Skip to main content
Glama
deenesjakoruzh
kira4094

DeepSeek Subagent MCP Server

by kira4094
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

DeepSeek Subagent MCP Server

MCP-Server, der es Codex (oder jedem MCP-Client) ermöglicht, Teilaufgaben an DeepSeek als Sub-Agenten zu delegieren. Ein einziges Tool deepseek_task: Übergib eine Aufgabe (+ optionalen Kontext) und erhalte die endgültige Antwort von DeepSeek.

Funktionen

  • 🧠 Ein einziges Tool deepseek_task — delegiere unabhängige Aufgaben (Recherche, Entwurf, Review, Refactoring, Analyse)

  • 💰 Standardmäßig deepseek-v4-flash (kostengünstig), umschaltbar auf deepseek-v4-pro per Umgebungsvariable

  • 🔍 Zeigt reasoning_content (V4-Denkmodus) als <thinking>-Block an

  • ⚡ Keine Abhängigkeiten außer MCP, Ein-Zeilen-Deployment per npx

Related MCP server: deepseek-subagent-mcp

Voraussetzungen

Installation und Ausführung

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

Umgebungsvariablen

Variable

Erforderlich

Standard

Beschreibung

DEEPSEEK_API_KEY

✅

—

Ihr DeepSeek-API-Schlüssel.

DEEPSEEK_MODEL

deepseek-v4-flash

Modell. Auch deepseek-v4-pro.

DEEPSEEK_BASE_URL

https://api.deepseek.com

Endpunkt überschreiben (für Proxys).

DEEPSEEK_MAX_TOKENS

8192

Standardwert für maximale Ausgabetokens.

Codex-Konfiguration

Füge das zur MCP-Konfiguration von Codex hinzu:

{
  "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"
      }
    }
  }
}

Oder führe es aus einem lokalen Checkout aus:

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

Tool: deepseek_task

Parameter

Typ

Erforderlich

Beschreibung

task

string

✅

Die Teilaufgabe für DeepSeek. Beschreibe genau, was zu tun ist und welches Ausgabeformat erwartet wird.

context

string

Optionaler Kontext (Code, Notizen, Snippets).

system

string

Optionaler eigener System-Prompt (Standard: Persona eines Sub-Agenten).

max_tokens

number

Maximale Ausgabetokens. Standard: 8192.

temperature

number

0-2. Weglassen, um den Modellstandard zu verwenden.

Beispielverwendung

# 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")

Verwandte Projekte

Lizenz

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