marketing-automation-mcp ist ein Python-MCP-Server für deterministisches Kampagnen-Reporting, anbietergestützte Budgetoptimierung, Anzeigentext-Generierung und Zielgruppensegmentierung (nur für Demo-Zwecke).
Dieses Repository bevorzugt nun Reproduzierbarkeit gegenüber Ad-hoc-Einrichtungen:
Unterstütztes Python: 3.12 und 3.13
Lokales Bootstrap: uv
Primärer MCP-Transport: stdio
Lokales Python 3.14.x wird als Kompatibilitätsarbeit behandelt, nicht als unterstützte Basislinie
Der öffentliche MCP-Vertrag in diesem Repository ist bewusst eng gefasst:
generate_campaign_report
optimize_campaign_budget
create_campaign_copy
analyze_audience_segments
Nur diese vier Tools sind heute Teil der unterstützten Server-Oberfläche. Andere Module unter src/tools/ existieren als interne oder angestrebte Codepfade und sollten nicht als produktive MCP-Funktionen behandelt werden.
Related MCP server: Google Ads MCP Server
DEMO_MODE=true
Gibt deterministische Beispieldaten für Demos und Vertragstests zurück.
DEMO_MODE=false
Verwendet echte Plattform-Anmeldedaten und den ausgewählten KI-Anbieter.
Fehlende Live-Abhängigkeiten geben strukturierte blocked-Antworten anstelle von erfundenen Ausgaben zurück.
uv sync --python 3.13 --extra dev
cp .env.example .env
uv run python -m compileall src tests dashboard
uv run pytestFalls Sie ein pip-Fallback benötigen:
python3.13 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"Starten Sie den MCP-Server in seinem unterstützten Transportmodus:
uv run python -m src.serverDer Server dokumentiert und unterstützt derzeit nur den stdio-Transport.
Konfiguration für Claude Desktop:
{
"mcpServers": {
"marketing-automation": {
"command": "uv",
"args": ["run", "python", "-m", "src.server"],
"cwd": "/absolute/path/to/Marketing-Automation-MCP-Server"
}
}
}cp .env.example .envMinimale nützliche Konfigurationen:
Nur Demo-Modus:
DEMO_MODE=true
Live-Reporting und -Optimierung:
DEMO_MODE=false
ein oder mehrere Plattform-Anmeldedatensätze
Live-Anzeigentext-Generierung:
DEMO_MODE=false
AI_PROVIDER=openai
OPENAI_API_KEY=...
AI_OPENAI_MODEL=gpt-5.4
Optionale Anbieter-Umgebungsvariablen:
ANTHROPIC_API_KEY, ANTHROPIC_MODEL
GEMINI_API_KEY, GEMINI_MODEL
Für stabiles Live-Verhalten setzen Sie:
SECRET_KEY
ENCRYPTION_KEY
Wenn ENCRYPTION_KEY fehlt, ist die API-Schlüssel-Verschlüsselung für diesen Prozess deaktiviert und der Server protokolliert eine Warnung.
Jede Tool-Antwort enthält diese Felder auf oberster Ebene:
{
"status": "ok | blocked",
"mode": "demo | live",
"blocked_reason": "optional string",
"warnings": []
}Siehe den vollständigen Vertrag in docs/api/README.md.
Live-Reporting- und Optimierungsabläufe können interne Audit-Datensätze in der konfigurierten Datenbank speichern:
Reporting-Abläufe können normalisierte Kampagnen-Snapshots speichern
Optimierungsabläufe können die Historie von KI-Entscheidungen speichern
Diese Schreibvorgänge sind interne Seiteneffekte für Beobachtbarkeit und Replay-Sicherheit. Sie sind nicht Teil des öffentlichen MCP-Antwortvertrags.
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 .Analyze audience segments in deterministic demo mode or return a structured live-mode block.
| Name | Required | Description | Default |
|---|---|---|---|
| contact_list_id | Yes | ||
| criteria | Yes | ||
| min_segment_size | No | ||
| max_segments | No | ||
| include_recommendations | No | ||
| analyze_overlap | No |
| 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 |
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.
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.
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.
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.
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.
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.
Generate campaign copy variants through the configured AI provider or deterministic demo mode.
| 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 |
| 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 |
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.
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.
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.
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.
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.
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 performance reports using live platform data or deterministic demo mode.
| Name | Required | Description | Default |
|---|---|---|---|
| campaign_ids | Yes | ||
| date_range | Yes | ||
| metrics | Yes | ||
| format | No | json | |
| include_charts | No | ||
| group_by | No | campaign |
| 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 |
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.
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.
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.
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.
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.
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.
Reallocate campaign budget using live platform metrics and provider-backed optimization logic.
| Name | Required | Description | Default |
|---|---|---|---|
| campaign_ids | Yes | ||
| total_budget | Yes | ||
| optimization_goal | No | maximize_roi | |
| constraints | No | ||
| historical_days | No | ||
| include_projections | No |
| 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 |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.1.0analyze_audience_segmentscreate_campaign_copygenerate_campaign_reportoptimize_campaign_budgetScored across 4 tools
Each tool targets a distinct marketing function: audience analysis, copy creation, reporting, and budget optimization. There is no overlap in purpose.
All tool names follow a consistent verb_noun pattern (analyze_*, create_*, generate_*, optimize_*), making them predictable and easy to distinguish.
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.
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.
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