Discover Awesome MCP Servers

mcp-server-logs-sieve

An MCP server that connects Claude (or any MCP compatible client) to your existing log infrastructure. Query, summarize, and trace logs in plain English across GCP Cloud Logging, AWS CloudWatch, Azure Log Analytics, Grafana Loki, and Elasticsearch without writing filter expressions or leaving your editor.

AgentWallet MCP Server

Provides permissionless wallet infrastructure for AI agents to manage wallets, sign transactions, and handle tokens across Solana and all EVM-compatible chains. It includes 29 specialized tools for on-chain operations, featuring built-in security guards and automated x402 payment processing without KYC requirements.

BloodHound MCP

Ekstensi yang memungkinkan Large Language Model (Model Bahasa Besar) untuk berinteraksi dengan dan menganalisis lingkungan Active Directory melalui kueri bahasa alami, alih-alih kueri Cypher manual.

xcomet-mcp-server

Translation quality evaluation MCP Server powered by xCOMET. Provides quality scoring (0-1), error detection with severity levels, and optimized batch processing for AI-assisted translation workflows.

AWS Security MCP

A Model Context Protocol server that connects AI assistants like Claude to AWS security services, allowing them to autonomously query, inspect, and analyze AWS infrastructure for security issues and misconfigurations.

Wikipedia Summarizer MCP Server

Sebuah server MCP (Model Context Protocol) yang mengambil dan meringkas artikel Wikipedia menggunakan Ollama LLM, dapat diakses melalui antarmuka baris perintah dan Streamlit. Sempurna untuk mengekstrak informasi penting dari Wikipedia dengan cepat tanpa membaca seluruh artikel.

Europe PMC Literature Search MCP Server

A professional literature search tool built on FastMCP framework that enables AI assistants to search academic literature from Europe PMC, retrieve article details, and analyze journal quality with seamless integration into Claude Desktop and Cherry Studio.

DateTime MCP Server

Provides timezone-aware date and time information with configurable time formats and timezone support. Enables users to get current date and time in their preferred timezone and format through simple MCP tools.

MCP DeepSeek 演示项目

Tentu, berikut adalah contoh kasus penggunaan minimal DeepSeek yang terintegrasi dengan MCP (MicroConfig Protocol) dalam bahasa Inggris dan terjemahannya ke dalam bahasa Indonesia, termasuk kode klien dan server: **English:** **Scenario:** A simple key-value store where the client can set and get values from the server using DeepSeek for inference and MCP for configuration management. **Components:** * **Client:** Sends requests to the server to set or get a value. * **Server:** Stores the key-value pairs and uses DeepSeek to potentially enhance the value before storing or returning it. MCP is used to configure the server's behavior (e.g., DeepSeek model endpoint). **Minimal Code (Conceptual):** **Server (Python):** ```python # server.py import socket import json import deepseek_client # Hypothetical DeepSeek client library import mcp_client # Hypothetical MCP client library # MCP Configuration mcp = mcp_client.MCPClient(server_address="mcp_server:8080") config = mcp.get_config("key_value_store") deepseek_endpoint = config.get("deepseek_endpoint", "default_endpoint") # DeepSeek Client deepseek = deepseek_client.DeepSeekClient(endpoint=deepseek_endpoint) def handle_request(data): try: request = json.loads(data.decode()) action = request.get("action") if action == "set": key = request.get("key") value = request.get("value") # Potentially enhance the value using DeepSeek enhanced_value = deepseek.infer(f"Enhance this value: {value}") store[key] = enhanced_value return json.dumps({"status": "success", "message": f"Set {key} to {enhanced_value}"}) elif action == "get": key = request.get("key") value = store.get(key, None) return json.dumps({"status": "success", "value": value}) else: return json.dumps({"status": "error", "message": "Invalid action"}) except Exception as e: return json.dumps({"status": "error", "message": str(e)}) # Simple in-memory store store = {} # Basic Socket Server (Example - Replace with a proper framework) HOST = 'localhost' PORT = 12345 with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) if not data: break response = handle_request(data) conn.sendall(response.encode()) ``` **Client (Python):** ```python # client.py import socket import json def send_request(action, key=None, value=None): request = {"action": action} if key: request["key"] = key if value: request["value"] = value message = json.dumps(request).encode() HOST = 'localhost' PORT = 12345 with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) s.sendall(message) data = s.recv(1024) return data.decode() # Example Usage response = send_request("set", key="my_key", value="initial value") print(f"Set Response: {response}") response = send_request("get", key="my_key") print(f"Get Response: {response}") ``` **Explanation:** 1. **MCP Integration:** The server uses a hypothetical `mcp_client` to fetch configuration data from an MCP server. This configuration includes the endpoint for the DeepSeek model. This allows you to change the DeepSeek model without redeploying the server. 2. **DeepSeek Integration:** The server uses a hypothetical `deepseek_client` to interact with a DeepSeek model. In the `set` action, it sends the value to DeepSeek for potential enhancement. 3. **Client-Server Communication:** The client sends JSON-formatted requests to the server using sockets. The server processes the requests and sends back JSON-formatted responses. 4. **Minimal Example:** This is a simplified example. A real-world application would likely use a more robust framework (e.g., Flask, FastAPI, gRPC) for the server and handle errors more gracefully. **Indonesian Translation:** **Skenario:** Sebuah penyimpanan nilai-kunci sederhana di mana klien dapat mengatur dan mendapatkan nilai dari server menggunakan DeepSeek untuk inferensi dan MCP untuk manajemen konfigurasi. **Komponen:** * **Klien:** Mengirim permintaan ke server untuk mengatur atau mendapatkan nilai. * **Server:** Menyimpan pasangan nilai-kunci dan menggunakan DeepSeek untuk berpotensi meningkatkan nilai sebelum menyimpannya atau mengembalikannya. MCP digunakan untuk mengkonfigurasi perilaku server (misalnya, endpoint model DeepSeek). **Kode Minimal (Konseptual):** **Server (Python):** ```python # server.py import socket import json import deepseek_client # Pustaka klien DeepSeek hipotetis import mcp_client # Pustaka klien MCP hipotetis # Konfigurasi MCP mcp = mcp_client.MCPClient(server_address="mcp_server:8080") config = mcp.get_config("key_value_store") deepseek_endpoint = config.get("deepseek_endpoint", "default_endpoint") # Klien DeepSeek deepseek = deepseek_client.DeepSeekClient(endpoint=deepseek_endpoint) def handle_request(data): try: request = json.loads(data.decode()) action = request.get("action") if action == "set": key = request.get("key") value = request.get("value") # Berpotensi meningkatkan nilai menggunakan DeepSeek enhanced_value = deepseek.infer(f"Tingkatkan nilai ini: {value}") store[key] = enhanced_value return json.dumps({"status": "success", "message": f"Mengatur {key} ke {enhanced_value}"}) elif action == "get": key = request.get("key") value = store.get(key, None) return json.dumps({"status": "success", "value": value}) else: return json.dumps({"status": "error", "message": "Aksi tidak valid"}) except Exception as e: return json.dumps({"status": "error", "message": str(e)}) # Penyimpanan dalam memori sederhana store = {} # Server Socket Dasar (Contoh - Ganti dengan kerangka kerja yang tepat) HOST = 'localhost' PORT = 12345 with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server mendengarkan di {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Terhubung oleh {addr}") while True: data = conn.recv(1024) if not data: break response = handle_request(data) conn.sendall(response.encode()) ``` **Klien (Python):** ```python # client.py import socket import json def send_request(action, key=None, value=None): request = {"action": action} if key: request["key"] = key if value: request["value"] = value message = json.dumps(request).encode() HOST = 'localhost' PORT = 12345 with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) s.sendall(message) data = s.recv(1024) return data.decode() # Contoh Penggunaan response = send_request("set", key="my_key", value="nilai awal") print(f"Respons Set: {response}") response = send_request("get", key="my_key") print(f"Respons Get: {response}") ``` **Penjelasan:** 1. **Integrasi MCP:** Server menggunakan `mcp_client` hipotetis untuk mengambil data konfigurasi dari server MCP. Konfigurasi ini mencakup endpoint untuk model DeepSeek. Ini memungkinkan Anda mengubah model DeepSeek tanpa menyebarkan ulang server. 2. **Integrasi DeepSeek:** Server menggunakan `deepseek_client` hipotetis untuk berinteraksi dengan model DeepSeek. Dalam aksi `set`, ia mengirimkan nilai ke DeepSeek untuk potensi peningkatan. 3. **Komunikasi Klien-Server:** Klien mengirim permintaan berformat JSON ke server menggunakan soket. Server memproses permintaan dan mengirim kembali respons berformat JSON. 4. **Contoh Minimal:** Ini adalah contoh yang disederhanakan. Aplikasi dunia nyata kemungkinan akan menggunakan kerangka kerja yang lebih kuat (misalnya, Flask, FastAPI, gRPC) untuk server dan menangani kesalahan dengan lebih baik. **Important Notes:** * **Hypothetical Libraries:** `deepseek_client` and `mcp_client` are placeholders. You'll need to replace them with actual libraries or implement your own. For DeepSeek, you'd likely use their API. For MCP, you'd need a client library that interacts with your MCP server. * **Error Handling:** The error handling is very basic. In a real application, you'd want to handle exceptions more gracefully and provide more informative error messages. * **Security:** This example does not include any security measures. In a production environment, you'd need to implement authentication, authorization, and data encryption. * **Socket Server:** The socket server is a very basic example. Consider using a more robust framework like Flask, FastAPI, or gRPC for a production application. * **MCP Server:** You'll need a running MCP server to provide the configuration data. The example assumes it's running at `mcp_server:8080`. This provides a minimal, conceptual example. You'll need to adapt it to your specific needs and environment. Remember to install the necessary libraries (or create your own) and configure the MCP server.

Haloscan MCP Server

A Model Context Protocol server that exposes Haloscan SEO API functionality, allowing users to access keyword insights, domain analysis, and competitor research through Claude for Desktop and other MCP-compatible clients.

MTG Card Lookup MCP Server

Enables fuzzy lookup of Magic: The Gathering cards by name using the Scryfall API, returning card details including type, oracle text, mana value, and images.

MCP Sentry Analyzer

Integrates Sentry error monitoring with AI-powered analysis to automatically capture frontend JavaScript errors and provide intelligent repair suggestions through multiple AI models including OpenAI, Claude, and Gemini.

Collective Brain MCP Server

Enables teams to create a shared knowledge base where members can store, search, and validate information collectively. Provides semantic search across team memories with granular permissions and collaborative verification features.

linear-mcp

A Model Context Protocol server that enables interaction with Linear workspaces to manage issues, projects, cycles, and teams through a GraphQL interface. It provides 21 tools for comprehensive task management, search, and workflow coordination using OAuth2 authentication.

browser-use-mcp-server

Mirror of

Jupiter Broadcasting Podcast Data MCP Server

Enables access to Jupiter Broadcasting podcast episodes through RSS feed parsing. Supports searching episodes by date, hosts, or content, retrieving detailed episode information, and fetching transcripts when available.

KeyID Agent Kit

Provides AI agents with a free, functional email address via the Model Context Protocol without requiring manual registration. It enables comprehensive email capabilities including sending, receiving, and managing messages, contacts, and automated settings.

Test MCP Feb4 MCP Server

An MCP server that provides standardized tools for AI agents to interact with the Test MCP Feb4 API. It enables LLMs to access API endpoints through asynchronous operations and standardized Model Context Protocol tools.

MCP Server for Odoo

Enables AI assistants to interact with Odoo ERP systems through natural language to search records, create entries, update data, and manage business operations. Supports secure authentication and configurable access controls for production environments.

Agent Sense

Provides AI agents with environmental sensing capabilities including current time, IP-based geolocation, system information, and hardware details. Enables more contextual and accurate AI responses based on user's environment.

VseGPT MCP Servers

Server MCP untuk VseGPT

Google Maps MCP Server

Integrates Google Maps routing and traffic capabilities with Claude AI for advanced route planning, real-time traffic analysis, route comparison, and trip cost estimation including fuel and tolls.

AskTable MCP Server

Enables interaction with AskTable's data analytics platform through MCP protocol. Supports querying and analyzing data from connected datasources using natural language, available via both SaaS and self-hosted deployments.

Math & Calculator MCP Server

Provides advanced mathematical utilities including basic arithmetic, statistical analysis, unit conversions, quadratic equation solving, percentage calculations, and trigonometric functions for AI assistants.

🤖 mcp-ollama-beeai

Aplikasi agentik minimal untuk berinteraksi dengan model OLLAMA yang memanfaatkan berbagai alat server MCP menggunakan kerangka kerja BeeAI.

Proto Server

Enables AI assistants to explore and understand Protocol Buffer (.proto) files through fuzzy search, service definitions, and message structure analysis. Integrates with Cursor IDE to provide natural language queries about protobuf schemas and API definitions.

GigAPI MCP Server

An MCP server that provides seamless integration with Claude Desktop for querying and managing timeseries data in GigAPI Timeseries Lake.

lucid-mcp

AI-native data analysis agent as an MCP Server. Connect your Excel files, CSVs, and MySQL databases. Understand business semantics. Query with natural language.