Skip to main content
Glama

Gemini Context MCP Server

by ogoldberg
test-add-improvements.js3.54 kB
import { spawn } from 'child_process'; import fs from 'fs'; // Create log file const logFile = fs.createWriteStream('./improvements-test.log', { flags: 'w' }); const log = (message) => { console.log(message); logFile.write(message + '\n'); }; // Start the MCP server const server = spawn('node', ['dist/mcp-server.js'], { stdio: ['pipe', 'pipe', process.stderr] }); // Helper function to send request and get response function sendRequest(request) { return new Promise((resolve, reject) => { const responseHandler = (data) => { try { const response = JSON.parse(data.toString()); if (response.id === request.id) { server.stdout.removeListener('data', responseHandler); resolve(response); } } catch (error) { log(`Error parsing response: ${error}`); reject(error); } }; server.stdout.on('data', responseHandler); server.stdin.write(JSON.stringify(request) + '\n'); }); } async function runTest() { try { log('Starting improvements context test'); // Add improvements to context log('\n1. Adding improvements to context...'); const addResponse = await sendRequest({ id: 1, method: 'tool', params: { name: 'add_context', arguments: { role: 'system', content: `Future Improvement Suggestions for Gemini Context Server: 1. Add persistence layer: Implement database storage for sessions and caches to survive restarts 2. Cache size management: Add maximum cache size limits and LRU eviction policies 3. Vector-based semantic search: Improve search with proper embeddings instead of basic text matching 4. Analytics and metrics: Track cache hit rates, token usage patterns, and query distributions 5. Vector store integration: Connect to dedicated vector stores like Pinecone or Weaviate 6. Batch operations: Support bulk context operations for efficiency 7. Hybrid caching strategy: Try native API caching when available, fall back to custom implementation 8. Auto-optimization: Analyze and reduce prompt sizes while preserving context`, metadata: { topic: 'improvements', tags: ['caching', 'performance', 'roadmap'] } } } }); log(`Add context response: ${JSON.stringify(addResponse.result?.content[0])}`); // Search for improvements log('\n2. Searching for improvement suggestions...'); const searchResponse = await sendRequest({ id: 2, method: 'tool', params: { name: 'search_context', arguments: { query: 'improvements' } } }); log(`Search results: ${JSON.stringify(searchResponse.result?.content[0])}`); log('\nTest completed successfully!'); } catch (error) { log(`Test error: ${error}`); } finally { // Shutdown properly log('\nShutting down MCP server...'); try { process.kill(server.pid, 'SIGINT'); logFile.end(); } catch (error) { log(`Error shutting down server: ${error}`); } process.exit(0); } } runTest();

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ogoldberg/gemini-context-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server