@websolutespa/payload-plugin-bowl-llm/dist/api/mcp.adapter.js

LLM plugin for Bowl PayloadCms plugin

import { fetchRobot } from '../utils/robot';
import { ToolSchemaRegistry } from './mcp.schemas';
export async function buildMcpToolsForApp(app) {
    const appTools = app.settings.appTools?.filter((t)=>t.isActive) || [];
    const descriptors = [];
    for (const tool of appTools){
        if (await ToolSchemaRegistry.has(tool.functionName)) {
            const schema = await ToolSchemaRegistry.get(tool.functionName);
            descriptors.push({
                name: tool.functionId,
                description: tool.functionDescription || tool.description || tool.name,
                inputSchema: schema || {
                    type: 'object',
                    properties: {}
                }
            });
        }
    }
    return descriptors;
}
// ---------- Tool Execution ----------
export async function executeTool(payload, app, functionId, args) {
    // Find the tool in the app
    const tool = app.settings.appTools?.find((t)=>t.functionId === functionId && t.isActive);
    if (!tool) {
        throw {
            status: 404,
            message: `Tool not found: ${functionId}`
        };
    }
    // Build the appTool payload for Robot (matches LlmAppTool fields)
    const appTool = {
        name: tool.name,
        description: tool.description,
        type: tool.type,
        functionId: tool.functionId,
        functionName: tool.functionName,
        functionDescription: tool.functionDescription,
        secrets: tool.secrets,
        llmChainSettings: tool.llmChainSettings,
        searchSettings: tool.searchSettings,
        dataSource: tool.dataSource,
        dbSettings: tool.dbSettings,
        apiSettings: tool.apiSettings,
        integrations: tool.knowledgeBase?.integrations || [],
        externalEndpoints: tool.knowledgeBase?.externalEndpoints || [],
        waitingMessage: tool.waitingMessage,
        vectorDbType: tool.knowledgeBase?.vectorDbType || 'faiss',
        vectorDbFile: tool.knowledgeBase?.vectorDbFile?.filename || null,
        isActive: true
    };
    // Call Robot direct tool execution (no agent, no LLM chain)
    const request = {
        provider: app.settings.llmConfig.provider || 'openai',
        model: app.settings.llmConfig.model || 'gpt-4o',
        secrets: app.settings.llmConfig.secrets || {},
        appTool,
        arguments: args
    };
    const result = await fetchRobot('api/llm/tool/execute', 'POST', request);
    return result;
}

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