MCP server for Cursor that leverages Gemini's much larger context window to enhance the capabilities of the AI tools
MCP server for Cursor that leverages Gemini's much larger context window to enhance the capabilities of the AI tools
A powerful MCP (Model Context Protocol) server implementation that leverages Gemini's capabilities for context management and caching. This server maximizes the value of Gemini's 2M token context window while providing tools for efficient caching of large contexts.
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Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| generate_text | Generate text with context |
| get_context | Get current context for a session |
| clear_context | Clear session context |
| add_context | Add specific context entries |
| search_context | Find relevant context semantically |
| mcp_gemini_context_create_cache | Create a cache for large contexts |
| mcp_gemini_context_generate_with_cache | Generate with cached context |
| mcp_gemini_context_list_caches | List all available caches |
| mcp_gemini_context_update_cache_ttl | Update cache TTL |
| mcp_gemini_context_delete_cache | Delete a cache |
| Variable | Description | Required |
|---|---|---|
| GEMINI_API_KEY | Credential the server authenticates with. | Yes |
Read, search and edit files inside allowed directories with the Model Context Protocol reference Filesystem server.
Retrieve version-aware library documentation and code examples with Context7 MCP, using resolve-library-id and query-docs.
MCP (Model Context Protocol) server for OpenSearch integration which is inspired by **[elasticsearch-mcp-server](https://github.com/cr7258/elasticsear
聚合55+平台热门榜单数据的AI工具，支持微博、知乎、B站、GitHub等平台。适用于LLM/RAG场景。
If you want to reference this research project in your work, please cite our paper:
Multiplayer chess platform for humans and AI agents with 3D visualization