chat_completion
Primary DeepSeek V4 chat tool for single-turn and multi-turn generation. Defaults to
This record as markdown: /tools/deepseek-mcp-server/chat-completion.md
What chat_completion does on DeepSeek MCP Server
AI agents invoke chat_completion to trigger actions in DeepSeek MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why chat_completion is rated High
This tool sends requests to an external AI service (DeepSeek) and executes model inference, producing generated content. It is not a simple read/query of stored data, nor does it write/modify persistent data — it executes an external operation whose outputs depend on the input arguments.
From the tool's definition 'chat_completion' described as 'Primary DeepSeek V4 chat tool for single-turn and multi-turn generation' — triggers external AI model inference and generation operations
Attacks that exploit this kind of access
The rule that runs chat_completion safely
PolicyLayer is an MCP gateway: it sits between your AI agents and DeepSeek MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For chat_completion, this is the rule to start with:
chat_completion stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect DeepSeek MCP Server, apply this rule, and every chat_completion call is checked against it from then on.
Questions about chat_completion
Primary DeepSeek V4 chat tool for single-turn and multi-turn generation. Defaults to. It is categorised as a Execute tool in the DeepSeek MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the DeepSeek MCP Server MCP server in PolicyLayer and add a rule for chat_completion: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches DeepSeek MCP Server. Nothing to install.
chat_completion is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the chat_completion rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for chat_completion. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
chat_completion is provided by the DeepSeek MCP Server MCP server (deepseek-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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