Highlights
- Pluggable LLM provider layer (
gee_mcp.server.llm): Google (Gemini Developer / Vertex AI), Anthropic, and OpenAI behind a commonBaseLLMinterface. - New
@register_llmdecorator +from_envclassmethod per provider — adding a provider is one decorated subclass. - Optional on-disk response cache (
JSONFileCache) injected viaResponseCacheprotocol; default isNullCache. - Reasoning passthrough where supported (Anthropic adaptive thinking, OpenAI reasoning summaries).
- New test module
tests/test_server/test_llm.py(24 tests, no network).
Breaking changes
gee_mcp.server.genaimodule removed. Import fromgee_mcp.server.llminstead (init_llm_client,BaseLLM,OpenAILLM,AnthropicLLM,GoogleLLM, …).LLM_PROVIDER(google|anthropic|openai) andLLM_NAMEenv vars are now required for the analysis / code-generation / coderun tools. Previously these defaulted implicitly to Gemini.- Provider-specific credentials must match
LLM_PROVIDER:OPENAI_API_KEY,ANTHROPIC_API_KEY, or one ofGEMINI_API_KEY/GOOGLE_API_KEY/VERTEXAI_PROJECT.
See updated .env.example and README.md for configuration.