$schema: ./schema/mcp-agent.config.schema.json
anthropic: null
default_search_server: filesystem
document_segmentation:
  enabled: false
  size_threshold_chars: 50000
execution_engine: asyncio
logger:
  level: info
  path_settings:
    path_pattern: logs/mcp-agent-{unique_id}.jsonl
    timestamp_format: '%Y%m%d_%H%M%S'
    unique_id: timestamp
  progress_display: false
  transports:
  - console
  - file
mcp:
  servers:
    bocha-mcp:
      args:
      - tools/bocha_search_server.py
      command: python3
      env:
        BOCHA_API_KEY: ''
        PYTHONPATH: .
    brave:
      # macos and linux should use this
      args:
      - -y
      - '@modelcontextprotocol/server-brave-search'
      command: npx

      # windows should use this
      # args:
      # # please use the correct path for your system
      # - C:/Users/LEGION/AppData/Roaming/npm/node_modules/@modelcontextprotocol/server-brave-search/dist/index.js
      # command: node
      env:
        BRAVE_API_KEY: ''
    filesystem:
      # macos and linux should use this
      # Note: "No valid root directories" warning is harmless - connection still works
      args:
      - -y
      - '@modelcontextprotocol/server-filesystem'
      - .
      - ./deepcode_lab
      command: npx

      # windows should use this
      # args:
      # # please use the correct path for your system
      # - C:/Users/LEGION/AppData/Roaming/npm/node_modules/@modelcontextprotocol/server-filesystem/dist/index.js
      # - .
      # command: node


    code-implementation:
      args:
      - tools/code_implementation_server.py
      command: python
      description: Paper code reproduction tool server - provides file operations,
        code execution, search and other functions
      env:
        PYTHONPATH: .
    code-reference-indexer:
      args:
      - tools/code_reference_indexer.py
      command: python
      description: Code reference indexer server - Provides intelligent code reference
        search from indexed repositories
      env:
        PYTHONPATH: .
    command-executor:
      args:
      - tools/command_executor.py
      command: python
      env:
        PYTHONPATH: .
    document-segmentation:
      args:
      - tools/document_segmentation_server.py
      command: python
      description: Document segmentation server - Provides intelligent document analysis
        and segmented reading to optimize token usage
      env:
        PYTHONPATH: .
    fetch:
      args:
      - mcp-server-fetch
      command: uvx
    file-downloader:
      args:
      - tools/pdf_downloader.py
      command: python
      env:
        PYTHONPATH: .
    github-downloader:
      args:
      - tools/git_command.py
      command: python
      env:
        PYTHONPATH: .
# LLM Provider Priority (选择使用哪个LLM / Choose which LLM to use)
# Options: "anthropic", "google", "openai"
# If not set or provider unavailable, will fallback to first available provider
llm_provider: "google"  # 设置为 "google", "anthropic", 或 "openai"

openai:
  base_max_tokens: 40000
  # default_model: google/gemini-2.5-pro
  default_model: anthropic/claude-sonnet-4.5
  # default_model: openai/gpt-oss-120b
  # default_model: deepseek/deepseek-v3.2-exp
  # default_model: moonshotai/kimi-k2-thinking
  reasoning_effort: low  # Only for thinking models
  max_tokens_policy: adaptive
  retry_max_tokens: 32768

# Provider configurations
# default_model is used by mcp_agent for planning/analysis phases
# implementation_model is used by code_implementation_workflow for code generation
google:
  default_model: "gemini-3-pro-preview"
  planning_model: "gemini-3-pro-preview"
  implementation_model: "gemini-2.5-flash"

anthropic:
  default_model: "claude-sonnet-4.5"
  planning_model: "claude-sonnet-4.5"
  implementation_model: "claude-sonnet-3.5"

openai:
  default_model: "o3-mini"
  planning_model: "o3-mini"
  implementation_model: "gpt-4o"

planning_mode: traditional