Gemini MCP Server

#!/usr/bin/env node /** * Gemini MCP Server * * A drop-in replacement for Codex MCP that uses Google Gemini 3 Pro Preview. * Mirrors the exact interface: gemini (like codex) and gemini-reply (like codex-reply) */ import { Server } from "@modelcontextprotocol/sdk/server/index.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { CallToolRequestSchema, ListToolsRequestSchema, Tool, } from "@modelcontextprotocol/sdk/types.js"; import { GoogleGenAI, Content } from "@google/genai"; // Configuration - Gemini 3 Pro Preview (latest) const MODEL = process.env.GEMINI_MODEL || "gemini-3-pro-preview"; const API_KEY = process.env.GEMINI_API_KEY || process.env.GOOGLE_API_KEY; if (!API_KEY) { console.error("Error: GEMINI_API_KEY or GOOGLE_API_KEY environment variable required"); process.exit(1); } // Initialize Gemini client const ai = new GoogleGenAI({ apiKey: API_KEY }); // Session storage for multi-turn conversations interface ConversationSession { history: Content[]; createdAt: number; lastUsed: number; cwd?: string; } const sessions = new Map<string, ConversationSession>(); // Clean up old sessions (older than 1 hour) function cleanupSessions() { const oneHourAgo = Date.now() - 60 * 60 * 1000; for (const [id, session] of sessions.entries()) { if (session.lastUsed < oneHourAgo) { sessions.delete(id); } } } // Generate unique conversation ID function generateConversationId(): string { return `gemini-${Date.now()}-${Math.random().toString(36).substring(2, 9)}`; } // Build system instruction based on config function buildSystemInstruction(config: { cwd?: string; sandbox?: string; baseInstructions?: string; developerInstructions?: string; }): string { const parts: string[] = []; // Base instructions if (config.baseInstructions) { parts.push(config.baseInstructions); } else { parts.push(`You are an expert software engineer assistant powered by Gemini 3 Pro Preview. You help with code review, analysis, planning, and problem-solving. Provide clear, concise, and actionable responses. When reviewing code or plans, be thorough but practical.`); } // Working directory context if (config.cwd) { parts.push(`\nWorking directory: ${config.cwd}`); } // Sandbox mode context if (config.sandbox) { const sandboxDescriptions: Record<string, string> = { "read-only": "You are in read-only mode. You can analyze and review but cannot make changes.", "workspace-write": "You can read and write within the workspace directory.", "danger-full-access": "You have full access to read and write files.", }; if (sandboxDescriptions[config.sandbox]) { parts.push(`\nAccess level: ${sandboxDescriptions[config.sandbox]}`); } } // Developer instructions if (config.developerInstructions) { parts.push(`\nDeveloper Instructions:\n${config.developerInstructions}`); } return parts.join("\n"); } // Image generation models - Nano Banana (Gemini native image generation) // gemini-2.5-flash-image = Nano Banana (fast, cheap ~$0.04/image) // gemini-3-pro-image-preview = Nano Banana Pro (advanced, better text rendering) const IMAGE_MODEL_FAST = "gemini-2.5-flash-image"; const IMAGE_MODEL_PRO = "gemini-3-pro-image-preview"; // Keywords that trigger Nano Banana Pro (text-heavy, precision work) const PRO_KEYWORDS = [ "nano banana pro", "nanobanana pro", "pro model", "infographic", "diagram", "chart", "graph", "text", "typography", "font", "lettering", "writing", "slide", "presentation", "deck", "logo", "brand", "poster", "flyer", "banner", "document", "page", "layout", "high quality", "high-quality", "hq", "4k", "detailed text", "readable", "legible" ]; // Auto-detect if prompt needs Pro model function shouldUsePro(prompt: string): boolean { const lower = prompt.toLowerCase(); return PRO_KEYWORDS.some(keyword => lower.includes(keyword)); } // Generate images using Nano Banana (via generateContent with IMAGE modality) async function generateImage( prompt: string, options: { numberOfImages?: number; aspectRatio?: string; outputDir?: string; usePro?: boolean; } = {} ): Promise<{ images: Array<{ base64: string; mimeType: string }>; prompt: string; model: string }> { const numberOfImages = options.numberOfImages || 1; const aspectRatio = options.aspectRatio || "1:1"; // Auto-detect Pro if prompt contains keywords, or use explicit usePro flag const usePro = options.usePro ?? shouldUsePro(prompt); const model = usePro ? IMAGE_MODEL_PRO : IMAGE_MODEL_FAST; const images: Array<{ base64: string; mimeType: string }> = []; // Nano Banana uses generateContent with response_modalities: ['IMAGE'] // Generate each image separately (generateContent returns 1 image per call) for (let i = 0; i < numberOfImages; i++) { const response = await ai.models.generateContent({ model: model, contents: prompt, config: { responseModalities: ["IMAGE"], // Include aspect ratio in the generation config ...(aspectRatio !== "1:1" && { generationConfig: { aspectRatio } }), }, }); // Extract image from response parts if (response.candidates && response.candidates[0]?.content?.parts) { for (const part of response.candidates[0].content.parts) { if (part.inlineData?.data) { images.push({ base64: part.inlineData.data, mimeType: part.inlineData.mimeType || "image/png", }); } } } } return { images, prompt, model }; } // Call Gemini API async function callGemini( prompt: string, history: Content[] = [], systemInstruction?: string ): Promise<{ text: string; history: Content[] }> { // Create the chat with history and config const chat = ai.chats.create({ model: MODEL, config: systemInstruction ? { systemInstruction } : undefined, history: history, }); // Send the message const response = await chat.sendMessage({ message: prompt }); // Build updated history const newHistory: Content[] = [ ...history, { role: "user", parts: [{ text: prompt }] }, { role: "model", parts: [{ text: response.text || "" }] }, ]; return { text: response.text || "", history: newHistory, }; } // Define tools - mirrors Codex MCP interface const tools: Tool[] = [ { name: "gemini", description: `Run a Gemini session. Similar to Codex but uses Google Gemini 3 Pro Preview. Supports configuration parameters matching the Codex Config struct: - prompt: The initial user prompt to start the conversation (required) - cwd: Working directory context - sandbox: Access policy ("read-only", "workspace-write", "danger-full-access") - base-instructions: Override default system instructions - developer-instructions: Additional developer context - model: Optional override for model (default: ${MODEL})`, inputSchema: { type: "object" as const, properties: { prompt: { type: "string", description: "The initial user prompt to start the Gemini conversation", }, cwd: { type: "string", description: "Working directory for context", }, sandbox: { type: "string", enum: ["read-only", "workspace-write", "danger-full-access"], description: "Access policy mode", }, "base-instructions": { type: "string", description: "Override the default system instructions", }, "developer-instructions": { type: "string", description: "Developer instructions for additional context", }, model: { type: "string", description: `Model override (default: ${MODEL})`, }, config: { type: "object", description: "Additional config settings (passthrough)", additionalProperties: true, }, }, required: ["prompt"], }, }, { name: "gemini-reply", description: `Continue a Gemini conversation by providing the conversation ID and prompt. Use this to continue a multi-turn conversation started with the 'gemini' tool.`, inputSchema: { type: "object" as const, properties: { conversationId: { type: "string", description: "The conversation ID from a previous gemini call", }, prompt: { type: "string", description: "The next user prompt to continue the conversation", }, }, required: ["conversationId", "prompt"], }, }, { name: "gemini-image", description: `Generate images using Nano Banana (Gemini's native image generation). Two models available: - Nano Banana (default): Fast, cheap (~$0.04/image), good for most use cases - Nano Banana Pro: Advanced model with better text rendering, infographics, diagrams Auto-detection: Says "nano banana pro" or mentions text/infographic/diagram/chart/logo/poster in prompt → automatically uses Pro model. Parameters: - prompt: Text description of the image to generate (required) - numberOfImages: How many images to generate (1-4, default: 1) - aspectRatio: Image aspect ratio ("1:1", "3:4", "4:3", "9:16", "16:9", default: "1:1") - usePro: Force Nano Banana Pro (auto-detected from prompt if not specified) - outputPath: Optional path to save images`, inputSchema: { type: "object" as const, properties: { prompt: { type: "string", description: "Text description of the image to generate", }, numberOfImages: { type: "number", description: "Number of images to generate (1-4)", minimum: 1, maximum: 4, }, aspectRatio: { type: "string", enum: ["1:1", "3:4", "4:3", "9:16", "16:9"], description: "Aspect ratio of generated images", }, usePro: { type: "boolean", description: "Use Nano Banana Pro for higher quality (better text, infographics)", default: false, }, outputPath: { type: "string", description: "Optional directory path to save generated images", }, }, required: ["prompt"], }, }, ]; // Create MCP server const server = new Server( { name: "gemini-mcp", version: "1.0.0", }, { capabilities: { tools: {}, }, } ); // Handle tool listing server.setRequestHandler(ListToolsRequestSchema, async () => { return { tools }; }); // Handle tool calls server.setRequestHandler(CallToolRequestSchema, async (request) => { const { name, arguments: args } = request.params; // Cleanup old sessions periodically cleanupSessions(); try { if (name === "gemini") { const { prompt, cwd, sandbox, "base-instructions": baseInstructions, "developer-instructions": developerInstructions, model, } = args as { prompt: string; cwd?: string; sandbox?: string; "base-instructions"?: string; "developer-instructions"?: string; model?: string; }; // Build system instruction const systemInstruction = buildSystemInstruction({ cwd, sandbox, baseInstructions, developerInstructions, }); // Call Gemini const result = await callGemini(prompt, [], systemInstruction); // Create new session const conversationId = generateConversationId(); sessions.set(conversationId, { history: result.history, createdAt: Date.now(), lastUsed: Date.now(), cwd, }); return { content: [ { type: "text", text: result.text, }, ], // Include conversation ID in metadata for continuation _meta: { conversationId, }, }; } else if (name === "gemini-reply") { const { conversationId, prompt } = args as { conversationId: string; prompt: string; }; // Get existing session const session = sessions.get(conversationId); if (!session) { return { content: [ { type: "text", text: `Error: Conversation ${conversationId} not found. It may have expired or never existed.`, }, ], isError: true, }; } // Build system instruction (use stored context) const systemInstruction = buildSystemInstruction({ cwd: session.cwd, }); // Continue conversation const result = await callGemini(prompt, session.history, systemInstruction); // Update session session.history = result.history; session.lastUsed = Date.now(); return { content: [ { type: "text", text: result.text, }, ], _meta: { conversationId, }, }; } else if (name === "gemini-image") { const { prompt, numberOfImages, aspectRatio, usePro, outputPath } = args as { prompt: string; numberOfImages?: number; aspectRatio?: string; usePro?: boolean; outputPath?: string; }; // Generate images using Nano Banana const result = await generateImage(prompt, { numberOfImages: numberOfImages || 1, aspectRatio: aspectRatio || "1:1", usePro: usePro || false, }); // If outputPath provided, save images to disk let savedPaths: string[] = []; if (outputPath && result.images.length > 0) { const fs = await import("fs"); const path = await import("path"); // Ensure directory exists if (!fs.existsSync(outputPath)) { fs.mkdirSync(outputPath, { recursive: true }); } // Generate safe filename from prompt const safePrompt = prompt.slice(0, 50).replace(/[^a-zA-Z0-9]/g, "_"); for (let i = 0; i < result.images.length; i++) { const filename = `${safePrompt}_${i + 1}.png`; const fullPath = path.join(outputPath, filename); const buffer = Buffer.from(result.images[i].base64, "base64"); fs.writeFileSync(fullPath, buffer); savedPaths.push(fullPath); } } // Return response with image data const content: Array<{ type: string; text?: string; data?: string; mimeType?: string }> = []; // Add summary text content.push({ type: "text", text: `Generated ${result.images.length} image(s) using ${result.model} for prompt: "${prompt}"${savedPaths.length > 0 ? `\n\nSaved to:\n${savedPaths.join("\n")}` : ""}`, }); // Add images as base64 for (const img of result.images) { content.push({ type: "image", data: img.base64, mimeType: img.mimeType, }); } return { content }; } else { return { content: [ { type: "text", text: `Unknown tool: ${name}`, }, ], isError: true, }; } } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [ { type: "text", text: `Gemini API error: ${errorMessage}`, }, ], isError: true, }; } }); // Start server async function main() { const transport = new StdioServerTransport(); await server.connect(transport); console.error("Gemini MCP server running on stdio"); } main().catch((error) => { console.error("Fatal error:", error); process.exit(1); });