deepseek-delegate-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@deepseek-delegate-mcpGenerate the boilerplate code for a new Express route"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
DeepSeek Delegate MCP
A tiny local MCP server that exposes
DeepSeek V4 to Codex as a single tool: call_deepseek_sub_agent.
It turns Codex into a hybrid agent: GPT Sol stays the architect (planning, reviewing, integrating) and DeepSeek V4 Flash does the cheap execution (boilerplate files, test-suite generation, bulk text transformation) at $0.14 per 1M input tokens.
How it works
┌────────────────────────┐ call_deepseek_sub_agent ┌────────────────────────────┐
│ GPT Sol (Codex agent) │ ───────────────────────────▶ │ local MCP server (node) │
│ plans / reviews / │ ◀─────────────────────────── │ "deepseek-delegate" │
│ integrates │ output text └─────────────┬──────────────┘
└────────────────────────┘ │ POST /responses
▼
DeepSeek API (api.deepseek.com)
deepseek-v4-flash / deepseek-v4-proCodex runs one model per session, so instead of switching providers mid-task you give Sol a delegation tool. Sol crafts a precise prompt, DeepSeek answers with plain text, and Sol reviews + applies the result. No file access is granted to DeepSeek — it only ever sees the exact prompt you send.
Related MCP server: Codex Gemini Delegator V2
Features
Single MCP tool:
call_deepseek_sub_agent(prompt required; model, system, temperature, max_output_tokens, reasoning_effort optional)Uses DeepSeek's native Responses API
Zero extra key setup if you already use DeepSeek's official Codex integration
Works with Codex CLI, the ChatGPT desktop app, and the Codex IDE extension
Requirements
Codex (CLI or desktop app)
A DeepSeek API key from the DeepSeek Platform
Node.js 18+
Quick start
1. Clone and install
git clone https://github.com/trixmix821/deepseek-delegate-mcp.git
cd deepseek-delegate-mcp
npm install2. Register the MCP server
Add this to ~/.codex/config.toml (use the absolute path from step 1):
[mcp_servers.deepseek-delegate]
command = "node"
args = ["/absolute/path/to/deepseek-delegate-mcp/deepseek-mcp-server.mjs"]
env_vars = ["DEEPSEEK_API_KEY"]
tool_timeout_sec = 600
default_tools_approval_mode = "auto"3. Provide your API key
The server looks for the key in this order:
DEEPSEEK_API_KEYenvironment variableexperimental_bearer_tokenunder[model_providers.deepseek]in~/.codex/config.toml(this is what DeepSeek's official Codex setup script writes — if you've run it, you're already done)
export DEEPSEEK_API_KEY=sk-...4. Teach Sol to delegate
Append to ~/.codex/custom_instructions.md:
## Hybrid Architect: Sol (planner) + DeepSeek (executor)
You are the master architect (GPT Sol). You own the high-level plan, design,
and project structure. DeepSeek is your cheap execution layer.
For massive boilerplate files, extensive test-suite generation, bulk/repetitive
text transformation, or well-specified mechanical subtasks, invoke the
`call_deepseek_sub_agent` tool instead of doing the work in your own context.
Craft a precise, self-contained prompt: exact file paths, signatures, language,
framework, constraints, and expected output format. Never delegate open-ended
design decisions. Review DeepSeek's output, fix logic/interface mismatches, then
integrate. If you are already running as a DeepSeek model, do not delegate.5. Verify
node test-deepseek.mjs "Reply with exactly: OK"Expected output: the model replies OK, and a usage line
([deepseek-delegate] model=... input_tokens=... output_tokens=...) is printed
to stderr.
Then restart Codex so it loads the new MCP server (in the TUI, /mcp shows
active servers), and ask something like: "delegate the test-suite boilerplate
to DeepSeek."
Tool reference
call_deepseek_sub_agent
Parameter | Type | Required | Default | Description |
| string | yes | — | The exact coding instruction or context to process |
| string | no |
|
|
| string | no | — | Optional system prompt for the sub-agent |
| number | no | — | 0.0–2.0 (no effect in thinking mode) |
| number | no |
| Maximum output tokens |
| string | no | — |
|
| boolean | no | off | Force thinking mode on/off |
Which model? deepseek-v4-flash is the default and right for nearly all
delegated work (boilerplate, tests, transformations). Escalate to
deepseek-v4-pro only when a single small task genuinely needs stronger
reasoning — it costs ~3x more.
Configuration
Environment variables (all optional):
Variable | Default | Description |
| — | DeepSeek API key (falls back to your Codex config) |
|
| Default model for the tool |
|
| API base URL |
|
|
|
|
|
|
|
| Reject prompts larger than this to prevent giant-payload delegation |
Costs
DeepSeek V4 pricing (per 1M tokens, source):
Model | Input (cache miss) | Input (cache hit) | Output |
| $0.14 | $0.0028 | $0.28 |
| $0.435 | $0.003625 | $0.87 |
Context window is 1M tokens; max output is 384K.
Security notes
The server only calls the DeepSeek API. It never reads or writes your files, and DeepSeek only sees the text you put in the
prompt.DeepSeek's official setup stores your key in
~/.codex/config.tomlin plaintext. Preferexport DEEPSEEK_API_KEY=...and keep the token out of the file.default_tools_approval_mode = "auto"lets Sol call the tool without a prompt; change it topromptif you want to approve every delegation.
Limitations
The tool only receives the prompt text — it has no access to your repository, so delegated prompts must be self-contained.
Delegation is only worth it for SMALL, mechanical, self-contained tasks. Prompts over 30,000 chars are rejected (configurable via
DEEPSEEK_MAX_PROMPT_CHARS) — split them into smaller subtasks instead.Thinking mode is off by default for speed; enable it via the
thinkingtool parameter only when the task genuinely needs reasoning.Delegation is a judgment call by the agent. Explicitly asking for it ("delegate X to DeepSeek") makes it deterministic.
If your session is already running on DeepSeek, delegation is redundant.
License
MIT
Available Tools
1 toolcall_deepseek_sub_agentDeepSeek Sub-AgentA
Delegates massive text processing, code generation boilerplate, or complex sub-modules to DeepSeek V4 to conserve Sol context tokens. Returns the model's text output.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | DeepSeek model to use: deepseek-v4-flash (default) or deepseek-v4-pro. | |
| prompt | Yes | The exact coding instruction or context to process. | |
| system | No | Optional system prompt for the sub-agent. | |
| thinking | No | Force thinking mode on/off. Defaults to off (fast, cheap). Only enable for tasks that genuinely need reasoning. | |
| temperature | No | Sampling temperature (0.0-2.0; no effect in thinking mode). | |
| reasoning_effort | No | Reasoning effort (enables thinking mode; omit for fast non-thinking execution). | |
| max_output_tokens | No | Maximum output tokens (default 8192). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool delegates to an external model and returns text output, which is useful. However, it does not address potential side effects, latency, cost, or error behavior, leaving gaps for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the primary purpose and ending with a clear return-value statement. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no output schema, no annotations), the description is fairly minimal. It states the return value as text output but does not elaborate on usage patterns, error scenarios, or how parameters interact. It is adequate but leaves room for more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The tool description adds no parameter-specific guidance beyond what the schema provides, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('delegates') and resource ('DeepSeek V4'), clearly stating the tool's function: handling massive text processing, code generation boilerplate, or complex sub-modules to conserve Sol context tokens. This distinguishes it from general-purpose tools, even though no siblings are listed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (for massive tasks that would otherwise consume Sol context tokens) and implies it is for offloading heavy work. It does not explicitly state when not to use it or list alternatives, but the absence of siblings makes this acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusing it with others. The tool's purpose is clearly defined as delegating to DeepSeek V4.
The tool name follows a clear verb_noun pattern ('call' + 'deepseek_sub_agent'). With only one tool, there are no inconsistencies or mixed conventions.
The server has a single tool, which is slightly below the typical well-scoped range. However, it is appropriate for the narrow purpose of delegating to DeepSeek, making the count reasonable.
The tool provides a generic delegation capability for any text processing or code generation task, covering the entire stated domain. There are no obvious missing operations for a simple delegate service.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Official MCP server for Agentwork — delegate tasks to AI agents with human-in-the-loop
An MCP server that gives your AI access to the source code and docs of all public github repos
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
Related MCP Servers
- FlicenseBqualityBmaintenanceA Codex MCP server that delegates exploration and implementation tasks to DeepSeek via Claude Agent SDK, supporting subagent types and task persistence.21
- AlicenseAqualityBmaintenanceAn MCP server that lets OpenAI Codex delegate tasks to Google Gemini agents as isolated background workers, with Git worktree isolation, loop detection, and security boundary enforcement.71MIT
- FlicenseAqualityCmaintenanceMCP server that packages DeepSeek agents as callable tools, offering a bare model for quick Q&A and a full agent capable of executing real coding and file-modifying tasks in a workspace.2
- AlicenseAqualityBmaintenanceLocal MCP server that provides DeepSeek-powered code review, file analysis, and consultation tools for coding agents, enabling read-only analysis with minimal token overhead.314MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/trixmix821/deepseek-delegate-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server