Ben
371806488d
- Core architecture from zen-mcp-server - OpenRouter and Gemini provider configuration - Content variant generator tool (first marketing tool) - Chat tool for marketing strategy - Version and model listing tools - Configuration system with .env support - Logging infrastructure - Ready for Claude Desktop integration
107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
"""
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Configuration and constants for Zen-Marketing MCP Server
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This module centralizes all configuration settings for the Zen-Marketing MCP Server.
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It defines model configurations, token limits, temperature defaults, and other
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constants used throughout the application.
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Configuration values can be overridden by environment variables where appropriate.
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"""
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import os
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# Version and metadata
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__version__ = "0.1.0"
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__updated__ = "2025-11-07"
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__author__ = "Ben (based on Zen MCP Server by Fahad Gilani)"
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# Model configuration
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# DEFAULT_MODEL: The default model used for all AI operations
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# Can be overridden by setting DEFAULT_MODEL environment variable
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DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "google/gemini-2.5-pro-latest")
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# Fast model for quick operations (variations, subject lines)
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FAST_MODEL = os.getenv("FAST_MODEL", "google/gemini-2.5-flash-preview-09-2025")
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# Creative model for content generation
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CREATIVE_MODEL = os.getenv("CREATIVE_MODEL", "minimax/minimax-m2")
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# Auto mode detection - when DEFAULT_MODEL is "auto", Claude picks the model
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IS_AUTO_MODE = DEFAULT_MODEL.lower() == "auto"
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# Temperature defaults for different content types
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# Temperature controls the randomness/creativity of model responses
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# Lower values (0.0-0.3) produce more deterministic, focused responses
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# Higher values (0.7-1.0) produce more creative, varied responses
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# TEMPERATURE_PRECISION: Used for fact-checking and technical verification
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TEMPERATURE_PRECISION = 0.2 # For factcheck, technical verification
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# TEMPERATURE_ANALYTICAL: Used for style enforcement and SEO optimization
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TEMPERATURE_ANALYTICAL = 0.3 # For styleguide, seooptimize, voiceanalysis
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# TEMPERATURE_BALANCED: Used for strategic planning
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TEMPERATURE_BALANCED = 0.5 # For guestedit, linkstrategy, campaignmap
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# TEMPERATURE_CREATIVE: Used for content variation and adaptation
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TEMPERATURE_CREATIVE = 0.7 # For platformadapt
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# TEMPERATURE_HIGHLY_CREATIVE: Used for bulk variation generation
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TEMPERATURE_HIGHLY_CREATIVE = 0.8 # For contentvariant, subjectlines
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# Thinking Mode Defaults
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DEFAULT_THINKING_MODE_THINKDEEP = os.getenv("DEFAULT_THINKING_MODE_THINKDEEP", "high")
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# MCP Protocol Transport Limits
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def _calculate_mcp_prompt_limit() -> int:
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"""
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Calculate MCP prompt size limit based on MAX_MCP_OUTPUT_TOKENS environment variable.
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Returns:
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Maximum character count for user input prompts
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"""
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max_tokens_str = os.getenv("MAX_MCP_OUTPUT_TOKENS")
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if max_tokens_str:
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try:
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max_tokens = int(max_tokens_str)
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# Allocate 60% of tokens for input, convert to characters (~4 chars per token)
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input_token_budget = int(max_tokens * 0.6)
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character_limit = input_token_budget * 4
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return character_limit
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except (ValueError, TypeError):
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pass
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# Default fallback: 60,000 characters
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return 60_000
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MCP_PROMPT_SIZE_LIMIT = _calculate_mcp_prompt_limit()
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# Language/Locale Configuration
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LOCALE = os.getenv("LOCALE", "")
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# Platform character limits
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PLATFORM_LIMITS = {
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"twitter": 280,
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"bluesky": 300,
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"linkedin": 3000,
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"linkedin_optimal": 1300,
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"instagram": 2200,
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"facebook": 500, # Optimal length
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"email_subject": 60,
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"email_preview": 100,
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"meta_description": 156,
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"page_title": 60,
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}
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# Web search configuration
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ENABLE_WEB_SEARCH = os.getenv("ENABLE_WEB_SEARCH", "true").lower() == "true"
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# Tool disabling
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# Comma-separated list of tools to disable
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DISABLED_TOOLS_STR = os.getenv("DISABLED_TOOLS", "")
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DISABLED_TOOLS = set(tool.strip() for tool in DISABLED_TOOLS_STR.split(",") if tool.strip())
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# Logging configuration
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LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
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