Which integrations are available for this server?

Integrates with Meta's advertising platform to sync read-only performance memory (spend, impressions, video metrics, etc.), scan competitor ads from the Meta Ad Library, and import Meta ad performance data via MCP, enabling AI agents to analyze winners, demographic opportunities, and taxonomy gaps.

How do I use creative-tagger-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., "@creative-tagger-mcp analyze the ad creative at ./new_ad.mp4 for Acme brand" 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.

creative-tagger-mcp

by stephenlavender

Overview Schema Related Servers Score Discussions

Python

Remote

Creative Tagger MCP Server

The MCP layer for Creative Tagger — plug structured creative intelligence into any AI agent (Claude Desktop, Cursor, Windsurf, ChatGPT with MCP, etc.).

Status note (2026-06-15): PyPI still serves creative-tagger-mcp==0.1.0. This README tracks the local 0.2.0 branch surface in this repo. Tools such as strategy reports, brain learnings, and performance timeseries require the matching API branch and should be treated as pre-publish until 0.2.0 is live on PyPI and the corresponding API changes are deployed.

Your AI of choice gets:

Taxonomy — 28 standardized dimensions for any ad creative (video, image, carousel, landing page, email)
Memory — every analysis is saved to the user's library; the agent can search it, recall patterns, and pull individual results
Brand-custom taxonomy — extend the standard taxonomy with each brand's founders, products, segments, aliases, and naming variables
Meta performance memory — read-only Meta sync/status/tools so agents can reason over winners, unproven tags, demographic opportunities, and taxonomy gaps
Brain learnings — auto-written account learnings in plain language, with agent-ready context for the next brief
Strategist — recommendation + gap-analysis tools that reason over the user's library plus saved brand context (voice, audience, anti-patterns)
Competitive intelligence — scan a competitor's Meta Ad Library through Creative Tagger's native Market access

Quick Start

# Install (PyPI currently resolves to 0.1.0)
pip install creative-tagger-mcp

# Run against production (default)
CREATIVE_TAGGER_API_KEY=ct_your_key creative-tagger-mcp

# Or against a local API
CREATIVE_TAGGER_URL=http://localhost:8000 \
CREATIVE_TAGGER_API_KEY=ct_your_key \
  creative-tagger-mcp

Get an API key at app.creativetagger.ai.

Related MCP server: muze-mcp

Release Verification

Before publishing a new MCP version, build the artifacts and smoke-test the wheel that will be uploaded to PyPI:

python -m build
python scripts/smoke_release.py
python -m twine check dist/*

The smoke test installs the wheel into a temporary virtualenv, verifies the creative-tagger-mcp console entry point, checks the package version, and confirms the V1 tool surface is present from the installed artifact.

Publishing to PyPI

The release workflow publishes from GitHub Actions after it builds the package, runs scripts/smoke_release.py, and passes twine check.

Recommended path:

git tag v0.2.0
git push origin v0.2.0

The workflow supports PyPI trusted publishing with GitHub OIDC. Configure the PyPI publisher for repository stephenlavender/creative-tagger-mcp, workflow .github/workflows/publish.yml, environment pypi, then push the version tag.

Exact PyPI trusted publisher values:

PyPI project: creative-tagger-mcp
Publisher: GitHub
Owner: stephenlavender
Repository: creative-tagger-mcp
Workflow filename: publish.yml
Environment name: pypi

If the workflow fails with invalid-publisher, PyPI does not have a trusted publisher matching those claims yet. Add the publisher above, then rerun the failed workflow or push the version tag again.

Fallback path: add a GitHub Actions repository secret named PYPI_API_TOKEN containing a PyPI project token. The same workflow will use that token when it is present.

Local fallback:

python -m build
python scripts/smoke_release.py
python -m twine check dist/*
python -m twine upload dist/*

Add to Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "creative-tagger": {
      "command": "creative-tagger-mcp",
      "env": {
        "CREATIVE_TAGGER_URL": "https://api.creativetagger.ai",
        "CREATIVE_TAGGER_API_KEY": "ct_your_key_here"
      }
    }
  }
}

Restart Claude Desktop. The tools appear in the MCP picker.

Tools

`analyze_creative`

Analyze any ad creative and get structured classification across 28 dimensions.

{ "file_path": "./ad.mp4", "brand_name": "Brand" }
{ "url": "https://example.com/landing-page", "brand_name": "Brand" }
{ "html_content": "<html>...</html>", "brand_name": "Brand" }

Results auto-save to the user's library.

`get_taxonomy`

Live fetch of the complete taxonomy or a single dimension.

{}                                # all 28 dimensions
{ "dimension": "hook_type" }      # one dimension

Taxonomy v2 splits three dimensions the old model mixed together: media type (the auto-detected format — static image, video, carousel; never AI-classified), asset type (production class: UGC, Studio, High Production, …), and visual format (execution style: Talking Head, Demo, Testimonial, …). Static Image and Carousel are media types and are no longer valid visual_format values. messaging_angle is the canonical angle dimension.

`list_library`

Browse saved analyses. Search by filename or hook, filter by format, messaging angle, emotion, CTA, talent, offer, audio type, or seasonality, and sort by joined performance.

{
  "limit": 50,
  "search": "BFCM",
  "format": "video",
  "angle": "Social Proof",
  "talent": "Founder",
  "sort": "roas"
}

`get_library_patterns`

Cross-library pattern insights — concentration and diversity per dimension, plus rule-based diversification flags.

`get_analysis`

Pull the full 28-dimension result for one library item.

{ "analysis_id": 42 }

`recommend` ⭐

Ask the Creative Strategist a question grounded in the user's library + brand context.

{ "brand_name": "Acme", "question": "What kind of UGC should I test for Q4?" }

Returns concrete recommendations using taxonomy values + library observations.

`analyze_gaps` ⭐

Identify concentration risk in the library and propose next creatives that diversify it.

{ "brand_name": "Acme" }

`get_brand_context` / `set_brand_context`

Long-term memory per brand. Voice, target audience, top performers, anti-patterns, notes.

set_brand_context: {
  "brand_name": "Acme",
  "voice": "clinical, precise, no personality",
  "target_audience": "new moms 28-40, postpartum recovery",
  "top_performers": ["UGC TalkHead", "BeforeAfter visuals"],
  "anti_patterns": ["loud humor", "celebrity endorsement"],
  "notes": "Q4 focus: gift-shoppers + retention"
}

Strategist tools auto-include this context.

`get_brand_taxonomy` / `set_brand_taxonomy_value` / `delete_brand_taxonomy_value` / `set_brand_entity` / `delete_brand_entity`

Customize the standard taxonomy for one brand without breaking cross-brand reporting.

set_brand_taxonomy_value: {
  "brand_name": "Acme",
  "dimension": "talent",
  "value": "Stephen Lavender / Founder",
  "aliases": ["Stephen", "founder"],
  "description": "Use when Stephen appears or is referenced"
}

set_brand_entity: {
  "brand_name": "Acme",
  "entity_type": "product",
  "name": "Creative Tagger",
  "aliases": ["CT", "tagger"]
}

delete_brand_taxonomy_value: {
  "brand_name": "Acme",
  "dimension": "talent",
  "value": "Old Founder Label"
}

delete_brand_entity: {
  "brand_name": "Acme",
  "entity_type": "product",
  "name": "Retired Product"
}

`get_naming_variables` / `list_naming_templates` / `save_naming_template`

Manage saved naming templates from your agent. Templates support standard taxonomy fields plus brand-custom variables like founder, product, offer, customer_segment, icp, and campaign_label. Saved templates auto-apply to future analyze_creative results.

save_naming_template: {
  "name": "default",
  "template": "{brand}_{founder}_{customer_segment}_{hook_type}_{cta}_{ratio}_{version}"
}

Use preview_naming_template to test a template before saving, and delete_naming_template to remove one.

`get_meta_status` / `sync_meta_performance`

Check or trigger read-only Meta performance memory. No campaign creation, no budget edits. Creative Tagger must have an approved native Meta OAuth connection before customer accounts can sync Meta performance. Pass attribution_windows when the buyer uses a non-default Meta lookback window and Creative Tagger should match Ads Manager exactly.

{
  "brand_name": "Acme",
  "date_preset": "last_30d",
  "attribution_windows": ["7d_click", "1d_view"]
}

`get_creative_strategy_report`

Pull the same strategy matrix shown in Creative Tagger Reports. Defaults to ad types by messaging angles, with states for next tests, live learning, winners, losers, fatigue, and gaps. Returns the decision queue plus an agent_context payload that can be handed directly to an LLM for strategy work. Supports creative-diagnostics metrics such as CTR, thumbstop, hook, hold, video milestone rates, CPA, CVR, ROAS, revenue, spend, and funnel score. For audience-mode reads, switch the axes to demographic dimensions such as demographic_age and demographic_gender, or use the demographic-read or audience-signals templates. Other built-in templates include creative-winners, fatigue-watch, coverage-gaps, hook-performance, and persona-read. Creative axes follow taxonomy v2: visual_format (execution style), asset_type (production class), and media_type (auto-detected format) are three separate dimensions, with ad_type kept as a deprecated alias for visual_format. For mixed creative × audience reads, keep one creative axis such as messaging_angle, visual_format, hook, persona, or offer_type and set the other axis to demographic_segment or demographic_signal. Add fatigue_minimum_calendar_days when fatigue should only count after a long enough live window, not just after a few close-together synced points. For fatigue-aware reads, pass the same embedded watch controls the app/API support: watch_group_by, watch_metric, watch_signal_focus, watch_trajectory_focus, watch_coverage_focus, watch_minimum_points, watch_minimum_calendar_days, watch_maximum_gap_days, and watch_limit.

{
  "brand_name": "Acme",
  "report_template": "next-tests",
  "rows": "ad_type",
  "columns": "messaging_angle",
  "metrics": "spend,ctr,thumbstop_rate,hook_rate,hold_rate,cpa"
}

{
  "brand_name": "Acme",
  "report_template": "demographic-read",
  "rows": "demographic_age",
  "columns": "demographic_gender",
  "metrics": "spend,roas,ctr,cpa,conversions,revenue",
  "roas_target": 2.5,
  "fatigue_minimum_calendar_days": 7,
  "watch_group_by": "hook_type",
  "watch_metric": "cpa",
  "watch_signal_focus": "fatigued",
  "watch_trajectory_focus": "worsening",
  "watch_coverage_focus": "windowed_history",
  "watch_minimum_points": 2,
  "watch_minimum_calendar_days": 7,
  "watch_maximum_gap_days": 7,
  "watch_limit": 5,
  "start_date": "2026-05-01",
  "end_date": "2026-05-31"
}

{
  "brand_name": "Acme",
  "report_template": "audience-signals",
  "rows": "demographic_signal",
  "columns": "demographic_segment",
  "metrics": "spend,roas,ctr,cpa,conversions,revenue",
  "date_preset": "last_30_days"
}

{
  "brand_name": "Acme",
  "rows": "messaging_angle",
  "columns": "demographic_segment",
  "status_focus": "all",
  "metrics": "spend,roas,ctr,cpa,conversions,revenue",
  "fatigue_minimum_calendar_days": 7,
  "date_preset": "last_30_days"
}

`get_brain_learnings`

Read the auto-written Brand Brain learnings generated from performance memory, strategy cells, taxonomy winners/watchouts, and audience signals. Returns a hero learning, concise stories, and an agent_context payload for the next brief or strategist prompt. Use kinds when an agent only wants a focused slice such as conclusion, working,audience, or watch. Add conclusion_statuses to narrow conclusion stories to winner, fatigued, or loser outcomes only, and conclusion_recency_days to keep only the most recent conclusion window. Use watch_group_by, watch_metric, watch_signal_focus, watch_trajectory_focus, watch_coverage_focus, watch_minimum_points, watch_minimum_calendar_days, watch_sources, and fatigue_decay_threshold when the watchouts should be written from a different fatigue lens such as fatigued-only CPA by ad type, weak taxonomy patterns only, CTR by hook, or stable ROAS by demographic_segment.

{
  "brand_name": "Acme",
  "date_preset": "last_30_days",
  "minimum_spend": 500,
  "learning_spend": 1500,
  "kinds": "conclusion,watch",
  "conclusion_statuses": "winner,fatigued",
  "conclusion_recency_days": 21,
  "watch_group_by": "ad_type",
  "watch_metric": "cpa",
  "watch_signal_focus": "fatigued",
  "watch_trajectory_focus": "worsening",
  "watch_coverage_focus": "windowed_history",
  "watch_minimum_points": 3,
  "watch_minimum_calendar_days": 7,
  "watch_sources": "timeseries,patterns",
  "fatigue_decay_threshold": 0.25,
  "limit": 6
}

`save_brain_learnings`

Persist the current auto-written Brand Brain learnings into saved Brain notes for a brand, using the same filtering controls as get_brain_learnings. Use this after reviewing a conclusion/working/watch/audience/gap slice when the user wants the best current learnings saved back into reusable strategist context.

{
  "brand_name": "Acme",
  "date_preset": "last_30_days",
  "minimum_spend": 500,
  "learning_spend": 1500,
  "kinds": "conclusion,watch",
  "conclusion_statuses": "winner,fatigued",
  "conclusion_recency_days": 21,
  "watch_group_by": "ad_type",
  "watch_metric": "cpa",
  "watch_signal_focus": "fatigued",
  "watch_trajectory_focus": "worsening",
  "watch_coverage_focus": "windowed_history",
  "watch_minimum_points": 3,
  "watch_minimum_calendar_days": 7,
  "watch_sources": "timeseries,patterns",
  "include_gaps_in_notes": false,
  "limit": 6
}

`get_performance_timeseries`

Read saved performance curves for fatigue checks without opening the dashboard. Returns dated points plus a fatigue signal for each grouped series, using the same decay threshold as Creative Tagger's strategy matrix. Group by creative, campaign, landing page, analysis_id, or audience slices like demographic_age, demographic_gender, demographic_segment, and demographic_signal, and inspect metrics like ROAS, CPA, CTR, CPM, thumbstop, completion rate, or funnel score. Use signal_focus when an agent only wants the current fatigue watchlist or only stable controls, and trajectory_focus when the agent wants only worsening, improving, flat, or insufficient-data series. Use coverage_focus to isolate call-ready, gappy, short-window, or windowed-history curves. Add minimum_calendar_days when fatigue should only count after a trend has been live long enough, not just after a few close-together points.

{
  "brand_name": "Acme",
  "date_preset": "last_30d",
  "group_by": "ad_name",
  "metric": "roas",
  "signal_focus": "fatigued",
  "trajectory_focus": "worsening",
  "coverage_focus": "call_ready",
  "minimum_spend": 500,
  "minimum_points": 3,
  "minimum_calendar_days": 7,
  "fatigue_decay_threshold": 0.18,
  "limit": 5
}

Use date_preset for a standard lookback window, or pass explicit start_date / end_date to override it.

`export_performance_timeseries_context`

Return the reusable agent_context payload from performance time series. Use this when another agent needs the fatigue decision queue, summary text, action mix, top groups, and prompt-ready export without carrying the full chart payload. It accepts the same inputs as get_performance_timeseries.

{
  "brand_name": "Acme",
  "date_preset": "last_30d",
  "group_by": "ad_name",
  "metric": "roas",
  "signal_focus": "fatigued",
  "trajectory_focus": "worsening",
  "coverage_focus": "call_ready",
  "minimum_spend": 500,
  "minimum_points": 3,
  "minimum_calendar_days": 7,
  "fatigue_decay_threshold": 0.18,
  "limit": 5
}

Internal migration/backfill tools are hidden from the default published MCP surface. They require CREATIVE_TAGGER_INTERNAL_BACKFILL_TOOLS=1 and should not be used in customer flows or to avoid Meta approval.

`get_meta_performance_summary`

Read saved Meta performance memory without triggering a sync.

{ "brand_name": "Acme" }

Returns account totals plus performance by standard taxonomy and brand-custom taxonomy. Each aggregate can include funnel_score and a funnel explanation object for capture -> hold -> bring-to-site -> convert diagnosis.

`get_taxonomy_performance`

Find which tags scale, which are unproven, and which standard taxonomy values have not been tested yet. Rows include ROAS, CTR, thumbstop, and funnel scores when performance memory exists.

{ "brand_name": "Acme", "dimension": "hook_type", "spend_threshold": 500 }

`get_prebuilt_reports`

Return ready-made Motion-style reports: best hooks, landing pages, messaging angles, audiences, offers, CTAs, visual formats, and brand-custom values. Add start_date / end_date when the report should only cover a specific synced window.

{ "brand_name": "Acme", "report_id": "best_hooks", "limit": 8 }
{ "brand_name": "Acme", "report_id": "best_angles", "start_date": "2026-05-01", "end_date": "2026-05-31", "limit": 8 }

`create_custom_report`

Build a custom report from selected standard or brand taxonomy dimensions and rank the actual matched dimension combinations by ROAS, funnel score, spend, CTR, or CPA. Use this for Motion-style views like best hook x landing page x offer, founder x hook, audience x offer, or brand segment x product. Add start_date and end_date when the report should isolate a specific test window instead of the full synced history.

{
  "brand_name": "Acme",
  "dimensions": ["hook_type", "landing_page", "offer_type"],
  "layer": "all",
  "metric": "roas",
  "start_date": "2026-05-01",
  "end_date": "2026-05-31"
}

Rows can include parts and values, so the agent can explain a winning combination instead of treating each tag independently.

Saved custom reports

Save reusable report definitions, list them for a brand, rerun them by id, or delete them when they are no longer needed. Saved reports can also persist a custom start_date / end_date window for a specific launch or test period, plus dashboard-style preset state such as view_type, date_range, group_by, metrics, filters, sort, and saved_metric_preset.

{
  "brand_name": "Acme",
  "name": "Hook + LP + Offer",
  "dimensions": ["hook_type", "landing_page", "offer_type"],
  "view_type": "matrix",
  "date_range": "custom",
  "group_by": "dimension",
  "metrics": ["spend", "roas", "cpa", "ctr"],
  "filters": [{"field": "status", "value": "winner"}],
  "sort": "desc",
  "saved_metric_preset": "delivery",
  "start_date": "2026-05-01",
  "end_date": "2026-05-31"
}
{ "brand_name": "Acme" }
{ "report_id": 7 }

Tools: save_custom_report, list_custom_reports, run_saved_custom_report, delete_custom_report.

`predict_creative`

Score a saved analysis or draft attributes before it spends, using the brand's own performance memory. Returns a fit score, per-tag ratings, and recommended swaps.

{ "brand_name": "Acme", "attributes": { "hook_type": "Question", "cta": "Shop Now" } }

`get_demographics_performance`

Read age x gender performance memory with opportunity and waste flags. Use date_preset for a standard audience window, or start_date / end_date to isolate a specific audience window.

{
  "brand_name": "Acme",
  "date_preset": "last_30_days",
  "start_date": "2026-05-01",
  "end_date": "2026-05-31"
}

`export_demographics_context`

Return an agent-ready audience context payload from the saved demographics read. Use this when another agent needs the top audience opportunities and waste segments, blended totals, per-segment mixed creative x audience views, and a prompt-ready summary without the full wrapper.

{
  "brand_name": "Acme",
  "date_preset": "last_30_days",
  "limit": 3
}

`generate_brand_taxonomy`

Generate brand-specific messaging themes and intended audiences from the analyzed creative library, then optionally save them to Brand Taxonomy Studio.

{ "brand_name": "Acme", "persist": true }

`scan_competitor`

Classify a competitor's Meta Ad Library ads and get strategy breakdown.

{ "brand_name": "Acme", "page_name": "Hims & Hers", "limit": 25 }

Internal competitor-row backfill is also hidden from the default published MCP surface. Customer-facing competitor intelligence should use scan_competitor after native Meta Ad Library access is approved.

`get_competitor_scan_history`

Read the saved Market scans/imports for a workspace without re-running Meta Ad Library access. Useful when the agent needs the latest saved competitor hooks, styles, or scan metadata before drafting briefs.

{ "brand_name": "Acme", "limit": 6 }

`generate_naming`

Build naming strings from already-classified attributes (rarely needed — analyze_creative already includes naming).

Architecture

Your AI agent  ←—stdio—→  creative-tagger-mcp  ←—HTTPS—→  api.creativetagger.ai
                                                              │
                                                              ├── Gemini 2.5 Flash (classifier)
                                                              ├── Claude Sonnet (fallback)
                                                              ├── SQLite (library + brand memory)
                                                              └── Meta Ad Library

You bring the agent. We provide the taxonomy, the memory, and the strategist.

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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Creative Tagger MCP Server

Quick Start

Release Verification

Publishing to PyPI

Add to Claude Desktop

Tools

analyze_creative

get_taxonomy

list_library

get_library_patterns

get_analysis

recommend ⭐

analyze_gaps ⭐

get_brand_context / set_brand_context

get_brand_taxonomy / set_brand_taxonomy_value / delete_brand_taxonomy_value / set_brand_entity / delete_brand_entity

get_naming_variables / list_naming_templates / save_naming_template

get_meta_status / sync_meta_performance

get_creative_strategy_report

get_brain_learnings

save_brain_learnings

get_performance_timeseries

export_performance_timeseries_context

get_meta_performance_summary

get_taxonomy_performance

get_prebuilt_reports

create_custom_report