effort control.

Documentation

Documentation

License

MIT