dsh-baton

Name: dsh-baton
Author: JialaoLiu

by JialaoLiuversion 0.6.9i★ –iRuns on your computer

Registry nameio.github.JialaoLiu/dsh-baton

Install

Overview

Description from the maker (original English)

Hand tasks from Claude Code, Codex or any MCP client to a local DeepSeek Harness.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiEarly↓ 2.1K/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "dsh-baton": {
      "command": "npx",
      "args": [
        "-y",
        "dsh-baton-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add dsh-baton -- npx -y dsh-baton-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "dsh-baton": {
      "command": "npx",
      "args": [
        "-y",
        "dsh-baton-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "dsh-baton": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "dsh-baton-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account JialaoLiu, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 0% of the other 5571 servers in this category
Published to the registry more recently than 87% of the other 5571 servers in this category
Basic checks equal to or better than 100% of the other 5571 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is dsh-baton safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.6.9) reached the registry yesterday.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

GitHub

GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

by githubWidely used★ 34.2KUpdated 6 days ago

Computer or cloud

Install

Desktop Commander

MCP server for terminal commands, file operations, and process management

by wonderwhy-erWidely used↓ 709.6K/wk★ 10.2KUpdated 18 days ago

Runs on your computer

Install

Cloudflare MCP

Cloudflare MCP servers

by cloudflareWidely used★ 4.4KUpdated 14 months ago

Runs in the cloud✓ mcp.cloudflare.com

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

English | 中文 · macOS · Windows ( guide ) · no Node.js needed · zero dependencies · MIT

Quick start · See it run · Token usage · How it works · Tools · Security

Hand coding tasks from Claude Code or Codex to DeepSeek Harness on your machine: the task runs live in the Harness window, the main agent hears back once when it ends, and it gets back the files that changed, the project's own test results and a requirement-by-requirement checklist.

Cheaper on money and on quota: with Claude Code, the total cost, DeepSeek included, is 51–78% lower. On a long Codex job, the share of a ChatGPT Plus 5-hour window it uses goes from 86.01% to 16.01% with GPT-6 Astra, and from 13.71% to 2.67% with GPT-6.1 Sol.
Nothing to watch or poll: Claude Code is woken once when the run ends; Codex waits out a run of up to an hour in a single call.
A clear split of the work: Claude Code or Codex writes the request and checks the result; DeepSeek writes the code and runs the tests in Harness. The one who checks is not the one who wrote it.

Needs DeepSeek Harness ≥ 0.2.1-rc.2 (signed in) and Claude Code or Codex. Quick start →

What it is

dsh-baton is an MCP server plus a small Harness plugin. The main agent hands the task to an ordinary Harness session with one tool call: you watch it run in the Harness window and can step in at any time, and the main agent hears back once, without polling. Unlike a plain MCP bridge to another model, the work stays visible and the proof comes back with the result.

Why split the work

Beyond the savings, the split itself helps:

A spec before the code. The main agent turns your request into concrete items before handing it over, so Harness gets a spec rather than a one-line "build me an X".
Checked on evidence. The result carries the changed files, your project's own test output and a point-by-point check against the request; anything missing goes back for another round, instead of taking "done" on trust.
The main conversation keeps the decisions. Hundreds of steps of reading files, editing and running tests stay in the Harness session; the main conversation holds your request and the conclusions.
Visible and steerable. The work runs live in the Harness window. You can step in there at any time, and the main agent can steer a new requirement into the running turn.

Quick start

Nothing else to install: no Node.js, no terminal. The steps are the same on macOS and Windows.

1. Add the plugin in Harness: Plugins (插件) → Add plugin (添加插件) → type dsh-baton-host → Install (安装) → Enable now (立即启用).

Switched on, the plugin registers dsh-baton for Claude Code and Codex, for every folder, and Harness says so.

2. Open a new chat: in Claude Code or Codex, start a new conversation; one that was already open does not see dsh-baton yet. The steps are the same for both. Codex asks you to approve MCP tool calls: approve the dsh-baton tools (for unattended codex exec runs, add --approve-for-me).

3. Check: ask it to call dsh_list_workspaces. It lists your Harness workspaces.

That is all. To hand a task over, end the request with "hand it to DeepSeek Harness". Updating, removing, Windows, and registering by hand when the plugin could not, are further down.

New in 0.6.4: Codex waits in one dsh_wait call of up to 59 minutes, so a run that ends within the hour costs one model request to wait for, and the result arrives as an ordinary reply in the chat. Update the plugin in Harness and new Codex conversations have it, with no restart.

See it run

One Codex chat starts a task, queues a follow-up, steers the running turn, and waits for all three:

Codex and Claude take turns in the same Harness session, and every message is labelled with who sent it:

More demos →

Token usage

In short: hand over the big, long jobs. With Claude Code the total cost, DeepSeek included, is 51–78% lower. With Codex on GPT-6 Astra it is 47–81% lower, and the long kanban app takes 16.01% of a ChatGPT Plus 5-hour window instead of 86.01%.

Hand it over: big jobs, long jobs, and whenever the 5-hour window is running low. The main agent's own spend drops 61–87% on every task and model.

Do it yourself: small tasks on Codex with GPT-6.1 Sol. Sol is cheap enough that DeepSeek's part of a hand-over, two thirds or more, outweighs what Sol saves.

The three test tasks, asked the way a user would (the requests are in Chinese, linked as written):

Task 1 · CLI tool: a command-line task manager in Node, no dependencies, split into modules, with tests. Request
Task 2 · Web app: a task manager with a Node server and a page, REST API, stats, tests and a README. Request
Task 3 · Kanban app (phased): a kanban board in plain Node and a plain front end, in three phases given one after another in the same conversation: ① the core (REST API, storage, drag and drop); ② labels, due dates, search syntax, archive, undo and redo, activity log, import and export; ③ version conflicts, WIP limits, bulk actions, live sync over SSE, stats. 55 acceptance tests written beforehand, the same for both arms. Requests: phase 1 · phase 2 · phase 3

Task 3	Alone	Handed over
Opus 5.5 high	55/55, 39 min	55/55, 93 min
GPT-6.1 Sol high	55/55, 18 min	55/55, 101 min
GPT-6 Astra high	55/55, about 31 min	55/55, about 87 min

Cost per task at list price, DeepSeek V4.1 Flash 1017 at its peak price. Opus 5.5 runs in Claude Code, GPT-6.1 Sol and GPT-6 Astra in Codex, all at high effort:

Task	Model	Alone	Handed over: main agent	Handed over: DeepSeek	Handed over: total	Total	Main agent	5-hour window, alone → handed over
Task 1 · CLI tool	Opus 5.5 high	$1.07	$0.42	$0.10	$0.52	−51.2%	−60.8%	—
	GPT-6.1 Sol high	$0.17	$0.06	$0.13	$0.20	+15.3%	−62.9%	2.08% → 0.77%
	GPT-6 Astra high	$0.79	$0.30	$0.12	$0.42	−47.4%	−62.0%	7.83% → 2.98%
Task 2 · Web app	Opus 5.5 high	$2.13	$0.47	$0.13	$0.60	−71.7%	−77.7%	—
	GPT-6.1 Sol high	$0.41	$0.06	$0.18	$0.25	−40.2%	−84.5%	5.08% → 0.79%
	GPT-6 Astra high	$2.27	$0.30	$0.13	$0.42	−81.4%	−86.9%	22.43% → 2.94%
Task 3 · Kanban app (phased)	Opus 5.5 high	$14.18	$2.32	$0.84	$3.16	−77.7%	−83.6%	—
	GPT-6.1 Sol high	$1.12	$0.22	$0.81	$1.03	−8.1%	−80.5%	13.71% → 2.67%
	GPT-6 Astra high	$8.69	$1.62	$0.74	$2.35	−72.9%	−81.4%	86.01% → 16.01%

What was tested, and how

The only difference in the hand-over runs is one line added at the end of the request: 交给 DeepSeek Harness 去做。 ("Hand it to DeepSeek Harness.")
Same model and effort in both arms, and every run judged by the same acceptance check, which the model never sees.
Tasks 1 and 2: the mean of three runs per cell. Task 3: one run per cell. Every run passed its acceptance check, with no rework.
Prices are list prices. DeepSeek is counted at its peak price; the real DeepSeek bill came to about 58% of that.
The 5-hour window share is worked out from each run's cost: 12.27 points per $1 for GPT-6.1 Sol, 9.90 for GPT-6 Astra.
0.6.2 cuts the MCP instructions to one sentence, so Codex reads a few thousand to over ten thousand fewer new input tokens per hand-over. The Codex runs were measured with 0.6.1 to 0.6.3, where a Stop hook did the waiting; from 0.6.4 one dsh_wait call does it, also in one model request for every 59 minutes of a run.

How it works

<img src="./assets/readme/en/architecture.gif" width="100%" alt="MCP clients call dsh-baton-mcp over stdio; it reaches the dsh-baton-host plugin inside Harness through an owner-only local socket; the

Only the beginning is shown; read the rest in the repo

dsh-baton

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

What it is

Why split the work

Quick start

See it run

Token usage

How it works