Evandro Fonseca Junior
4739a13458
Add support for Model Context Protocol (MCP) tools in the Ollama chat API. Detect model capabilities to use native tools or prompt engineering. Handle tool calls, execute them via MCPExecutor, and inject results back into the conversation for seamless tool-assisted responses. This enhances chat functionality by enabling external tool integration.
59 lines
1.7 KiB
TypeScript
59 lines
1.7 KiB
TypeScript
/**
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* API endpoint para listar MCPs disponíveis para uso no chat
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*/
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import { NextResponse } from "next/server";
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import { MCPRepository } from "@/database/repositories/mcp";
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import { MCPExecutor } from "@/lib/services/mcp-executor";
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import type { MCPTool } from "@/lib/types/mcp-chat";
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export const dynamic = "force-dynamic";
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export async function GET() {
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try {
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// Buscar todos os MCPs instalados com status "ready"
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const allMcps = MCPRepository.getAllInstalled();
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const availableMcps = allMcps
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.filter((mcp: any) => mcp.status === "ready")
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.map((mcp: any) => {
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const tools = MCPExecutor.getAvailableTools(mcp.id);
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// Converter tools para formato OpenAI function calling
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const toolsFormatted = tools.map((tool: MCPTool) => ({
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type: "function" as const,
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function: {
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name: `${mcp.id}__${tool.name}`, // Prefixar com mcpId para evitar conflitos
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description: tool.description,
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parameters: tool.inputSchema,
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},
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}));
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return {
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mcpId: mcp.id,
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name: mcp.name,
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owner: mcp.owner,
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repo: mcp.repo,
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toolCount: tools.length,
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tools: toolsFormatted,
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rawTools: tools, // Tools originais para referência
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};
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});
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return NextResponse.json({
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success: true,
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mcps: availableMcps,
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count: availableMcps.length,
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});
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} catch (error: any) {
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console.error("Error listing available MCPs:", error);
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return NextResponse.json(
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{
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success: false,
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error: error.message || "Failed to list available MCPs",
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},
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{ status: 500 }
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);
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}
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}
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