How do I use AdaptOrch MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AdaptOrch MCP route 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

by dmae97

Overview Schema Related Servers Score Discussions

Python

Remote

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:

Sign up → adaptorch.com/app/signup
Create an API key → adaptorch.com/app/api-keys → generate a key (starts with ado_)

Use that key as ADAPTORCH_CONTROL_PLANE_TOKEN:

export ADAPTORCH_CONTROL_PLANE_TOKEN="ado_..."

Sign up at /app/signup and open /app/api-keys.
Generate an ado_* key and copy the raw value once.
Set it only in your local MCP environment as ADAPTORCH_CONTROL_PLANE_TOKEN.
Run adaptorch_run or adaptorch_list_runs from Claude Code.
Refresh /app/runs in the dashboard to see the same tenant's run list.

한국어 요약: 가입 → /app/api-keys에서 ado_* 생성 → MCP env에 설정 → Claude Code에서 실행/목록 확인 → 대시보드 /app/runs에서 같은 실행 목록 확인.

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)

Starter $0 includes API key access, 1,000 calls/month, and shadow mode. See adaptorch.com for Pro/Team plans.

Engine-delegated optional algorithm controls (latest)

AdaptOrch MCP forwards optional algorithm controls to the installed adaptorch engine. The wrapper does not implement these algorithms. Treat these as benchmark/eval or operator controls, not quickstart defaults.

Control	Scope	Verified behavior
`ADAPTORCH_REPRODUCIBLE`	Benchmark/eval beta	Fixes benchmark clock/RNG sources and canonicalizes record timing/path fields. It does not cover live-provider outputs, parallel-suite record order, cassettes, traces, or report timing aggregates.
`manifest_canonical_sha256`	Benchmark manifest	Importable as `adaptorch.benchmarking.manifest_canonical_sha256`; hashes canonical nonvolatile manifest fields.
`ADAPTORCH_ROUTER_ACCURACY_GATE`	Online router	`point` is the default; `wilson` uses a Wilson lower bound for learned-model adoption. Pair with operator knobs such as `retrain_window`, `min_loo_accuracy`, `min_posterior`, `quality_floor`, `use_quality_weights`, `use_failure_evidence`, `exploration_rate`, `max_observations`, `cv`, and `kfold_k`.
`pass_rate_credit` / `quality_signal`	Online-router learning	`compute_quality` tries exact-answer token matching before fuzzy matching. `pass_rate_credit` is opt-in partial credit; do not claim it changes `AdaptOrchEngine` router feedback by default.
`ADAPTORCH_PAPER_SEMANTIC_WEIGHT`	Synthesis	Default is `0.35`. Nonzero semantic weight, plus CJK/Hangul inputs, use Python scoring rather than the native fast path.
`prefer_multi_model_ensemble_singleton`	Routing threshold	Auto-enables when at least two ensemble providers exist and synthesis mode is not `direct`, unless an explicit debate-singleton preference wins. The MCP hint `prefer_ensemble_singleton` can request the same preference manually.

Related MCP server: Orchestrator 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-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`.

Scenario benchmark projection

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.com

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.com \
  --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

CLI and environment reference

Command	Purpose	Important options
`adaptorch-mcp`	Start the stdio or HTTP MCP server.	`--transport stdio
`adaptorch-mcp-doctor`	Print redacted local diagnostics.	`--json`, `--strict`
`adaptorch-mcp-smoke`	Verify stdio `initialize` + `tools/list`.	`--command`, `--base-url`, `--api-token`, `--timeout-seconds`, repeatable `--expected-tool`

For adaptorch-mcp, the public wrapper resolves the control-plane URL in this order: explicit --base-url, then trimmed/validated ADAPTORCH_CONTROL_PLANE_BASE_URL, then the hosted fallback https://adaptorch.com. adaptorch-mcp-smoke keeps a local-dev fallback of http://127.0.0.1:8000 when no base URL is configured. Pass --base-url explicitly in checked-in MCP client configs for reproducible behavior.

Variable	Purpose	Notes
`ADAPTORCH_CONTROL_PLANE_TOKEN`	Upstream AdaptOrch token.	Required unless `--api-token` is passed.
`ADAPTORCH_CONTROL_PLANE_BASE_URL`	Base URL used when `--base-url` is omitted.	Trimmed and validated as HTTP(S); do not embed credentials.
`ADAPTORCH_MCP_HTTP_AUTH_TOKEN`	Client-facing bearer token for HTTP/SSE MCP.	Keep separate from the upstream control-plane token.
`ADAPTORCH_MCP_ALLOWED_ORIGINS`	Comma-separated HTTP origin allowlist.	Use with browser or remote HTTP clients.
`ADAPTORCH_MCP_MAX_PAYLOAD_SIZE_BYTES`	Maximum accepted HTTP request body size.	Keep bounded for public deployments.
`ADAPTORCH_MCP_REQUEST_TIMEOUT_SECONDS`	HTTP request timeout budget.	Applies to HTTP server request handling.
`ADAPTORCH_MCP_MAX_SSE_SUBSCRIBERS`	Maximum concurrent SSE subscribers.	Defaults are provided by `adaptorch.mcp_server`.
`ADAPTORCH_MCP_TIMEOUT_SECONDS`	Control-plane client timeout for app-factory usage.	Useful when embedding the ASGI app.
`ADAPTORCH_REPRODUCIBLE`	Benchmark/eval reproducibility beta.	Benchmark/eval scope only; not general runtime determinism.
`ADAPTORCH_ROUTER_ACCURACY_GATE`	Online-router learned-model gate.	`point` default or `wilson`; advanced/operator use.
`ADAPTORCH_PAPER_SEMANTIC_WEIGHT`	Paper-mode lexical/semantic blend.	Default `0.35`; nonzero values use Python scoring over the native fast path.

Claude Code MCP config

{
  "mcpServers": {
    "adaptorch": {
      "command": "adaptorch-mcp",
      "args": [
        "--transport",
        "stdio",
        "--base-url",
        "https://adaptorch.com"
      ],
      "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

Checked-in examples use placeholders or environment interpolation. Fill real URLs and tokens only in local, uncommitted config files.

Diagnostics

Print redacted local diagnostics:

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

Run a stdio smoke test. The token is passed through the child environment, not process arguments. If no base URL is supplied, smoke targets http://127.0.0.1:8000 for local development.

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

Expected JSON includes "ok": true, adaptorch_plan_catalog, and the expected core tool subset. Doctor JSON also includes redacted controlPlane metadata for the resolved base-url source. Add repeatable --expected-tool <name> flags when validating a specific hosted/core release.

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 (write/destructive; keep manually approved).
`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`.

For trusted local clients, auto-approve only tools whose outputs are safe for that client. Keep adaptorch_run and adaptorch_cancel_run manually approved. For shared or production clients, avoid auto-approving run, artifact, and trace readers unless those payloads are already sanitized.

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

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

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

Latest Blog Posts

Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly
Lightport: Open-Sourcing Glama's AI Gateway
By punkpeye on April 27, 2026.
OpenAI
open source

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/dmae97/Adaptorch-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server

AdaptOrch MCP

Get your API key

Signup → MCP → dashboard run list

Engine-delegated optional algorithm controls (latest)

Research paper

Install

pip

uvx (one-shot, no install)

Why Claude Code users feel it quickly

Scenario benchmark projection

Architecture

Packages

Quickstart

Local development

stdio MCP

HTTP MCP

CLI and environment reference

Claude Code MCP config

Diagnostics

Tool surface

Branding assets

Public release checklist

Security

License

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API