What tools does DeepSeek MCP Server expose?
It provides `chat_completion`, `completion`, `list_models`, `get_user_balance`, `reset_conversation`, and `list_conversations`. Only documented DeepSeek V4 API endpoints are supported.
@DMontgomery40
基本信息
Model Context Protocol server for DeepSeek's advanced language models
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"deepseek-mcp-server": {
"command": "npx",
"args": [
"-y",
"deepseek-mcp-server"
],
"env": {
"DEEPSEEK_API_KEY": "REPLACE_WITH_DEEPSEEK_KEY"
}
}
}
}Primary DeepSeek V4 chat tool for single-turn and multi-turn generation. Defaults to `deepseek-v4-flash`; use `deepseek-v4-pro` for higher-capability reasoning. Provide either `message` (simple single user turn) or `messages` (full chat history); if both are provided, `messages` is used. Thinking mode is enabled by DeepSeek by default; pass `thinking:{type:"disabled"}` for non-thinking mode, and use `reasoning_effort:"low"|"high"|"max"` when thinking is enabled. Use `conversation_id` to persist context across calls and `clear_conversation=true` to reset stored state before sending the next turn. Set `include_raw_response=true` only for debugging because it returns the full provider payload.
DeepSeek V4 Pro FIM completion tool for prompt/suffix fill-in-the-middle workflows. Defaults to `deepseek-v4-pro`. Use this when you need raw completion text instead of chat message formatting. Set `include_raw_response=true` only when you need the full provider payload for debugging.
Create a stateless DeepSeek V4 response using the native OpenAI-compatible Responses API. Defaults to `deepseek-v4-flash`. Provide `input`, `instructions`, or both. Use `reasoning.effort` for thinking control, `tools` for function or server-side web-search tools, and `stream=true` for semantic SSE aggregation. This tool does not persist provider-side response state; send the full input history for multi-turn work. Set `include_raw_response=true` only for debugging because it returns the full provider payload.
List available DeepSeek models for model selection and validation. This tool takes no parameters. Use it before passing an explicit model ID to generation tools.
Return the current DeepSeek account balance and availability status. This tool takes no parameters and is read-only. Use it for account health checks when diagnosing provider-side failures.
Delete stored in-memory chat history for a `conversation_id`. Use this when you want to keep the same ID but start a fresh thread. This only affects server-side memory in the current MCP process.
List all conversation IDs currently stored in this MCP process memory. This tool takes no parameters and does not call the DeepSeek API. Useful for debugging conversation persistence behavior.
A Model Context Protocol server that exposes DeepSeek’s V4 API (chat completions, FIM completions, model listing, account balance) as MCP tools. Designed for developers using MCP-compatible hosts like Claude Code, Cursor, or Codex CLI.
Run locally via npx deepseek-mcp-server or Docker, setting the DEEPSEEK_API_KEY environment variable. Alternatively, connect to the hosted remote at https://deepseek-mcp.ragweld.com/mcp using a bearer token. Transport is stdio by default; HTTP can be configured via environment variables.
deepseek-v4-flash and deepseek-v4-prodeepseek-v4-proIt provides `chat_completion`, `completion`, `list_models`, `get_user_balance`, `reset_conversation`, and `list_conversations`. Only documented DeepSeek V4 API endpoints are supported.
Set the environment variable `DEEPSEEK_MCP_AUTH_TOKEN` and pass it as a bearer token in the `Authorization` header. For local usage, set `DEEPSEEK_API_KEY`.
Requires Node.js (to run via npx) and the `DEEPSEEK_API_KEY` environment variable. Optional variables include `DEEPSEEK_BASE_URL`, `DEEPSEEK_REQUEST_TIMEOUT_MS`, `MCP_TRANSPORT`, and `CONVERSATION_MAX_MESSAGES`.
Conversation history is held in memory on the server. Use `reset_conversation` or `list_conversations` to manage it.
No. The server explicitly states it does not ship speculative image, video, or upload tools, nor automatic model substitution. Only documented chat, FIM, model listing, and balance endpoints are exposed.

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