Marketic server is a full marketing operations platform that senses market signals, generates campaign briefs and creatives, tracks competitor moves, optimizes budgets, and manages CRM, publishing, and learning loops.
Signal intelligence & calibration: Track signals from sources like Polymarket, Google Trends, HN, Reddit, X, YouTube, RSS, and Facebook Ads; resolve outcomes; get Brier-score calibration reports per source.
Competitor intelligence: Search real Facebook Ads Library creatives, break down competitor ads (hook/offer/CTA/triggers), analyze positioning, and generate counter-angles.
Campaign strategy & execution: Build multi-channel campaigns, generate briefs, allocate budgets by margin/ROAS, create ad copy and social posts, and optionally launch ads via Meta/LinkedIn/Google/HubSpot/Salesforce.
Content & SEO: Generate SEO content, social posts across platforms, optimized hashtags, brand narratives, and design templates via brand tokens.
Prospecting & CRM: Discover and enrich prospects, auto-draft outreach, manage leads/deals/pipeline/activities, and track timelines.
Marketing platform integrations: Sync contacts, send campaigns/transactional messages, broadcast events, create segments, and pull unified dashboards across WebEngage, HubSpot, CleverTap, Braze, Mailchimp, Mixpanel, etc.
Publishing & UGC: Schedule posts, view upcoming content, curate and track user-generated content, and request repost permission.
Analytics & attribution: Multi-touch attribution models, UTM building/parsing, cost summaries, and CRM/prospect analytics.
Learning & audit: Log every AI action with reasoning and cost, retrieve audit trails, distill recurring patterns into a brand brain, and export learnings as markdown.
Master orchestration: Use ask_marketic as a plain-language router to any tool, signal_fanout for cross-source synthesis, ensemble_vote for model consensus, and run_workflow to chain multi-step marketing automations.
Provides access to the Facebook Ads Library for retrieving real competitor ad copy, delivery dates, and spend signals to analyze competitor hooks and offers.
Uses Google Trends India to discover rising queries for trend validation in the Indian market.
Scans Product Hunt to identify trending products and conversations relevant to market movements and narrative opportunities.
Scans Reddit for market discussions and signals to filter drama from genuine demand.
Fetches content from marketing media RSS feeds (e.g., ET Brand Equity, Afaqs, YourStory, Inc42, Mint) for trend validation and market intelligence.
Scans TikTok for trending content and signals to inform campaign planning and narrative tracking.
Ranks YouTube search results by view count to identify trending topics, especially in India mode.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Marketic Analyze competitor HubSpot in marketing automation"
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.
The best marketing teams don't guess what the market wants. They measure it.
Marketic watches the market 24/7. Every morning, you get a briefing with what actually matters — before your first coffee.
Not a dashboard. Not another tab. A prioritised brief with real signals, budget advice, competitor moves, and campaign plans — generated from 9 sources, measured against outcomes, and logged with reasoning.
(If you've ever opened 7 browser tabs at 8am wondering what actually happened — this is for you.)
YOUR MONDAY WITHOUT MARKETIC
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
7:45 Wake up. Coffee. Open laptop.
8:00 Open 7 browser tabs:
Google Trends "what's trending?"
Competitor ads "what launched?"
Mixpanel "why did last week drop?"
LinkedIn "what's everyone saying?"
Slack #mktg "did anyone reply?"
Email analyst "can you pull..."
HubSpot "which lead converted?"
9:30 Still don't know what matters.
10:00 Stand-up. Wing it.
YOUR MONDAY WITH MARKETIC
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
7:58 Notification: "Your briefing is ready"
8:00 Read 6 sentences:
① Kraken IPO buzz ($1.6M, 15% likely)
→ Ride fintech content today
② D2C skincare India rising (Google IN)
→ Publish before the spike peaks
③ Shark Tank India: 3x mentions this week
→ "As seen on Shark Tank" angle
⚠ IGNORED: Crypto drama ($4M, 6% likely)
→ Probability-adjusted — doesn't make the brief
📊 Budget: Email $9,200 · Social $5,800
→ Email margin 85% > Social 15%
🤖 Competitor X: new ad live since Tue
→ Counter-angle: clinical vs DIY
8:07 Coffee. Done.
8:08 Start the actual work.See: docs/assets/monday_problem.txt
Related MCP server: campaign_chest
Before | With Marketic | |
Briefing time | 175 min | 8 min |
Decisions informed/day | 2–3 (gut feel) | 4–5 (signal-backed) |
Time saved per week | 0 | 3.2 hrs |
See: docs/assets/comparison_table.txt
1. The Signal Brief (8:00 AM)
TOP SIGNALS
① Kraken IPO buzz — $1.6M bet · 15% probability · SCORE 72/100
→ Ride fintech narrative this week
② D2C skincare India — Rising Google searches in IN
→ Publish before spike peaks · 5-day window
③ Shark Tank India buzz — 3x mentions week-over-week
→ Angle: "as seen on Shark Tank" trust signal
⚠ IGNORED: Crypto crash talk ($4M volume, 6% odds)
→ Drama, not signal — probability-adjusted out2. The Campaign Brief (2:00 PM)
BRAND: GlowSkincare IN
BUDGET: Email $9,200 · Paid Social $5,800
(margin-adjusted: email 85% margin > social 15%)
COPY VARIANTS:
A: "Your skin deserves clinical-grade care"
B: "Dermatologist-approved. Now in India."
C: "Skincare that actually works in Delhi heat"
BEST TIMES (IN): 7-9AM · 12-1PM · 7-9PM
BRAND TOKENS: #6D0000 · DM Sans · @glowskinofficial3. The Competitor Alert (10:00 AM)
BRAND X — "Skin that survives Delhi heat"
Running since Aug 20 · ~$12-18K/week
Hook: survival · Offer: heat-proof · CTA: shop now
COUNTER-ANGLES (AI-generated):
① Clinical vs DIY: "Derms agree: $47 gel > $300 AC"
② Speed: "Results in 14 days or money back"
③ Transparency: "We list every active ingredient"See: docs/assets/sample_brief.txt
Most tools say "our signals are reliable." Marketic shows you:
SIGNAL CALIBRATION — Last 30 Days
════════════════════════════════════════════════════
Source Signals Brier Verdict
────────────────────────────────────────────────────
Polymarket 12 0.09 ✅ Well-calibrated
Google Trends 8 0.14 ✅ Reliable
Hacker News 6 0.21 ⚠️ Uncertain
Reddit 9 0.28 ⚠️ Needs data
Twitter / X 14 0.31 ⚠️ Needs data
Product Hunt 5 0.24 ⚠️ Needs data
────────────────────────────────────────────────────
Overall 54 0.19 📊 Honest
Random baseline — 0.25 ───── ─────
════════════════════════════════════════════════════
Brier score: 0.00 = perfect · 0.25 = random · Higher = worseWhat this means:
Polymarket signals: right ~9 out of 10 times
Twitter/X signals: 3 more weeks of data before we trust them
This is the first marketing tool that tells you how wrong it might be
See: docs/assets/calibration_table.txt See: docs/assets/calibration.mmd — pie chart
flowchart LR
subgraph SENSE["📡 SENSE"]
P[Polymarket]
G[Google Trends]
H[Hacker News]
R[Reddit]
X[X / Twitter]
Y[YouTube]
I[Indian RSS]
F[FB Ads Library]
end
subgraph THINK["🧠 THINK"]
EA[Ensemble AI]
SC[Scorecard]
BR[Budget Router]
AA[Ad Analyzer]
PS[Prospect Scout]
end
subgraph HANDOFF["📋 HANDOFF"]
BF[Brief JSON]
BT[Brand Tokens]
PW[Posting Windows]
end
subgraph LEARN["🔄 LEARN"]
AT[Audit Trail]
DS[Distillation]
BB[Brand Brain]
end
SENSE -->|"volume × P(YES)"| THINK
SENSE -->|"all signals"| SC
THINK -->|"scored signals"| EA
EA -->|"reasoned"| BR
EA -->|"ad insights"| AA
EA -->|"prospects"| PS
BR -->|"budget split"| BF
AA -->|"counter-angles"| BF
PS -->|"enriched leads"| BF
BF -->|"handoff"| HANDOFF
BT --> HANDOFF
PW --> HANDOFF
HANDOFF -->|"execute"| AT
AT -->|"patterns"| DS
DS -->|"approved rules"| BB
BB -->|"inject"| THINK
classDef sense fill:#6366f1,color:#fff,stroke:none
classDef think fill:#f59e0b,color:#fff,stroke:none
classDef handoff fill:#22c55e,color:#fff,stroke:none
classDef learn fill:#f43f5e,color:#fff,stroke:none
class P,G,H,R,X,Y,I,F sense
class EA,SC,BR,AA,PS think
class BF,BT,PW handoff
class AT,DS,BB learnSee: docs/assets/architecture.mmd
Two design decisions worth knowing:
Polymarket: volume × P(YES) — $2.1M at 4% = $84K effective, not $2.1M. Because 73.4% of all Polymarket markets resolve "No" historically. Raw volume overweights drama.
Budget: margin-adjusted, not vanity-ROAS — Email at 5× ROAS but 15% margin can lose to social at 1.5× ROAS but 85% margin. Marketic recommends where profit actually lands.
flowchart TB
subgraph GTM["🎯 GTM & Intel (20 tools)"]
A1[search_fb_ads] A2[analyze_competitor_ad]
A3[analyze_competitor] A4[analyze_positioning]
A5[breakdown_ad] A6[generate_narrative]
A7[discover_prospects] A8[signal_fanout]
A9[...]
end
subgraph CORE["⚙️ Core (12 tools)"]
B1[generate_creatives] B2[generate_social_posts]
B3[generate_seo_content] B4[build_campaign]
B5[optimize_budget] B6[launch_campaign_ad]
B7[generate_brief] B8[run_prospect_loop]
B9[distill_learnings] B10[ask_marketic]
end
subgraph DATA["📊 Data & Learning (13 tools)"]
C1[track_signal] C2[get_calibration_report]
C3[resolve_signal] C4[ensemble_vote]
C5[audit_log] C6[audit_get_log]
C7[get_cost_summary] C8[get_attribution]
C9[crm_leads/deals/pipeline/activities...]
end
subgraph PUB["🚀 Publishing & UGC (10 tools)"]
D1[schedule_content] D2[get_upcoming_posts]
D3[optimize_hashtags] D4[curate_ugc]
D5[request_ugc_permission] D6[track_ugc]
D7[render_template] D8[hub_*] D9[...]
end
classDef gtm fill:#6366f1,color:#fff,stroke:none
classDef core fill:#f59e0b,color:#fff,stroke:none
classDef data fill:#22c55e,color:#fff,stroke:none
classDef pub fill:#f43f5e,color:#fff,stroke:none
class A1,A2,A3,A4,A5,A6,A7,A8,A9 GTM
class B1,B2,B3,B4,B5,B6,B7,B8,B9,B10 CORE
class C1,C2,C3,C4,C5,C6,C7,C8,C9 DATA
class D1,D2,D3,D4,D5,D6,D7,D8,D9 PUBCategory | Highlights |
Signal intelligence | 9 sources: Polymarket · Google Trends IN · HN · Reddit · X · YouTube · Indian RSS · PH · FB Ads |
Competitor intel | FB Ads Library search · Ad breakdown (hook/offer/CTA) · Positioning analysis |
Calibration | Track signals → Resolve outcomes → Brier score per source |
Campaigns | Full-funnel brief · Margin-aware budget · Narrative framing |
Prospecting | ICP discovery (4 sources) + CRM enrichment loop |
Creative | Copy variants · Social posts · SEO — scored by predicted performance |
Publishing | Content calendar · Hashtag optimisation · Platform scheduling |
UGC | Discover · Curate · Request permission · Track reposts |
Design | Brand-as-data templates: one template, any brand |
CRM | Leads · Deals · Pipeline · Activities |
Analytics | 5-model attribution: first/last/linear/time-decay/data-driven |
Learning | Audit trail → Distillation → Brand brain (markdown) |
git clone https://github.com/Das-rebel/marketic
cd marketic && pip install -e .
python3 init_memory_db.py
# Your first briefing — no API keys needed for core loop
python3 daily_briefing.py "D2C skincare brand India"Optional Key | Adds |
| Real competitor ad data from Facebook Ads Library |
| ICP prospect discovery across Google/Twitter/Reddit/PH |
| Free local AI models for creative generation |
Everything self-hosted. Your competitor data, your pipeline, your budget logic — stays yours.
git clone https://github.com/Das-rebel/marketicWhat's inside a campaign brief | |
How the learning loop compounds over time | |
Architecture decisions and why | |
All 55 tools, all parameters | |
Visual assets: Mermaid diagrams + ASCII, regenerate with |
MIT © Subho Das
Deep-dive competitive analysis: positioning, messaging, ad strategy, audience targeting, strengths, weaknesses, and exploitable gaps.
Inputs: brand (required), category.
Deconstruct a competitor ad (image/video frame URL or local path) via VLM: hook, pacing, psychological triggers, CTA, counter-angles. Falls back to copy heuristics when no vision backend available. Use derive=true to aggregate multiple ads into a counter-brief for generate_creatives.
Inputs: batch, derive, caption, transcript, +1 more.
Analyze your brand's market positioning against competitors. Returns positioning map, differentiation strategy, and messaging framework.
Inputs: brand (required), product, industry.
Master router - one entry point for Marketic. Describe what you need in plain language (e.g. 'what's moving in AI markets', 'allocate my budget', 'deconstruct this competitor ad') and it routes to the right specialist tool(s). Use route_only=true to preview routing without executing.
Inputs: question (required), arguments, route_only.
Get cost summary by model and action type for a date range.
Inputs: brand_id, end_date, start_date.
Retrieve audit log entries with filtering by brand, action, and date range.
Inputs: limit, action, brand_id, end_date, +1 more.
Log an AI marketing action with full audit trail. Records model, cost, confidence, reasoning chain, and human approval status.
Inputs: action (required), cost, model, brand_id, +7 more.
Break down a competitor ad into its structural components: hook, offer, call-to-action, emotional triggers, pacing, and format. Works from a URL (uses Ollama vision model locally if available, falls back to cloud vision then heuristic parsing) or from raw ad copy text. Optionally enrich with brand context.
Inputs: ad_url_or_text (required), brand_name, analysis_depth.
Build a complete multi-channel campaign strategy with channel-specific tactics, budgets, and timelines.
Inputs: objective (required), campaign_name (required), channels, total_budget, +2 more.
Build a UTM-tagged URL for campaign tracking.
Inputs: medium (required), source (required), base_url (required), campaign (required), +2 more.
Collect marketing signals from Product Hunt, Hacker News, Twitter, Reddit for a brand.
Inputs: brand (required), days, sources.
Compare your product against multiple competitors. Returns feature comparison matrix, price comparison, and strategic recommendations.
Inputs: competitors (required), your_product (required).
Create a new deal/opportunity from a lead. Sets deal value, stage, and links to lead.
Inputs: name (required), stage, value, lead_id, +1 more.
Create a new CRM lead from marketing data. Stores lead with email, name, company, source, and scoring data.
Inputs: email (required), tags, phone, source, +4 more.
Get CRM dashboard with lead/deal pipeline metrics.
Inputs: no named parameters.
Get deal pipeline summary with values per stage.
Inputs: no named parameters.
Get timeline of all activities and stage changes for a lead or deal.
Inputs: entity_id (required).
Log an activity (call, email, meeting, note) on a lead or deal.
Inputs: entity_id (required), activity_type (required), notes, subject, +1 more.
Move a deal to a new pipeline stage. Updates probability and logs stage history.
Inputs: deal_id (required), new_stage (required).
Search CRM leads by name, email, or company.
Inputs: query (required), limit.
Curate user-generated content for a given hashtag. Discovers posts via hashtag monitoring, filters by aesthetic score, and returns a list sorted by combined relevance + aesthetic score.
Inputs: hashtag (required), limit, platform, min_aesthetic_score.
Promote recurring audit-trail patterns into brand learnings; optionally capture an explicit rule; export brain/.md markdown for human review.
Inputs: brand, category, capture_rule, export_brain, +1 more.
Run ensemble voting across multiple AI models. Selects optimal model tier based on task complexity. Returns consensus decision with confidence score.
Inputs: prompt (required), task_type (required), models, context.
Generate a self-contained campaign brief (handoff artifact) for any brand execution agent: positioning, budget split, posting windows, resolved BrandTokens, and execution contract. The agent can execute without calling back.
Inputs: objective (required), product_name (required), campaign_name (required), product_description (required), +9 more.
Generate ad copy variants across channels (Google, Meta, LinkedIn, etc.). Each variant includes headline, description, CTA, hooks, confidence score, and performance prediction.
Inputs: product_name (required), product_description (required), tone, channel, +4 more.
Generate brand narrative, stories, and messaging frameworks for marketing.
Inputs: brand (required), narrative_type (required), product, industry.
Generate SEO-optimized content including meta titles, descriptions, headers, and FAQs for target keywords.
Inputs: target_keyword (required), word_count, content_type, competitor_url.
Generate platform-specific social media posts (LinkedIn, X/Twitter, Instagram, Facebook). Supports threads, single posts, and multi-format content.
Inputs: topic (required), tone, format, length, +2 more.
Calculate multi-touch attribution across marketing channels using various models.
Inputs: channel_points (required), model.
Get the signal calibration report: number of predictions, Brier score (lower=better), resolved vs pending breakdown, and per-source accuracy. Shows whether Marketic's signal sources are reliable.
Inputs: source, end_date, start_date.
Get all scheduled posts for the upcoming N days from the content calendar.
Inputs: days, limit, platform.
Track an event across ALL connected marketing platforms simultaneously.
Inputs: contact_id (required), event_name (required), revenue, properties.
Create an audience segment across ALL connected marketing platforms.
Inputs: name (required), conditions, description.
Get unified analytics dashboard across ALL connected marketing platforms. Aggregates metrics from WebEngage, CleverTap, Mixpanel, HubSpot, Braze, etc.
Inputs: no named parameters.
Check health status of ALL connected marketing platforms. Returns connection status and capabilities.
Inputs: no named parameters.
List all supported marketing platforms and their capabilities. Returns platform features, supported channels, and connection status.
Inputs: no named parameters.
Search for prospects using Clay data enrichment. Returns enriched company/contact data including title, company size, tech stack, funding, and social profiles.
Inputs: query (required), limit.
Send a marketing campaign via the best available platform. Routes to Mailchimp, HubSpot, WebEngage, Braze, or CleverTap.
Inputs: campaign_name (required), channel, subject, content_html, +2 more.
Send a transactional message (single contact) via the best platform for the channel.
Inputs: body (required), contact_id (required), title, channel, +1 more.
Sync a contact to ALL connected marketing platforms. Creates/updates profile across WebEngage, HubSpot, CleverTap, Braze, Mailchimp, etc.
Inputs: email (required), contact_id (required), tags, phone, +5 more.
⚠️ REQUIRES APPROVAL. Launch a campaign ad via Composio integration (Meta, LinkedIn, Google Ads).
Inputs: platform (required), campaign_name (required), targeting, ad_creative, +1 more.
Optimize budget allocation across marketing channels based on historical ROAS data.
Inputs: total_budget (required), current_allocation (required), channel_performance (required), strategy.
Get optimized hashtags for a social media post. Returns a mix of trending and content-specific hashtags, respecting platform limits.
Inputs: content_text (required), limit, platform.
Extract UTM parameters from a URL.
Inputs: url (required).
Render a brand design template to a Paper MCP script or JSON layer spec. Accepts a template name, brand tokens (name, primary, background, accent, secondary, font, handle, tagline), and optional content overrides. Returns HTML/layers or a placeholders list if required content is missing.
Inputs: brand (required), template_name (required), content_overrides.
Request permission from a UGC creator to repost their content. Sends a DM template (English or Indonesian) via platform API.
Inputs: platform (required), content_url (required), message.
Resolve a previously tracked signal with its actual outcome (YES/NO/PARTIAL). Used to close the calibration loop and improve future signal quality.
Inputs: signal_id (required), actual_outcome (required), notes.
Signal-driven prospecting (JoeCRM pattern): discover prospects matching a niche, enrich with live market signals, auto-draft personalized outreach, insert scored leads into CRM. Degrades to signal-derived prospects when Serper key absent.
Inputs: niche_query (required), limit, market_query.
Execute a multi-step marketing workflow. Chain operations: sync_contact → create_segment → send_campaign.
Inputs: steps (required), first_step (required), workflow_id (required), name.
Schedule a social media post to a platform via Postiz or direct API. Takes platform, content text, optional media URLs, scheduled time (ISO datetime), and hashtags. Falls back to PostizPublisher.publish_post() when platform=postiz.
Inputs: platform (required), content_text (required), hashtags, media_urls, +1 more.
Search Facebook Ads Library for REAL competitor ad creatives, copy and delivery data (ground truth, not VLM guessing). Requires FB_ACCESS_TOKEN env var.
Inputs: brand_name (required), limit, country.
Parallel multi-source signal search (Product Hunt, HN, Twitter, Reddit, Polymarket) with cross-source engagement normalization. Returns one synthesized brief with consensus themes and money outliers.
Inputs: query, sources, limit_per_source.
Record a signal prediction for later calibration tracking. Once tracked, you can call resolve_signal when the outcome is known to measure prediction accuracy (Brier score). Used to build calibration over time.
Inputs: url (required), title (required), source (required), signal_type (required), +3 more.
Track UGC repost performance: reach, likes, comments, saves, and shares across platforms.
Inputs: platform (required), repost_id (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 54 tools
Most tools have clearly distinct purposes, with some overlap in competitor ad analysis (analyze_competitor_ad vs breakdown_ad) but descriptions differentiate them by use case and output. Signal-related tools (track_signal, collect_signals, signal_fanout) are also distinct in scope.
Tool naming is mostly snake_case with verb_noun patterns, but there are exceptions like 'ask_marketic', 'signal_fanout', and 'ensemble_vote' that break the pattern. Prefixes like hub_ and crm_ provide some consistency, but the mix of action verbs (get, create, launch, run) and occasional non-verb nouns is inconsistent.
With 54 tools, the server is extremely heavy. This far exceeds the typical well-scoped range (3-15) and even the 'too many' threshold of 25+. The vast count suggests a broad aggregation of marketing capabilities that would likely overwhelm an agent and cause selection difficulty.
The tool surface covers an impressively wide range of marketing functions: scheduling, UGC, CRM, analytics, competitor analysis, content generation, campaign management, and calibration. While some niche areas like A/B testing are absent, the core lifecycle of marketing operations (plan, create, launch, track, learn) is well covered.
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