cf0b3f7f44
- Add use_websearch parameter to base ToolRequest class - All tools now inherit web search capability automatically - Enhanced description emphasizes brainstorming and architectural design - Add get_websearch_instruction() helper method to base class - Update all tools to include web search in their prompts when enabled - Update README documentation with web search examples - Fix linting issues (ruff, black formatting) Web search is particularly useful for: - Brainstorming sessions and architectural design discussions - Exploring industry best practices - Working with specific frameworks/technologies - Researching solutions to complex problems - Getting current documentation and community insights 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
52 lines
2.4 KiB
Python
52 lines
2.4 KiB
Python
"""
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Configuration and constants for Gemini MCP Server
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This module centralizes all configuration settings for the Gemini 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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# Version and metadata
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# These values are used in server responses and for tracking releases
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__version__ = "2.11.1" # Semantic versioning: MAJOR.MINOR.PATCH
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__updated__ = "2025-06-10" # Last update date in ISO format
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__author__ = "Fahad Gilani" # Primary maintainer
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# Model configuration
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# GEMINI_MODEL: The Gemini 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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GEMINI_MODEL = "gemini-2.5-pro-preview-06-05"
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# MAX_CONTEXT_TOKENS: Maximum number of tokens that can be included in a single request
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# This limit includes both the prompt and expected response
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# Gemini Pro models support up to 1M tokens, but practical usage should reserve
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# space for the model's response (typically 50K-100K tokens reserved)
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MAX_CONTEXT_TOKENS = 1_000_000 # 1M tokens for Gemini Pro
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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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# 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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