AdaptOrch 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., "@AdaptOrch MCProute a task topology for the new deploy"
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.
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 artifactsGet your API key
AdaptOrch requires authentication. Get your token in two steps:
Sign up → adaptorch.ai.kr/app/signup
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 |
| All AdaptOrch API calls (run, status, artifacts) | Dashboard after signup |
| 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:
Abstract page: arxiv.org/abs/2602.16873
HTML paper: arxiv.org/html/2602.16873v1
Install
pip
pip install adaptorch-mcpIf 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-mcpuvx (one-shot, no install)
uvx adaptorch-mcp --helpWith the adaptorch dependency from GitHub:
uvx --with "adaptorch[api] @ git+https://github.com/dmae97/adaptorch.git" adaptorch-mcp --helpWhy Claude Code users feel it quickly
First-run win | Tool | What changes in the chat |
Less planning uncertainty |
| Claude can explain whether the task should be singleton, pipeline, DAG, or ensemble before spending run budget. |
Fewer failed long tasks |
| Large goals move through AdaptOrch routing, synthesis, and telemetry instead of one brittle pass. |
Evidence without context switching |
| Outputs, traces, and run proof come back into the Claude Code conversation. |
Safer setup support |
| Users can paste redacted diagnostics without leaking tokens. |
Fast install loop |
| Local MCP wiring is verified with |
Architecture
Packages
Path | Package | Purpose |
|
| Python CLI wrapper around |
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 --helpstdio 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.krHTTP 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 8765Health check:
python - <<'PY'
import httpx
print(httpx.get('http://127.0.0.1:8765/mcp/health').json())
PYClaude 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.jsonexamples/omk.mcp.jsonexamples/mcp-http.env.example
Diagnostics
Print redacted local diagnostics:
adaptorch-mcp-doctor
adaptorch-mcp-doctor --jsonRun 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.krExpected output includes adaptorch_plan_catalog and the core AdaptOrch MCP tool surface.
Tool surface
Tool | Purpose |
| Submit an AdaptOrch task payload and optionally wait. |
| Read run summary by |
| Read artifact metadata for a run. |
| List recent runs. |
| Read execution traces. |
| Request run cancellation. |
| Locally route a DAG through AdaptOrch's topology router. |
| Read redacted MCP server metrics. |
| Read synthesis modes, connectors, and server features. |
| Read hosted plan catalog: Starter |
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.pngGitHub flow diagram:
assets/mcp-flow.pngGPT-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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