Which integrations are available for this server?

Enables Facebook Ads campaign management and audience insights, allowing creation and optimization of ad campaigns with intelligent audience segmentation. Provides full API integration with OAuth2 authentication for Google Ads campaign management, enabling AI-powered budget optimization, performance tracking, and automated campaign operations. Integrates with Google Analytics for performance tracking, attribution analysis, and real-time campaign monitoring with automated ROI calculation. Enables contact management, email campaign creation, and audience segmentation through Mailchimp's marketing automation platform. Leverages GPT-4 for AI-powered ad copy generation, intelligent budget allocation recommendations, campaign insights, and predictive ROI modeling. Provides email service integration for marketing campaign delivery and communication automation.

How do I use Marketing Automation MCP Server?

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., "@Marketing Automation MCP Server generate a performance report for my Q4 Google Ads campaign" 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.

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Marketing Automation MCP Server

by Mohit4022-cloud

Overview Schema Related Servers Score Discussions

Python

Hybrid

Marketing Automation MCP Server

marketing-automation-mcp is a Python MCP server for deterministic campaign reporting, provider-backed budget optimization, copy generation, and demo-only audience segmentation.

This repo now favors reproducibility over ad hoc setup:

Supported Python: 3.12 and 3.13
Local bootstrap: uv
Primary MCP transport: stdio
Local Python 3.14.x is treated as compatibility work, not the supported baseline

Current Scope

The public MCP contract in this repo is intentionally narrow:

generate_campaign_report
optimize_campaign_budget
create_campaign_copy
analyze_audience_segments

Only these four tools are part of the supported server surface today. Other modules under src/tools/ exist as internal or aspirational code paths and should not be treated as production MCP features.

Related MCP server: Google Ads MCP Server

Execution Modes

DEMO_MODE=true Returns deterministic sample data for demos and contract testing.
DEMO_MODE=false Uses real platform credentials and the selected AI provider. Missing live dependencies return structured blocked responses instead of fabricated output.

Clean Machine Setup

uv sync --python 3.13 --extra dev
cp .env.example .env
uv run python -m compileall src tests dashboard
uv run pytest

If you need a pip fallback:

python3.13 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"

Run The Server

Start the MCP server in its supported transport mode:

uv run python -m src.server

The server currently documents and supports stdio transport only.

Claude Desktop configuration:

{
  "mcpServers": {
    "marketing-automation": {
      "command": "uv",
      "args": ["run", "python", "-m", "src.server"],
      "cwd": "/absolute/path/to/Marketing-Automation-MCP-Server"
    }
  }
}

Configure

cp .env.example .env

Minimum useful configurations:

Demo mode only:
- DEMO_MODE=true
Live reporting and optimization:
- DEMO_MODE=false
- one or more platform credential sets
Live copy generation:
- DEMO_MODE=false
- AI_PROVIDER=openai
- OPENAI_API_KEY=...
- AI_OPENAI_MODEL=gpt-5.4

Optional provider env vars:

ANTHROPIC_API_KEY, ANTHROPIC_MODEL
GEMINI_API_KEY, GEMINI_MODEL

For stable live behavior, set:

SECRET_KEY
ENCRYPTION_KEY

If ENCRYPTION_KEY is missing, API-key encryption is disabled for that process and the server logs a warning.

Tool Contract

Every tool response includes these top-level fields:

{
  "status": "ok | blocked",
  "mode": "demo | live",
  "blocked_reason": "optional string",
  "warnings": []
}

See the full contract in docs/api/README.md.

Internal Write Side Effects

Live report and optimization flows may persist internal audit records to the configured database:

report flows can persist normalized campaign snapshots
optimization flows can persist AI decision history

These writes are internal side effects for observability and replay safety. They are not part of the public MCP response contract.

Validation Commands

uv run python -m compileall src tests dashboard
uv run pytest
uv run python -c "import src.server, src.cli, src.ai_engine, src.performance; print('imports ok')"
docker build -t marketing-automation-mcp:latest .

Documentation

Available Tools

4 tools

analyze_audience_segmentsC

Analyze audience segments in deterministic demo mode or return a structured live-mode block.

ParametersJSON Schema

Name	Required	Description	Default
`contact_list_id`	Yes
`criteria`	Yes
`min_segment_size`	No
`max_segments`	No
`include_recommendations`	No
`analyze_overlap`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`status`	No
`mode`	No
`blocked_reason`	No
`warnings`	No
`analysis_id`	Yes
`total_contacts`	Yes
`segments`	Yes
`uncategorized_count`	Yes
`overlaps`	No
`recommendations`	Yes
`insights`	Yes
`created_at`	Yes

TDQS

C2.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description fails to disclose behavioral traits such as read/write nature, side effects, or mode implications. 'Deterministic demo mode' hints at reproducibility but is not explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, but it is under-specified rather than concise. It omits critical details that would justify its brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 6 parameters, no annotations, and no explanation of the output schema, the description is insufficient for an agent to correctly invoke the tool. The live-mode vs. demo distinction is mentioned but not fleshed out.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage for its 6 parameters, and the description does not mention or clarify any parameters. It adds no value beyond the schema's field names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes audience segments and mentions two modes (deterministic demo vs. live), which distinguishes it from sibling tools like create_campaign_copy or generate_campaign_report. However, it lacks specificity on what the analysis produces.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives; no prerequisites or context for the modes. The description only states the action without any conditional advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_campaign_copyB

Generate campaign copy variants through the configured AI provider or deterministic demo mode.

ParametersJSON Schema

Name	Required	Description	Default
`product_name`	Yes
`product_description`	Yes
`target_audience`	Yes
`tone`	Yes
`copy_type`	Yes
`variants_count`	No
`keywords`	No
`max_length`	No
`call_to_action`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`status`	No
`mode`	No
`blocked_reason`	No
`warnings`	No
`copy_generation_id`	Yes
`copy_type`	Yes
`variants`	Yes
`tone`	Yes
`target_audience`	Yes
`keywords_used`	Yes
`best_variant_id`	Yes
`generation_metadata`	Yes

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions two modes (AI provider and deterministic demo) but fails to disclose potential side effects, failure modes, authentication requirements, rate limits, or any constraints. This is a significant gap for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently communicates the core purpose and key behavioral distinction (two modes). There is no fluff; every word earns its place. The structure effectively front-loads the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 parameters, 5 required) and the lack of schema descriptions and annotations, the description should provide more contextual guidance. Although an output schema exists (so return values need no explanation), the lack of parameter semantics and behavioral detail makes the description incomplete for an agent to use the tool confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, meaning no parameter documentation exists in the schema. The tool description does not provide any additional meaning for the 9 parameters (e.g., expected formats, examples, or relationships). The only hint comes from parameter titles, which is insufficient for correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('generate'), the resource ('campaign copy variants'), and the two operational modes (AI provider or deterministic demo mode). It distinguishes well from sibling tools like analyze_audience_segments, generate_campaign_report, and optimize_campaign_budget, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for generating copy, but it does not explicitly state when to use it versus alternatives. There is no guidance on prerequisites, exclusions, or conditions that would help an agent decide between this tool and others. The context suggests usage, but explicit guidelines are missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_campaign_reportB

Generate campaign performance reports using live platform data or deterministic demo mode.

ParametersJSON Schema

Name	Required	Default
`campaign_ids`	Yes
`date_range`	Yes
`metrics`	Yes
`format`	No	json
`include_charts`	No
`group_by`	No	campaign

Output Schema

ParametersJSON Schema

Name	Required	Description
`status`	No
`mode`	No
`blocked_reason`	No
`warnings`	No
`report_id`	Yes
`generated_at`	Yes
`date_range`	Yes
`campaigns`	Yes
`summary`	Yes
`charts`	No
`format`	Yes
`download_url`	No

TDQS

B3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that the tool can use live data or a deterministic demo mode, which is a behavioral trait. However, with no annotations, it fails to disclose other important aspects like permissions, side effects, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that efficiently conveys the core action. It is concise, though it could briefly describe parameters without losing brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 parameters, nested objects, enums), the description is too sparse. It does not explain demo mode, report formats, or group_by behavior. Output schema exists but does not justify the lack of parameter context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage and the tool description provides no explanations for any of the 6 parameters, including required ones like campaign_ids, date_range, and metrics. This forces the agent to infer meaning from schemas alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates campaign performance reports and distinguishes it from siblings by specifying data sources (live vs demo mode). The verb 'generate' and resource 'campaign performance reports' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs alternatives like analyze_audience_segments. There is no mention of prerequisites or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

optimize_campaign_budgetC

Reallocate campaign budget using live platform metrics and provider-backed optimization logic.

ParametersJSON Schema

Name	Required	Default
`campaign_ids`	Yes
`total_budget`	Yes
`optimization_goal`	No	maximize_roi
`constraints`	No
`historical_days`	No
`include_projections`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`status`	No
`mode`	No
`blocked_reason`	No
`warnings`	No
`optimization_id`	Yes
`total_budget`	Yes
`optimization_goal`	Yes
`allocations`	Yes
`projected_improvement`	Yes
`confidence_score`	Yes
`recommendations`	Yes

TDQS

C2.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It only states the high-level purpose but omits details like budget modification being destructive, required permissions, or impact of reallocation. Minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single 10-word sentence, concise but too brief for a tool with 6 parameters. It front-loads the action but lacks essential context, making it minimally adequate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, no schema descriptions, no annotations, and an output schema not explained, the description is severely incomplete. An agent cannot infer inputs, outputs, or behavioral effects from this alone.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does not mention any parameters (campaign_ids, total_budget, etc.) nor explain their meaning. Only the optimization_goal enum is partially documented in the schema itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (reallocate campaign budget) and the method (live platform metrics, optimization logic). It distinguishes from siblings like analyze_audience_segments or create_campaign_copy, which are unrelated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., when to manually adjust budgets). No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

4 tool updatesv1.1.0May 6, 2026
- First observedanalyze_audience_segments
- First observedcreate_campaign_copy
- First observedgenerate_campaign_report
- First observedoptimize_campaign_budget

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0

Disambiguation5/5

Each tool targets a distinct marketing function: audience analysis, copy creation, reporting, and budget optimization. There is no overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (analyze_*, create_*, generate_*, optimize_*), making them predictable and easy to distinguish.

Tool Count4/5

With 4 tools, the set is slightly lean but covers core marketing automation tasks. It does not feel overly sparse or bloated for a focused server.

Completeness3/5

The tools cover analysis, copy, reporting, and optimization, but lack common features like campaign lifecycle management (create, schedule, pause) or list/segment management, leaving notable gaps.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

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Marketing Automation MCP Server

Current Scope

Execution Modes

Clean Machine Setup

Run The Server

Configure

Tool Contract

Internal Write Side Effects

Validation Commands

Documentation

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

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Appeared in Searches

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MCP directory API