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AdaptOrch MCP

AdaptOrch MCP is the public MCP wrapper for AdaptOrch: a reliability kernel that lets Claude Code route tasks, launch orchestrated runs, and pull evidence artifacts back into the chat.

Use it when a coding task is too large, too ambiguous, or too expensive to trust to one single-pass response.

Claude Code → AdaptOrch MCP → route topology → run with synthesis → retrieve artifacts

Get your API key

AdaptOrch requires authentication. Get your token in two steps:

  1. Sign upadaptorch.ai.kr/app/signup

  2. Create an API key → Dashboard → API Key Management → generate a key (starts with ado_)

Use that key as ADAPTORCH_CONTROL_PLANE_TOKEN:

export ADAPTORCH_CONTROL_PLANE_TOKEN="ado_..."

Token

Purpose

Where to get it

ADAPTORCH_CONTROL_PLANE_TOKEN

All AdaptOrch API calls (run, status, artifacts)

Dashboard after signup

ADAPTORCH_MCP_HTTP_AUTH_TOKEN

Protect your local HTTP MCP endpoint

You define it (any secure string)

Free tier (Starter $0) includes API key access. See adaptorch.ai.kr for Pro/Team plans.

Related MCP server: Xylent MCP Server

Research paper

AdaptOrch MCP follows the AdaptOrch research line. Read the paper on arXiv:

Install

pip

pip install adaptorch-mcp

If AdaptOrch core is not yet on PyPI, install it from GitHub first:

pip install "adaptorch[api] @ git+https://github.com/dmae97/adaptorch.git"
pip install adaptorch-mcp

uvx (one-shot, no install)

uvx adaptorch-mcp --help

With the adaptorch dependency from GitHub:

uvx --with "adaptorch[api] @ git+https://github.com/dmae97/adaptorch.git" adaptorch-mcp --help

Why Claude Code users feel it quickly

First-run win

Tool

What changes in the chat

Less planning uncertainty

adaptorch_route_topology

Claude can explain whether the task should be singleton, pipeline, DAG, or ensemble before spending run budget.

Fewer failed long tasks

adaptorch_run

Large goals move through AdaptOrch routing, synthesis, and telemetry instead of one brittle pass.

Evidence without context switching

adaptorch_get_artifacts

Outputs, traces, and run proof come back into the Claude Code conversation.

Safer setup support

adaptorch-mcp-doctor

Users can paste redacted diagnostics without leaking tokens.

Fast install loop

adaptorch-mcp-smoke

Local MCP wiring is verified with initialize + tools/list.

Architecture

Packages

Path

Package

Purpose

packages/adaptorch-mcp

adaptorch-mcp

Python CLI wrapper around adaptorch.mcp_server

The wrapper intentionally delegates runtime behavior to adaptorch.mcp_server. That keeps MCP tools, resources, prompts, safety checks, and transports aligned with the latest AdaptOrch core release.

Quickstart

Local development

git clone git@github.com:dmae97/Adaptorch-MCP.git
git clone git@github.com:dmae97/adaptorch.git  # alongside Adaptorch-MCP
cd Adaptorch-MCP
uv sync --all-packages --extra dev
uv run adaptorch-mcp --help

stdio MCP

Use stdio for local clients such as Claude Code or desktop MCP hosts.

export ADAPTORCH_CONTROL_PLANE_TOKEN="<your-token>"
adaptorch-mcp --transport stdio --base-url https://adaptorch.ai.kr

HTTP MCP

Use HTTP for local gateways, reverse proxies, or remote MCP clients.

export ADAPTORCH_CONTROL_PLANE_TOKEN="<upstream-adaptorch-token>"
export ADAPTORCH_MCP_HTTP_AUTH_TOKEN="<client-facing-mcp-token>"

adaptorch-mcp \
  --transport http \
  --base-url https://adaptorch.ai.kr \
  --http-host 127.0.0.1 \
  --http-port 8765

Health check:

python - <<'PY'
import httpx
print(httpx.get('http://127.0.0.1:8765/mcp/health').json())
PY

Claude Code MCP config

{
  "mcpServers": {
    "adaptorch": {
      "command": "adaptorch-mcp",
      "args": [
        "--transport",
        "stdio",
        "--base-url",
        "https://adaptorch.ai.kr"
      ],
      "env": {
        "ADAPTORCH_CONTROL_PLANE_TOKEN": "${ADAPTORCH_CONTROL_PLANE_TOKEN}"
      }
    }
  }
}

More templates:

  • examples/claude_desktop_config.json

  • examples/omk.mcp.json

  • examples/mcp-http.env.example

Diagnostics

Print redacted local diagnostics:

adaptorch-mcp-doctor
adaptorch-mcp-doctor --json

Run a stdio smoke test. The token is passed through the child environment, not process arguments.

export ADAPTORCH_CONTROL_PLANE_TOKEN="<your-token>"
adaptorch-mcp-smoke --base-url https://adaptorch.ai.kr

Expected output includes adaptorch_plan_catalog and the core AdaptOrch MCP tool surface.

Tool surface

Tool

Purpose

adaptorch_run

Submit an AdaptOrch task payload and optionally wait.

adaptorch_get_run

Read run summary by run_id.

adaptorch_get_artifacts

Read artifact metadata for a run.

adaptorch_list_runs

List recent runs.

adaptorch_get_traces

Read execution traces.

adaptorch_cancel_run

Request run cancellation.

adaptorch_route_topology

Locally route a DAG through AdaptOrch's topology router.

adaptorch_server_metrics

Read redacted MCP server metrics.

adaptorch_capabilities

Read synthesis modes, connectors, and server features.

adaptorch_plan_catalog

Read hosted plan catalog: Starter $0, Pro $39, Team $149.

Read-only tools are safe candidates for MCP auto-approve. Keep adaptorch_run and adaptorch_cancel_run manually approved.

Branding assets

  • GitHub hero: assets/readme-hero.png

  • GitHub flow diagram: assets/mcp-flow.png

  • GPT-image-2.0 raster prompt brief: docs/brand/gpt-image-2-brief.md

Public release checklist

Before publishing:

uv run ruff check packages/adaptorch-mcp
uv run mypy packages/adaptorch-mcp/src
uv run pytest packages/adaptorch-mcp/tests -q
uv run python -m build packages/adaptorch-mcp --outdir dist
uv publish --dry-run dist/*

Then follow docs/publishing.md for PyPI Trusted Publishing or token-based uv publish.

Security

Never commit .env, API keys, bearer tokens, private keys, or MCP client tokens. See SECURITY.md.

License

Proprietary — Copyright EGG. All rights reserved. See LICENSE.

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license - not found
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quality - not tested
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maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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