148 lines
7.7 KiB
Python
148 lines
7.7 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__ = "5.1.3"
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# Last update date in ISO format
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__updated__ = "2025-06-19"
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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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#
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# IMPORTANT: These are the built-in natively supported models:
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# - When GEMINI_API_KEY is set: Enables "flash", "pro" (and their full names)
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# - When OPENAI_API_KEY is set: Enables "o3", "o3mini", "o4-mini", "o4-mini-high"
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# - When both are set: All models below are available
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# - When neither is set but OpenRouter/Custom API is configured: These model
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# aliases will automatically map to equivalent models via the proxy provider
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#
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# In auto mode (DEFAULT_MODEL=auto), Claude will see these descriptions and
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# intelligently select the best model for each task. The descriptions appear
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# in the tool schema to guide Claude's selection based on task requirements.
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MODEL_CAPABILITIES_DESC = {
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# Gemini models - Available when GEMINI_API_KEY is configured
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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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# OpenAI models - Available when OPENAI_API_KEY is configured
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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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"o3-pro": "Professional-grade reasoning (200K context) - EXTREMELY EXPENSIVE: Only for the most complex problems requiring universe-scale complexity analysis OR when the user explicitly asks for this model. Use sparingly for critical architectural decisions or exceptionally complex debugging that other models cannot handle.",
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"o4-mini": "Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
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"o4-mini-high": "Enhanced O4 mini (200K context) - Higher reasoning effort for complex tasks",
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# X.AI GROK models - Available when XAI_API_KEY is configured
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"grok": "GROK-3 (131K context) - Advanced reasoning model from X.AI, excellent for complex analysis",
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"grok-3": "GROK-3 (131K context) - Advanced reasoning model from X.AI, excellent for complex analysis",
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"grok-3-fast": "GROK-3 Fast (131K context) - Higher performance variant, faster processing but more expensive",
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"grok3": "GROK-3 (131K context) - Advanced reasoning model from X.AI, excellent for complex analysis",
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"grokfast": "GROK-3 Fast (131K context) - Higher performance variant, faster processing but more expensive",
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# Full model names also supported (for explicit specification)
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"gemini-2.5-flash": "Ultra-fast (1M context) - Quick analysis, simple queries, rapid iterations",
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"gemini-2.5-pro": ("Deep reasoning + thinking mode (1M context) - Complex problems, architecture, deep analysis"),
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}
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# OpenRouter/Custom API Fallback Behavior:
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# When only OpenRouter or Custom API is configured (no native API keys), these
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# model aliases automatically map to equivalent models through the proxy:
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# - "flash" → "google/gemini-2.5-flash" (via OpenRouter)
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# - "pro" → "google/gemini-2.5-pro" (via OpenRouter)
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# - "o3" → "openai/o3" (via OpenRouter)
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# - "o3mini" → "openai/o3-mini" (via OpenRouter)
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# - "o4-mini" → "openai/o4-mini" (via OpenRouter)
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# - "o4-mini-high" → "openai/o4-mini-high" (via OpenRouter)
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#
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# This ensures the same model names work regardless of which provider is configured.
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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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# Consensus Tool Defaults
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# Consensus timeout and rate limiting settings
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DEFAULT_CONSENSUS_TIMEOUT = 120.0 # 2 minutes per model
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DEFAULT_CONSENSUS_MAX_INSTANCES_PER_COMBINATION = 2
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# NOTE: Consensus tool now uses sequential processing for MCP compatibility
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# Concurrent processing was removed to avoid async pattern violations
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# MCP Protocol Transport Limits
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#
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# IMPORTANT: This limit ONLY applies to the Claude CLI ↔ MCP Server transport boundary.
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# It does NOT limit internal MCP Server operations like system prompts, file embeddings,
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# conversation history, or content sent to external models (Gemini/O3/OpenRouter).
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#
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# MCP Protocol Architecture:
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# Claude CLI ←→ MCP Server ←→ External Model (Gemini/O3/etc.)
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# ↑ ↑
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# │ │
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# MCP transport Internal processing
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# (25K token limit) (No MCP limit - can be 1M+ tokens)
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#
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# MCP_PROMPT_SIZE_LIMIT: Maximum character size for USER INPUT crossing MCP transport
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# The MCP protocol has a combined request+response limit of ~25K tokens total.
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# To ensure adequate space for MCP Server → Claude CLI responses, we limit user input
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# to 50K characters (roughly ~10-12K tokens). Larger user prompts must be sent
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# as prompt.txt files to bypass MCP's transport constraints.
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#
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# What IS limited by this constant:
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# - request.prompt field content (user input from Claude CLI)
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# - prompt.txt file content (alternative user input method)
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# - Any other direct user input fields
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#
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# What is NOT limited by this constant:
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# - System prompts added internally by tools
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# - File content embedded by tools
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# - Conversation history loaded from storage
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# - Web search instructions or other internal additions
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# - Complete prompts sent to external models (managed by model-specific token limits)
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#
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# This ensures MCP transport stays within protocol limits while allowing internal
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# processing to use full model context windows (200K-1M+ tokens).
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MCP_PROMPT_SIZE_LIMIT = 50_000 # 50K characters (user input only)
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# Threading configuration
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# Simple in-memory conversation threading for stateless MCP environment
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# Conversations persist only during the Claude session
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