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).
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