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MCP server · Content

io.github.dreliq9/adclip

Ad creative generation via MCP — copy and static images from a JSON brief. Keyless.

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About io.github.dreliq9/adclip

io.github.dreliq9/adclip is an MCP server in the Content category: ad creative generation via MCP — copy and static images from a JSON brief. Keyless. It has been installed 0 times through Conduid.

Install

uvx
uvx adclip
pip
pip install adclip

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

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_4x5 composites (1080×1350, headline + body + CTA burned in)
  • 2 × google_rsa text variants
  • manifest.json with 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 → render
  • adclip_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)

Brief + inspection

  • adclip_brief_validate — schema check
  • adclip_estimate_cost — LLM + fal cost estimate
  • adclip_list_formats — format catalog
  • adclip_policy_check — policy dry-run on arbitrary copy
  • adclip_campaign_status — manifest, variants, costs, missing-file audit for a campaign dir

Generation

  • adclip_generate_copy — copy pool only
  • adclip_generate_visuals — given a list of winner copies, produce images + composites
  • adclip_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 both
  • adclip_score_variants — re-rank variants against (possibly edited) brief; heuristic or LLM judge
  • adclip_export_dco — Meta DCO modular export

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, and judge_flags land 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 a heal_attempts count and a healed_from snapshot 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" when must_avoid contains "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).

README mirrored from the source repository 4 months ago. The original is authoritative.

Questions

About io.github.dreliq9/adclip

How do I install io.github.dreliq9/adclip?

Run uvx adclip, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is io.github.dreliq9/adclip safe to use with an AI agent?

Its trust score is 37 out of 100 (low). Conduid hasn't run static security checks on this repository yet, so review the source yourself before granting it credentials. It has no ConduID identity yet, so agent calls to it are not receipted.

Is io.github.dreliq9/adclip still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.