Adclip MCP server

adclip

<!-- mcp-name: io.github.dreliq9/adclip -->

An open, standalone, model-routed marketing creative and learning engine.

adclip turns campaign intent into policy-checked copy, static creative, short-form video, and responsive email; preserves exact creative lineage; reads performance back from deployed creative; and structures that evidence into explicit experiments and next-test recommendations.

brief
  -> copy / image / video / email
  -> exact creative artifacts + provenance
  -> deployment lineage
  -> performance observations
  -> experiment evidence
  -> next test

MCP is one interface into adclip, not the architecture. The same application services are available to the standalone CLI and are intended to back a future local browser workbench.

Why adclip exists

Most AI marketing stacks split the workflow across a copy tool, image/video generators, an email platform, ad-platform dashboards, and creative analytics. adclip's goal is to keep the campaign model, creative lineage, and learning loop portable, while letting model providers and delivery platforms remain replaceable adapters.

Core principles:

one model vendor.

• Model-neutral: workflows request capabilities/routes rather than hard-code

or air-gapped when configured appropriately.

• Standalone: CLI workflows do not require an MCP host.
• Local-first: local command and OpenAI-compatible inference can run offline

mappings, observations, and experiments remain inspectable files.

• Portable: campaign artifacts, email HTML/text, manifests, deployment

causal lift.

• Evidence-aware: observational rankings are not silently presented as

executed.

• Spend-safe: paid generation is opt-in and route fallbacks are not silently

Start here

• Quickstart — zero-cost DTC creative, email, and learning walkthrough.
• Examples — marketer-facing campaign portfolio.
• Documentation index — architecture and capability docs.
• LLM guidance — model-neutral contributor/agent contract.

Install from PyPI:

pipx install adclip

The PyPI release can lag the current repository. For the exact main feature set documented here, install from source:

git clone https://github.com/dreliq9/adclip.git
cd adclip
python3.11 -m venv .venv
.venv/bin/pip install -e ".[dev]"

Python 3.11+ is required.

Five-minute zero-cost demo

Generate a fictional DTC skincare launch across Meta, Reels/TikTok, and Google using only fake creative providers:

adclip run examples/01-dtc-skincare/brief.json \
  --text-provider fake \
  --image-provider fake \
  --video-provider fake

Render the matching checked-in launch email without a model call:

adclip email render \
  examples/01-dtc-skincare/email_brief.json \
  examples/01-dtc-skincare/email_message.json \
  --output-dir ./adclip_skincare_email_render

Build a complete synthetic creative-test bundle:

python examples/06-creative-experiment/build_demo.py

Then inspect the evidence:

adclip performance report ./adclip_creative_test_demo \
  --since 2026-08-01 \
  --until 2026-08-07 \
  --action-report-time conversion

The builder prints an experiment ID that can be passed to experiment-evaluate and next-test. None of the commands above need a paid model API or live ad account.

Example portfolio

The repository examples are organized around marketing problems rather than internal subsystems:

Shortened. The full README is on GitHub.