adclip
Generates ad creative (copy and images) from a structured JSON brief across Meta, Google, LinkedIn, and X formats. Includes self-review loops for policy compliance and variant scoring, and can run without API keys using Claude CLI.
README
adclip
<!-- mcp-name: io.github.dreliq9/adclip -->
Generate ad creative from a single JSON brief. adclip is an MCP server that turns a structured brief into ad copy and static images across Meta, Google, LinkedIn, and X formats. Self-review loops filter for policy violations and score variants before export.
Runs under your Claude Code subscription with no API key — adclip
shells out to the claude CLI for LLM calls, so your subscription auth is
reused. Paid third-party providers (Anthropic direct, fal.ai image
generation) are opt-in and gated behind ADCLIP_ALLOW_LIVE_APIS=1 so a
stray key in your environment can't silently bill you.
What a run looks like
Brief in (examples/taichi_brief.json):
{
"product": "Taichi crypto trading bot",
"value_prop": "Paper-trade our signals before risking real cash.",
"audience": "Skeptical retail crypto traders.",
"angles": ["credibility", "curiosity"],
"tone": "confident, dry, no hype",
"cta": "Start paper trading",
"formats": ["meta_feed_4x5", "google_rsa"],
"variants": 2,
"policy_profile": "crypto",
"must_avoid": ["guaranteed returns"],
"use_judge": true,
"heal_violations": 2,
"output_dir": "/tmp/adclip_out"
}
Out:
- 2 ×
meta_feed_4x5composites (1080×1350, headline + body + CTA burned in) - 2 ×
google_rsatext variants manifest.jsonwith per-variant costs, policy flags, judge scores, and rationales- A campaign directory ready for
adclip_export_dco→ direct Meta DCO upload
Install
pipx install adclip
For the optional direct-Anthropic-API provider:
pipx install "adclip[anthropic]"
Requires Python 3.11+ and the claude CLI
on $PATH (for the default keyless LLM path).
From source (for contributors)
git clone https://github.com/dreliq9/adclip.git
cd adclip
python3.11 -m venv .venv
.venv/bin/pip install -e ".[dev]"
MCP usage
Add to your project's .mcp.json (or ~/.claude.json):
{
"mcpServers": {
"adclip": {
"command": "adclip-mcp"
}
}
}
Then ask Claude: "Generate ad variants for examples/taichi_brief.json"
The three tools you'll use most
adclip_generate_variants— full pipeline: brief → copy → policy → images → composite → renderadclip_generate_copy— copy pool only (cheap iteration before spending on images)adclip_export_dco— emit Meta DCO modular components (deduped headlines/bodies/ctas + per-aspect images)
<details> <summary>All 12 tools</summary>
Brief + inspection
adclip_brief_validate— schema checkadclip_estimate_cost— LLM + fal cost estimateadclip_list_formats— format catalogadclip_policy_check— policy dry-run on arbitrary copyadclip_campaign_status— manifest, variants, costs, missing-file audit for a campaign dir
Generation
adclip_generate_copy— copy pool onlyadclip_generate_visuals— given a list of winner copies, produce images + compositesadclip_generate_variants— full pipeline
Iteration on an existing campaign
adclip_render_variant— re-composite one variant (cheap; no LLM, no fal)adclip_regenerate— redo one variant's copy, visual, or bothadclip_score_variants— re-rank variants against (possibly edited) brief; heuristic or LLM judgeadclip_export_dco— Meta DCO modular export
</details>
CLI
adclip formats # list format specs
adclip estimate examples/taichi_brief.json # cost preview
adclip copy examples/taichi_brief.json # copy only (no images)
adclip run examples/taichi_brief.json --image fake # full pipeline, stub images
The CLI uses claude-cli by default — no key setup needed.
Formats
| Name | Aspect | Size | Kind |
|---|---|---|---|
meta_feed_1x1 |
1:1 | 1080×1080 | static |
meta_feed_4x5 |
4:5 | 1080×1350 | static |
google_display_square |
1:1 | 1200×1200 | static |
google_display_landscape |
1.91:1 | 1200×628 | static |
linkedin_single |
1.91:1 | 1200×627 | static |
x_promoted |
16:9 | 1200×675 | static |
google_rsa |
text | — | text |
stories_reels_9x16 |
9:16 | 1080×1920 | video¹ |
tiktok_9x16 |
9:16 | 1080×1920 | video¹ |
youtube_shorts_9x16 |
9:16 | 1080×1920 | video¹ |
¹ Video formats produce a fal.ai-generated clip (default kling-2.6, 5s)
with headline + CTA burned in via FFmpeg drawtext, scaled/padded to the
format's dimensions, and (when audio is present) loudness-normalized to
the format's LUFS target. Requires an ffmpeg build with the drawtext
filter (i.e. compiled with freetype). Set ADCLIP_ALLOW_LIVE_APIS=1 and
FAL_KEY to enable; pass --video fake (CLI) or video_provider="fake"
(MCP) for tests.
LLM provider modes
| Mode | Key? | Where it runs |
|---|---|---|
default / claude-cli |
none | Subprocess to the claude CLI; uses your subscription auth. |
sampling |
none | MCP sampling — asks the calling MCP client to run the LLM. Only works under clients that implement sampling (Claude Code does not today). |
anthropic |
adclip[anthropic] extra + key + ADCLIP_ALLOW_LIVE_APIS=1 |
Direct Anthropic API. ~3× faster per call. |
fake |
none | Deterministic scripted responses for tests. |
Self-review loops
- Judge (
use_judge: true): after policy filtering, an LLM scores each survivor on brand fit, angle fit, and copy quality; top-N by blended score wins.judge_score,judge_rationale, andjudge_flagsland in the manifest. - Heal (
heal_violations: N): policy-violating candidates are sent back to the LLM with the specific violations and asked to rewrite. Successful heals gain aheal_attemptscount and ahealed_fromsnapshot of the original copy. - Semantic policy (
use_semantic_policy: true): an LLM second-pass flags paraphrases that slip past the literal blocklist (e.g. "printing money" whenmust_avoidcontains "guaranteed returns"). Feeds the same heal loop. Adds one LLM call per candidate — opt-in.
Live-API opt-in
ADCLIP_ALLOW_LIVE_APIS=1 must be set to use any paid third-party API
(anthropic provider, fal.ai image + video). If a key is in your env but
the gate is closed, the provider refuses with a clear error instead of
billing you. Default keyless paths never need this set.
Tests
.venv/bin/python -m pytest
Status
v0.1 — static images, text ads, and 9:16 video ads (Reels / TikTok / Shorts) via fal.ai (declip-driven model catalog). 12 MCP tools, CLI, four LLM providers (claude-cli / sampling / anthropic / fake), Meta DCO export, self-review loops (policy + heal + semantic + judge).
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.