mirror of
https://github.com/czlonkowski/n8n-mcp.git
synced 2026-02-06 13:33:11 +00:00
fix: resolve TypeScript compilation errors in integration tests
Fixed multiple TypeScript errors preventing clean build: - Fixed import paths for ValidationResponse type (5 test files) - Fixed validateBasicLLMChain function signature (removed extra workflow parameter) - Enhanced ValidationResponse interface to include missing properties: - Added code, nodeName fields to errors/warnings - Added info array for informational messages - Added suggestions array - Fixed type assertion in mergeConnections helper - Fixed implicit any type in chat-trigger-validation test All tests now compile cleanly with no TypeScript errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
czlonkowski
434
tests/integration/ai-validation/ai-agent-validation.test.ts
Normal file
434
tests/integration/ai-validation/ai-agent-validation.test.ts
Normal file
@@ -0,0 +1,434 @@
|
||||
/**
|
||||
* Integration Tests: AI Agent Validation
|
||||
*
|
||||
* Tests AI Agent validation against real n8n instance.
|
||||
* These tests validate the fixes from v2.17.0 including node type normalization.
|
||||
*/
|
||||
|
||||
import { describe, it, expect, beforeEach, afterEach, afterAll } from 'vitest';
|
||||
import { createTestContext, TestContext, createTestWorkflowName } from '../n8n-api/utils/test-context';
|
||||
import { getTestN8nClient } from '../n8n-api/utils/n8n-client';
|
||||
import { N8nApiClient } from '../../../src/services/n8n-api-client';
|
||||
import { cleanupOrphanedWorkflows } from '../n8n-api/utils/cleanup-helpers';
|
||||
import { createMcpContext } from '../n8n-api/utils/mcp-context';
|
||||
import { InstanceContext } from '../../../src/types/instance-context';
|
||||
import { handleValidateWorkflow } from '../../../src/mcp/handlers-n8n-manager';
|
||||
import { getNodeRepository, closeNodeRepository } from '../n8n-api/utils/node-repository';
|
||||
import { NodeRepository } from '../../../src/database/node-repository';
|
||||
import { ValidationResponse } from '../n8n-api/types/mcp-responses';
|
||||
import {
|
||||
createAIAgentNode,
|
||||
createChatTriggerNode,
|
||||
createLanguageModelNode,
|
||||
createHTTPRequestToolNode,
|
||||
createCodeToolNode,
|
||||
createMemoryNode,
|
||||
createRespondNode,
|
||||
createAIConnection,
|
||||
createMainConnection,
|
||||
mergeConnections,
|
||||
createAIWorkflow
|
||||
} from './helpers';
|
||||
|
||||
describe('Integration: AI Agent Validation', () => {
|
||||
let context: TestContext;
|
||||
let client: N8nApiClient;
|
||||
let mcpContext: InstanceContext;
|
||||
let repository: NodeRepository;
|
||||
|
||||
beforeEach(async () => {
|
||||
context = createTestContext();
|
||||
client = getTestN8nClient();
|
||||
mcpContext = createMcpContext();
|
||||
repository = await getNodeRepository();
|
||||
});
|
||||
|
||||
afterEach(async () => {
|
||||
await context.cleanup();
|
||||
});
|
||||
|
||||
afterAll(async () => {
|
||||
await closeNodeRepository();
|
||||
if (!process.env.CI) {
|
||||
await cleanupOrphanedWorkflows();
|
||||
}
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 1: Missing Language Model
|
||||
// ======================================================================
|
||||
|
||||
it('should detect missing language model in real workflow', async () => {
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
text: 'Test prompt'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[agent],
|
||||
{},
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - Missing Model'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(false);
|
||||
expect(data.errors).toBeDefined();
|
||||
expect(data.errors!.length).toBeGreaterThan(0);
|
||||
|
||||
const errorCodes = data.errors!.map(e => e.code);
|
||||
expect(errorCodes).toContain('MISSING_LANGUAGE_MODEL');
|
||||
|
||||
const errorMessages = data.errors!.map(e => e.message).join(' ');
|
||||
expect(errorMessages).toMatch(/language model|ai_languageModel/i);
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 2: Valid AI Agent with Language Model
|
||||
// ======================================================================
|
||||
|
||||
it('should validate AI Agent with language model', async () => {
|
||||
const languageModel = createLanguageModelNode('openai', {
|
||||
name: 'OpenAI Chat Model'
|
||||
});
|
||||
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
text: 'You are a helpful assistant'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[languageModel, agent],
|
||||
mergeConnections(
|
||||
createAIConnection('OpenAI Chat Model', 'AI Agent', 'ai_languageModel')
|
||||
),
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - Valid'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(true);
|
||||
expect(data.errors).toBeUndefined();
|
||||
expect(data.summary.errorCount).toBe(0);
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 3: Tool Connections Detection
|
||||
// ======================================================================
|
||||
|
||||
it('should detect tool connections correctly', async () => {
|
||||
const languageModel = createLanguageModelNode('openai', {
|
||||
name: 'OpenAI Chat Model'
|
||||
});
|
||||
|
||||
const httpTool = createHTTPRequestToolNode({
|
||||
name: 'HTTP Request Tool',
|
||||
toolDescription: 'Fetches weather data from API',
|
||||
url: 'https://api.weather.com/current',
|
||||
method: 'GET'
|
||||
});
|
||||
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
text: 'You are a weather assistant'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[languageModel, httpTool, agent],
|
||||
mergeConnections(
|
||||
createAIConnection('OpenAI Chat Model', 'AI Agent', 'ai_languageModel'),
|
||||
createAIConnection('HTTP Request Tool', 'AI Agent', 'ai_tool')
|
||||
),
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - With Tool'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(true);
|
||||
|
||||
// Should NOT have false "no tools" warning
|
||||
if (data.warnings) {
|
||||
const toolWarnings = data.warnings.filter(w =>
|
||||
w.message.toLowerCase().includes('no ai_tool')
|
||||
);
|
||||
expect(toolWarnings.length).toBe(0);
|
||||
}
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 4: Streaming Mode Constraints (Chat Trigger)
|
||||
// ======================================================================
|
||||
|
||||
it('should validate streaming mode constraints', async () => {
|
||||
const chatTrigger = createChatTriggerNode({
|
||||
name: 'Chat Trigger',
|
||||
responseMode: 'streaming'
|
||||
});
|
||||
|
||||
const languageModel = createLanguageModelNode('openai', {
|
||||
name: 'OpenAI Chat Model'
|
||||
});
|
||||
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
text: 'You are a helpful assistant'
|
||||
});
|
||||
|
||||
const respond = createRespondNode({
|
||||
name: 'Respond to Webhook'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[chatTrigger, languageModel, agent, respond],
|
||||
mergeConnections(
|
||||
createMainConnection('Chat Trigger', 'AI Agent'),
|
||||
createAIConnection('OpenAI Chat Model', 'AI Agent', 'ai_languageModel'),
|
||||
createMainConnection('AI Agent', 'Respond to Webhook') // ERROR: streaming with main output
|
||||
),
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - Streaming Error'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(false);
|
||||
expect(data.errors).toBeDefined();
|
||||
|
||||
const streamingErrors = data.errors!.filter(e =>
|
||||
e.code === 'STREAMING_WITH_MAIN_OUTPUT' ||
|
||||
e.code === 'STREAMING_AGENT_HAS_OUTPUT'
|
||||
);
|
||||
expect(streamingErrors.length).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 5: AI Agent Own streamResponse Setting
|
||||
// ======================================================================
|
||||
|
||||
it('should validate AI Agent own streamResponse setting', async () => {
|
||||
const languageModel = createLanguageModelNode('openai', {
|
||||
name: 'OpenAI Chat Model'
|
||||
});
|
||||
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
text: 'You are a helpful assistant',
|
||||
streamResponse: true // Agent has its own streaming enabled
|
||||
});
|
||||
|
||||
const respond = createRespondNode({
|
||||
name: 'Respond to Webhook'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[languageModel, agent, respond],
|
||||
mergeConnections(
|
||||
createAIConnection('OpenAI Chat Model', 'AI Agent', 'ai_languageModel'),
|
||||
createMainConnection('AI Agent', 'Respond to Webhook') // ERROR: streaming with main output
|
||||
),
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - Own Streaming'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(false);
|
||||
expect(data.errors).toBeDefined();
|
||||
|
||||
const errorCodes = data.errors!.map(e => e.code);
|
||||
expect(errorCodes).toContain('STREAMING_WITH_MAIN_OUTPUT');
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 6: Multiple Memory Connections
|
||||
// ======================================================================
|
||||
|
||||
it('should validate memory connections', async () => {
|
||||
const languageModel = createLanguageModelNode('openai', {
|
||||
name: 'OpenAI Chat Model'
|
||||
});
|
||||
|
||||
const memory1 = createMemoryNode({
|
||||
name: 'Memory 1'
|
||||
});
|
||||
|
||||
const memory2 = createMemoryNode({
|
||||
name: 'Memory 2'
|
||||
});
|
||||
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
text: 'You are a helpful assistant'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[languageModel, memory1, memory2, agent],
|
||||
mergeConnections(
|
||||
createAIConnection('OpenAI Chat Model', 'AI Agent', 'ai_languageModel'),
|
||||
createAIConnection('Memory 1', 'AI Agent', 'ai_memory'),
|
||||
createAIConnection('Memory 2', 'AI Agent', 'ai_memory') // ERROR: multiple memory
|
||||
),
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - Multiple Memory'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(false);
|
||||
expect(data.errors).toBeDefined();
|
||||
|
||||
const errorCodes = data.errors!.map(e => e.code);
|
||||
expect(errorCodes).toContain('MULTIPLE_MEMORY_CONNECTIONS');
|
||||
});
|
||||
|
||||
// ======================================================================
|
||||
// TEST 7: Complete AI Workflow (All Components)
|
||||
// ======================================================================
|
||||
|
||||
it('should validate complete AI workflow', async () => {
|
||||
const chatTrigger = createChatTriggerNode({
|
||||
name: 'Chat Trigger',
|
||||
responseMode: 'lastNode' // Not streaming
|
||||
});
|
||||
|
||||
const languageModel = createLanguageModelNode('openai', {
|
||||
name: 'OpenAI Chat Model'
|
||||
});
|
||||
|
||||
const httpTool = createHTTPRequestToolNode({
|
||||
name: 'HTTP Request Tool',
|
||||
toolDescription: 'Fetches data from external API',
|
||||
url: 'https://api.example.com/data',
|
||||
method: 'GET'
|
||||
});
|
||||
|
||||
const codeTool = createCodeToolNode({
|
||||
name: 'Code Tool',
|
||||
toolDescription: 'Processes data with custom logic',
|
||||
code: 'return { result: "processed" };'
|
||||
});
|
||||
|
||||
const memory = createMemoryNode({
|
||||
name: 'Window Buffer Memory',
|
||||
contextWindowLength: 5
|
||||
});
|
||||
|
||||
const agent = createAIAgentNode({
|
||||
name: 'AI Agent',
|
||||
promptType: 'define',
|
||||
text: 'You are a helpful assistant with access to tools',
|
||||
systemMessage: 'You are an AI assistant that helps users with data processing and external API calls.'
|
||||
});
|
||||
|
||||
const respond = createRespondNode({
|
||||
name: 'Respond to Webhook'
|
||||
});
|
||||
|
||||
const workflow = createAIWorkflow(
|
||||
[chatTrigger, languageModel, httpTool, codeTool, memory, agent, respond],
|
||||
mergeConnections(
|
||||
createMainConnection('Chat Trigger', 'AI Agent'),
|
||||
createAIConnection('OpenAI Chat Model', 'AI Agent', 'ai_languageModel'),
|
||||
createAIConnection('HTTP Request Tool', 'AI Agent', 'ai_tool'),
|
||||
createAIConnection('Code Tool', 'AI Agent', 'ai_tool'),
|
||||
createAIConnection('Window Buffer Memory', 'AI Agent', 'ai_memory'),
|
||||
createMainConnection('AI Agent', 'Respond to Webhook')
|
||||
),
|
||||
{
|
||||
name: createTestWorkflowName('AI Agent - Complete Workflow'),
|
||||
tags: ['mcp-integration-test', 'ai-validation']
|
||||
}
|
||||
);
|
||||
|
||||
const created = await client.createWorkflow(workflow);
|
||||
context.trackWorkflow(created.id!);
|
||||
|
||||
const response = await handleValidateWorkflow(
|
||||
{ id: created.id },
|
||||
repository,
|
||||
mcpContext
|
||||
);
|
||||
|
||||
expect(response.success).toBe(true);
|
||||
const data = response.data as ValidationResponse;
|
||||
|
||||
expect(data.valid).toBe(true);
|
||||
expect(data.errors).toBeUndefined();
|
||||
expect(data.summary.errorCount).toBe(0);
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user