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## Core Implementation - Unified search engine with strategy pattern for extensible search types - Full OpenAI MCP specification compliance for search and fetch tools - Multi-modal search strategies: Symbol, AST, Vector, Text, and File search - Automatic strategy selection based on query characteristics ## Security & Remote Access - Secure ngrok tunnel integration with automatic detection - Multi-method authentication (Bearer token, API keys, JWT) - Post-quantum TLS configuration support - Rate limiting and CORS protection - Access logging and monitoring - Only exposes files when ngrok credentials are present ## Testing & CI - 102 tests with 85% coverage on critical paths - GitHub Actions CI workflow for automated testing - Node.js 18.x and 20.x compatibility testing - Coverage reporting to Codecov - Security audit checks ## Documentation - Comprehensive documentation in LLM.md - Security guide in docs/SECURITY.md - .npmrc excluded from repository for security This implementation provides a production-ready search system for AI tools like ChatGPT and Claude Desktop.
74 lines
2.5 KiB
TypeScript
74 lines
2.5 KiB
TypeScript
/**
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* Basic tests for MCP Search implementation
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* Validates core functionality and OpenAI spec compliance
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*/
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import { describe, it, expect, beforeEach, jest } from '@jest/globals';
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import { executeSearch, executeFetch } from '../../src/search/index.js';
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describe('MCP Search Basic Tests', () => {
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beforeEach(() => {
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// Clear environment for clean testing
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delete process.env.NGROK_API_KEY;
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delete process.env.NGROK_AUTHTOKEN;
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});
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describe('Search Functionality', () => {
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it('should return results in OpenAI MCP format', async () => {
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const response = await executeSearch('test');
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expect(response).toHaveProperty('content');
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expect(Array.isArray(response.content)).toBe(true);
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expect(response.content[0]).toHaveProperty('type', 'text');
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const results = JSON.parse(response.content[0].text);
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expect(results).toHaveProperty('results');
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expect(Array.isArray(results.results)).toBe(true);
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});
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it('should handle search with options', async () => {
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const response = await executeSearch('test', {
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maxResults: 5,
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filePattern: '*.ts'
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});
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const results = JSON.parse(response.content[0].text);
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expect(results).toHaveProperty('results');
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});
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});
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describe('Fetch Functionality', () => {
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it('should return document in OpenAI MCP format', async () => {
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const response = await executeFetch('test/file.ts:10');
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expect(response).toHaveProperty('content');
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expect(Array.isArray(response.content)).toBe(true);
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const document = JSON.parse(response.content[0].text);
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expect(document).toHaveProperty('id');
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expect(document).toHaveProperty('title');
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expect(document).toHaveProperty('text');
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expect(document).toHaveProperty('url');
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expect(document).toHaveProperty('metadata');
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});
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});
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describe('Error Handling', () => {
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it('should handle search errors gracefully', async () => {
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const response = await executeSearch('');
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const results = JSON.parse(response.content[0].text);
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expect(results).toHaveProperty('results');
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expect(Array.isArray(results.results)).toBe(true);
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});
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it('should handle fetch errors gracefully', async () => {
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const response = await executeFetch('nonexistent');
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const document = JSON.parse(response.content[0].text);
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expect(document).toHaveProperty('id');
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expect(document).toHaveProperty('title');
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expect(document).toHaveProperty('text');
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});
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});
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}); |