Skip to main content
Glama
deenesjakoruzh
kira4094

DeepSeek Subagent MCP Server

by kira4094
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

DeepSeek Subagent MCP Server

一个 MCP 服务器，可让 Codex（或任意 MCP 客户端）将子任务委托给 DeepSeek 作为子代理。 单一工具 deepseek_task：给它一个任务（＋可选上下文），返回 DeepSeek 的最终回答。

功能

  • 🧠 单一工具 deepseek_task — 委托独立工作（研究/起草/审查/重构/分析）

  • 💰 默认使用 deepseek-v4-flash（高性价比），可通过环境变量切换到 deepseek-v4-pro

  • 🔍 将 reasoning_content（V4 思考模式）以 <thinking> 块形式呈现

  • ⚡ 无非 MCP 依赖，一行 npx 命令即可部署

Related MCP server: deepseek-subagent-mcp

要求

安装与运行

cd deepseek-subagent-mcp-server
npm install
npm start

环境变量

变量

必填

默认值

说明

DEEPSEEK_API_KEY

✅

—

你的 DeepSeek API 密钥。

DEEPSEEK_MODEL

deepseek-v4-flash

模型。也可设为 deepseek-v4-pro。

DEEPSEEK_BASE_URL

https://api.deepseek.com

覆盖 API 端点（适用于代理）。

DEEPSEEK_MAX_TOKENS

8192

默认最大输出 token 数。

Codex 配置

添加到 Codex 的 MCP 配置中：

{
  "mcpServers": {
    "deepseek-subagent": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "deepseek-subagent-mcp-server"],
      "env": {
        "DEEPSEEK_API_KEY": "your-deepseek-api-key",
        "DEEPSEEK_MODEL": "deepseek-v4-flash"
      }
    }
  }
}

或从本地 checkout 运行：

{
  "mcpServers": {
    "deepseek-subagent": {
      "command": "node",
      "args": ["D:\\GitHub\\MCP\\deepseek-subagent-mcp-server\\src\\index.js"],
      "env": {
        "DEEPSEEK_API_KEY": "your-key-here"
      }
    }
  }
}

工具：deepseek_task

参数

类型

必填

说明

task

string

✅

交给 DeepSeek 的子任务。请具体说明要做什么以及输出格式。

context

string

可选的上下文资料（代码、笔记、代码片段）。

system

string

可选的自定义系统提示词（默认为子代理身份）。

max_tokens

number

最大输出 token 数。默认 8192。

temperature

number

0-2。省略时使用模型默认值。

使用示例

# Delegate a code review
deepseek_task(task="Review this function for bugs and edge cases", context="<code>...")

# Delegate a research task
deepseek_task(task="Summarize the key risks of a subscription pricing model")

相关项目

许可证

MIT

Available Tools

1 tool
deepseek_taskA

Delegate a sub-task to DeepSeek as a sub-agent and get back its result. Use this for work that can be done independently: research, drafting, code review, refactoring, analysis, writing, or any self-contained task that shouldn't pollute the main conversation. The caller (Codex) supplies a task description and optional context; DeepSeek completes the task in one shot and returns the final answer (with reasoning, if produced). Default model: deepseek-v4-flash. Override via DEEPSEEK_MODEL env (e.g. DeepSeek-V4-Pro-0813).

ParametersJSON Schema
NameRequiredDescriptionDefault
taskYesThe sub-task for DeepSeek to complete. Be specific: what to do, what format the result should take.
systemNoOptional custom system prompt overriding the default subagent persona.
contextNoOptional context material the sub-agent should consider (code snippets, file contents, notes).
max_tokensNoMaximum output tokens.
temperatureNoSampling temperature (0-2). Omit to use the model default.
reasoning_effortNoThinking effort for DeepSeek V4 reasoning mode. Omit to use the model default.

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden of explaining behavior. It does this well: it clarifies the one-shot execution model, the return style ('returns the final answer (with reasoning, if produced)'), and the default/environment-configurable model. It doesn't cover failure modes or side effects, but the behavior of a delegation tool is well-scoped.

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 modestly long but every sentence carries weight: the first defines the operation and result, the second clarifies the use cases and scope, and the third provides model configuration with an override example. It is structured and front-loaded, without filler.

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?

The tool is a one-shot delegation with no output schema and no siblings. The description adequately conveys its oracle-style behavior, expected result, and the extent of inputs. It could give example output shape or failure handling, but not that could be unique; the essentials are present.

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 coverage is 100%, so all parameters are already documented. The description restates some parameter-level guidance ('supplies a task description and optional context') but adds no new meaning beyond the schema. The mention of the env-var model override is useful but relates to configuration rather than the input parameters. Baseline 3 is appropriate.

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

Purpose5/5

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

The description uses a specific verb ('Delegate'), a clear resource ('sub-task to DeepSeek as a sub-agent'), and explicitly frames the return value ('get back its result'). It further distinguishes the tool by noting the work is done independently as a sub-agent without polluting the main conversation — making the purpose unambiguous.

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 a clear list of when the tool is appropriate (research, drafting, code review, refactoring, analysis, writing, any self-contained task) and states that it is for independent work. It does not explicitly list exclusions or alternatives, but there are no sibling tools to differentiate from, so the guidance is contextually sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.2.0
    • First observeddeepseek_task
Deploy Server
Context Every Agent Can ShareAgents share what they learn, hand off work, and keep it when you switch models. Start with 5 GB free.fulcradynamics.comAd

TDQS

A4.2/5.0

Scored across 1 tool

Disambiguation5/5

There is only one tool, so there is no possibility of confusion or overlapping purposes. The tool clearly covers the single action of delegating a sub-task to DeepSeek.

Naming Consistency5/5

With a single tool, there is no pattern to be inconsistent about. The name 'deepseek_task' is descriptive and follows a clear noun-based naming convention.

Tool Count3/5

One tool is on the thin side; while the server's purpose is narrow, a richer integration might include multiple tools (e.g., different task types or configuration options). The count is borderline but not unreasonable for a single-purpose sub-agent server.

Completeness4/5

The tool fully covers the stated purpose of delegating a self-contained task and returning the result. Minor gaps exist, such as no way to manage task history or configure multiple output modes, but these are not essential to the core workflow.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • deepseek-mcp

    arizen-dev
    A
    license
    A
    quality
    D
    maintenance
    Enables using DeepSeek models as a small, cheap supervised worker from any MCP-compatible client, providing fast flash and deep reasoning tools for bounded tasks.
    2
    53
    MIT

  • deepseek-subagent-mcp

    gaztrabisme
    A
    license
    A
    quality
    B
    maintenance
    Enables AI coding agents like Claude Code or Codex to delegate tasks to a DeepSeek Harness subagent with its own context window, providing tools for task delegation, result waiting, continuation, and supervision with sandboxed execution.
    6
    MIT