eb388ab2f2
Encourage Claude to pick the best model for the job automatically in auto mode Lots of new tests to ensure automatic model picking works reliably based on user preference or when a matching model is not found or ambiguous Improved error reporting when bogus model is requested and is not configured or available
88 lines
4.0 KiB
Python
88 lines
4.0 KiB
Python
"""
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Configuration and constants for Zen MCP Server
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This module centralizes all configuration settings for the Zen 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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# These values are used in server responses and for tracking releases
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# IMPORTANT: This is the single source of truth for version and author info
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# Semantic versioning: MAJOR.MINOR.PATCH
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__version__ = "4.3.0"
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# Last update date in ISO format
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__updated__ = "2025-06-14"
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# Primary maintainer
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__author__ = "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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# This should be a stable, high-performance model suitable for code analysis
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# Can be overridden by setting DEFAULT_MODEL environment variable
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# Special value "auto" means Claude should pick the best model for each task
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DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "auto")
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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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# Model capabilities descriptions for auto mode
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# These help Claude choose the best model for each task
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MODEL_CAPABILITIES_DESC = {
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"flash": "Ultra-fast (1M context) - Quick analysis, simple queries, rapid iterations",
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"pro": "Deep reasoning + thinking mode (1M context) - Complex problems, architecture, deep analysis",
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"o3": "Strong reasoning (200K context) - Logical problems, code generation, systematic analysis",
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"o3-mini": "Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
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# Full model names also supported
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"gemini-2.5-flash-preview-05-20": "Ultra-fast (1M context) - Quick analysis, simple queries, rapid iterations",
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"gemini-2.5-pro-preview-06-05": (
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"Deep reasoning + thinking mode (1M context) - Complex problems, architecture, deep analysis"
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),
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}
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# Note: When only OpenRouter is configured, these model aliases automatically map to equivalent models:
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# - "flash" → "google/gemini-2.5-flash-preview-05-20"
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# - "pro" → "google/gemini-2.5-pro-preview-06-05"
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# - "o3" → "openai/gpt-4o"
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# - "o3-mini" → "openai/gpt-4o-mini"
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# Temperature defaults for different tool 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_ANALYTICAL: Used for tasks requiring precision and consistency
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# Ideal for code review, debugging, and error analysis where accuracy is critical
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TEMPERATURE_ANALYTICAL = 0.2 # For code review, debugging
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# TEMPERATURE_BALANCED: Middle ground for general conversations
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# Provides a good balance between consistency and helpful variety
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TEMPERATURE_BALANCED = 0.5 # For general chat
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# TEMPERATURE_CREATIVE: Higher temperature for exploratory tasks
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# Used when brainstorming, exploring alternatives, or architectural discussions
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TEMPERATURE_CREATIVE = 0.7 # For architecture, deep thinking
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# Thinking Mode Defaults
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# DEFAULT_THINKING_MODE_THINKDEEP: Default thinking depth for extended reasoning tool
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# Higher modes use more computational budget but provide deeper analysis
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DEFAULT_THINKING_MODE_THINKDEEP = os.getenv("DEFAULT_THINKING_MODE_THINKDEEP", "high")
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# MCP Protocol Limits
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# MCP_PROMPT_SIZE_LIMIT: Maximum character size for prompts sent directly through MCP
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# The MCP protocol has a combined request+response limit of ~25K tokens.
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# To ensure we have enough space for responses, we limit direct prompt input
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# to 50K characters (roughly ~10-12K tokens). Larger prompts must be sent
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# as files to bypass MCP's token constraints.
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MCP_PROMPT_SIZE_LIMIT = 50_000 # 50K characters
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# Threading configuration
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# Simple Redis-based conversation threading for stateless MCP environment
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# Set REDIS_URL environment variable to connect to your Redis instance
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REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/0")
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