Discover Awesome MCP Servers

MongoDB MCP Server

Server Protokol Konteks Model yang menyediakan akses baca-saja ke database MongoDB, memungkinkan asisten AI untuk langsung meminta dan menganalisis data MongoDB sambil menjaga keamanan data.

Home Assistant MCP Server

Sebuah server yang memungkinkan interaksi dengan perangkat dan otomatisasi Home Assistant melalui Model Context Protocol, memungkinkan pengguna untuk memantau status perangkat, mengontrol perangkat, memicu otomatisasi, dan membuat daftar entitas.

MCP-DBLP

Server Protokol Konteks Model yang menyediakan akses ke basis data bibliografi ilmu komputer DBLP, memungkinkan model AI untuk mencari publikasi, memproses sitasi, dan menghasilkan entri BibTeX yang akurat.

mcp-server-tmdb

Terintegrasi dengan API The Movie Database (TMDB) untuk menyediakan informasi film, kemampuan pencarian, dan rekomendasi.

Bitcoin MCP Server

Memfasilitasi interaksi dengan jaringan Bitcoin melalui Model Context Protocol, memungkinkan pembuatan kunci, validasi alamat, dekode transaksi, dan pengambilan data blockchain.

PyTorch HUD MCP Server

Menyediakan akses ke data analitik CI/CD PyTorch termasuk alur kerja, pekerjaan, uji coba, dan analisis log melalui antarmuka MCP.

Worldpay MCP Server

Sebuah server yang mengimplementasikan Protokol Konteks Model untuk membuat dan menanyakan pembayaran melalui API Worldpay, memungkinkan pemrosesan pembayaran, kueri pembayaran, dan pembuatan formulir checkout.

Tiny Cryptography MCP Server

Server Protokol Konteks Model yang dibangun dengan Express.js yang menyediakan alat kriptografi termasuk pembuatan pasangan kunci, derivasi rahasia bersama, dan enkripsi/dekripsi pesan.

Python Docs Server

Sebuah server MCP yang memungkinkan pengguna untuk mengambil dokumentasi Python menggunakan Brave Search API melalui kueri bahasa alami.

KNMI Weather MCP

Sebuah server FastMCP yang menyediakan data cuaca waktu nyata dari stasiun cuaca KNMI, memungkinkan pengguna untuk mengakses suhu, kelembapan, kecepatan angin, dan metrik cuaca lainnya untuk lokasi mana pun di Belanda melalui kueri bahasa alami.

Tavily MCP Server with Proxy Support

Memungkinkan LLM (Model Bahasa Besar) untuk melakukan pencarian web yang canggih melalui server proxy menggunakan API Tavily, mendukung pencarian web komprehensif, penjawaban pertanyaan langsung, dan pengambilan artikel berita terbaru dengan konten yang diekstraksi oleh AI.

Time Server

Sebuah server MCP yang menyediakan konversi zona waktu dan operasi terkait waktu melalui titik akhir API RESTful, menampilkan penanganan kesalahan yang komprehensif dan integrasi basis data zona waktu.

BrianKnows MCP Server

Sebuah server MCP yang menghubungkan Claude ke basis pengetahuan blockchain BrianKnows, memungkinkan pengguna untuk mencari informasi blockchain/DeFi dan berinteraksi dengan agen khusus di berbagai basis pengetahuan.

Eventbrite MCP Server

Server ini menyediakan alat bagi asisten AI untuk berinteraksi dengan Eventbrite API, memungkinkan pengguna untuk mencari acara, mendapatkan detail acara, mengambil informasi tempat acara, dan membuat daftar kategori acara.

MCP Project Orchestrator

Sebuah server MCP yang membantu dalam orkestrasi proyek perangkat lunak baru dengan menerapkan templat standar dan praktik terbaik dalam pola desain dan arsitektur perangkat lunak.

TranscriptionTools MCP Server

Menyediakan kemampuan pemrosesan transkrip cerdas untuk Claude, menampilkan pemformatan alami, perbaikan kontekstual, dan peringkasan cerdas yang didukung oleh LLM Pemikiran Mendalam.

Typesense MCP Server

Implementasi server MCP yang memungkinkan model AI untuk menemukan, mencari, dan menganalisis data yang disimpan dalam koleksi Typesense melalui alat untuk menanyakan dokumen, mengambil item tertentu, dan mengakses statistik koleksi.

Memory Box MCP Server

Integrasi Cline MCP yang memungkinkan pengguna untuk menyimpan, mencari, dan memformat memori dengan pemahaman semantik, menyediakan alat untuk menyimpan dan mengambil informasi menggunakan *vector embeddings* untuk pencarian berbasis makna.

Meme MCP Server

Server Protokol Konteks Model sederhana yang memungkinkan model AI menghasilkan gambar meme menggunakan API ImgFlip, memungkinkan pengguna membuat meme dari perintah teks.

MCP Server Enhanced SSH

Server SSH yang kuat memfasilitasi eksekusi perintah jarak jauh yang aman dengan manajemen sesi TMUX, dukungan multi-jendela, dan pemulihan sesi cerdas untuk meningkatkan interaksi AI-manusia.

MCP Firecrawl Server

Sebuah server yang menyediakan alat untuk mengikis (scrape) situs web dan mengekstrak data terstruktur dari situs tersebut menggunakan API Firecrawl, mendukung pengikisan situs web dasar dalam berbagai format dan ekstraksi data berbasis skema khusus.

Placid MCP Server

Sebuah server untuk berintegrasi dengan API Placid.app, memungkinkan daftar templat dan menghasilkan materi kreatif menggunakan Protokol Konteks Model dengan pengelolaan token API yang aman.

MCP Redmine

Menghubungkan Claude Desktop ke Redmine untuk manajemen proyek dan isu yang lancar, termasuk mencari, membuat, memperbarui, dan melacak tugas menggunakan API Redmine.

World Bank MCP Server

Memungkinkan asisten AI untuk berinteraksi dengan API data terbuka Bank Dunia, memungkinkan daftar dan analisis indikator di seluruh negara yang tersedia.

mcp-github

Server MCP GitHub Anthropic, tapi lebih baik. Dukungan untuk lebih banyak titik akhir (endpoint). Termasuk rilis dan tag, ulasan *pull request*, status, batas laju (rate limit), gist, proyek, paket, dan bahkan diff *pull request*. Dimaksudkan untuk digunakan dengan API MCP MissionSquad untuk manajemen rahasia (alias token akses Anda).

emqx-mcp-server

Implementasi server Model Context Protocol (MCP) yang menyediakan interaksi broker MQTT EMQX.

mcp-server-birdstats

Okay, here's how you can cross-reference your BirdNET-Pi data with eBird observations using natural language, along with some considerations and potential approaches: **Understanding the Goal** The core idea is to compare what BirdNET-Pi *thinks* it heard with what human observers *actually* reported seeing in the same area and time frame. This helps: * **Validate BirdNET-Pi's accuracy:** See how often BirdNET-Pi correctly identifies birds present. * **Identify potential misidentifications:** Investigate cases where BirdNET-Pi reports a bird that wasn't observed by humans. This could be due to rare birds, distant calls, or BirdNET-Pi errors. * **Gain a more complete picture of bird activity:** Combine automated detection with human observation for a richer dataset. **General Steps & Considerations** 1. **Data Preparation:** * **BirdNET-Pi Data:** You'll need your BirdNET-Pi detection logs. These typically include: * Timestamp (date and time) * Latitude and Longitude (location of your BirdNET-Pi device) * Bird species detected (and often a confidence score) * **eBird Data:** You'll need to access eBird data. There are a few ways to do this: * **eBird API (preferred):** The eBird API allows you to programmatically query eBird data based on location and date ranges. This is the most efficient and flexible approach. You'll need an eBird API key. * **eBird Data Downloads:** eBird offers bulk data downloads (e.g., the eBird Basic Dataset). These are large files, but you can filter them to your region of interest. * **eBird Website:** You can manually browse eBird observations on the eBird website for specific locations and dates, but this is only practical for very small-scale comparisons. 2. **Location Matching:** * **Define a Radius:** Determine a reasonable radius around your BirdNET-Pi location. eBird observations within this radius will be considered "nearby." The appropriate radius depends on the habitat and the mobility of the birds you're interested in (e.g., a smaller radius for songbirds in a forest, a larger radius for waterfowl on a lake). 1-5 km is a reasonable starting point. * **Spatial Queries:** Use spatial functions (if you're using a database or programming language with spatial capabilities) to efficiently find eBird observations within the defined radius. Otherwise, you'll need to calculate distances between your BirdNET-Pi location and each eBird observation. 3. **Time Matching:** * **Define a Time Window:** Determine a time window around the BirdNET-Pi detection. eBird observations within this time window will be considered "concurrent." A time window of +/- 1 hour to +/- 1 day is common. Consider the behavior of the birds (e.g., are they more active at dawn?). * **Time Comparisons:** Compare the timestamps of BirdNET-Pi detections with the timestamps of eBird observations. 4. **Species Matching:** * **Taxonomic Consistency:** Ensure that the species names used by BirdNET-Pi and eBird are consistent. There may be slight variations in common names or scientific names. Use a taxonomic database (e.g., the Clements Checklist) to standardize the names. * **Consider Subspecies/Forms:** Decide whether you want to match at the species level or consider subspecies or distinct forms. 5. **Analysis and Interpretation:** * **Calculate Concordance:** Determine the percentage of BirdNET-Pi detections that are also reported by eBird observers within the specified radius and time window. * **Investigate Discrepancies:** Examine cases where BirdNET-Pi detects a bird that is *not* reported by eBird. Consider: * **Rarity:** Is the bird rare in the area? If so, it's possible BirdNET-Pi is correct, and eBird observers simply missed it. * **Distance:** Could the bird be calling from a distance, making it audible to BirdNET-Pi but not visible to observers? * **Habitat:** Is the habitat around your BirdNET-Pi location different from the habitat where eBird observers are reporting? * **BirdNET-Pi Confidence:** What was the confidence score of the BirdNET-Pi detection? Low confidence detections are more likely to be errors. * **eBird Effort:** How much effort (time spent observing) did eBird observers put in? A short checklist is less likely to capture all species present. * **Consider False Negatives:** Also, consider cases where eBird observers reported a bird that BirdNET-Pi *didn't* detect. This could be due to: * **Quiet Birds:** Some birds are less vocal. * **BirdNET-Pi Sensitivity:** BirdNET-Pi might not be sensitive enough to detect faint calls. * **Noise Interference:** Noise might mask the bird's calls. **Example using Python and the eBird API (Conceptual)** ```python import requests import json from datetime import datetime, timedelta from math import radians, sin, cos, sqrt, atan2 # For distance calculation # --- Configuration --- BIRDNET_LAT = 34.0522 # Example latitude BIRDNET_LON = -118.2437 # Example longitude RADIUS_KM = 2.0 TIME_WINDOW_HOURS = 2 EBIRD_API_KEY = "YOUR_EBIRD_API_KEY" # Replace with your actual API key def haversine(lat1, lon1, lat2, lon2): """Calculates the distance between two points on Earth (Haversine formula).""" R = 6371 # Radius of Earth in kilometers lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2]) dlon = lon2 - lon1 dlat = lat2 - lat1 a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2 c = 2 * atan2(sqrt(a), sqrt(1 - a)) distance = R * c return distance def get_ebird_observations(latitude, longitude, radius, date, api_key): """Queries the eBird API for observations within a radius of a location on a given date.""" date_str = date.strftime("%Y-%m-%d") url = f"https://api.ebird.org/v2/data/obs/geo/recent?lat={latitude}&lng={longitude}&dist={radius}&back=7&fmt=json" #back=7 gets the last 7 days of data headers = {"X-eBirdApiToken": api_key} response = requests.get(url, headers=headers) if response.status_code == 200: return response.json() else: print(f"Error fetching eBird data: {response.status_code} - {response.text}") return None # --- Example BirdNET-Pi Data (replace with your actual data) --- birdnet_detections = [ {"timestamp": datetime(2024, 10, 27, 7, 30, 0), "species": "Northern Cardinal", "confidence": 0.85}, {"timestamp": datetime(2024, 10, 27, 8, 15, 0), "species": "American Robin", "confidence": 0.70}, {"timestamp": datetime(2024, 10, 27, 9, 0, 0), "species": "California Quail", "confidence": 0.90}, {"timestamp": datetime(2024, 10, 27, 10, 30, 0), "species": "Great Horned Owl", "confidence": 0.60}, #Example of a bird not seen by ebird ] # --- Main Logic --- for detection in birdnet_detections: detection_time = detection["timestamp"] ebird_data = get_ebird_observations(BIRDNET_LAT, BIRDNET_LON, RADIUS_KM, detection_time, EBIRD_API_KEY) if ebird_data: nearby_ebird_sightings = [] for sighting in ebird_data: sighting_time = datetime.fromtimestamp(sighting["obsDt"].timestamp()) #Convert to datetime object time_difference = abs(detection_time - sighting_time) distance = haversine(BIRDNET_LAT, BIRDNET_LON, sighting["lat"], sighting["lng"]) if time_difference <= timedelta(hours=TIME_WINDOW_HOURS) and distance <= RADIUS_KM: nearby_ebird_sightings.append(sighting["comName"]) birdnet_species = detection["species"] if birdnet_species in nearby_ebird_sightings: print(f"BirdNET-Pi detected {birdnet_species} at {detection_time}, confirmed by eBird.") else: print(f"BirdNET-Pi detected {birdnet_species} at {detection_time}, NOT confirmed by eBird.") print(f" Nearby eBird sightings: {nearby_ebird_sightings}") else: print(f"Could not retrieve eBird data for {detection_time}.") ``` **Explanation of the Python Code:** 1. **Imports:** Imports necessary libraries (requests for API calls, json for handling JSON data, datetime for time manipulation, and math for distance calculation). 2. **Configuration:** Sets up key parameters like the BirdNET-Pi location, search radius, time window, and *your eBird API key*. **Important:** Replace `"YOUR_EBIRD_API_KEY"` with your actual API key. 3. **`haversine()` function:** Calculates the distance between two latitude/longitude points using the Haversine formula. 4. **`get_ebird_observations()` function:** * Constructs the eBird API URL based on the location, radius, and date. * Makes a request to the eBird API. * Parses the JSON response and returns the eBird data. * Includes error handling for API requests. 5. **`birdnet_detections`:** This is a *placeholder*. You'll need to replace this with your actual BirdNET-Pi data. The example shows a list of dictionaries, where each dictionary represents a BirdNET-Pi detection. 6. **Main Logic:** * Iterates through each BirdNET-Pi detection. * Calls `get_ebird_observations()` to retrieve eBird data for the relevant location and date. * Iterates through the eBird sightings and checks if they are within the specified time window and radius. * Compares the BirdNET-Pi species detection with the eBird sightings. * Prints a message indicating whether the BirdNET-Pi detection was confirmed by eBird. **Important Notes:** * **Error Handling:** The code includes basic error handling for the eBird API request. You should add more robust error handling to handle potential issues like network errors, invalid API keys, and unexpected data formats. * **Rate Limiting:** The eBird API has rate limits. Be mindful of these limits and implement appropriate delays or caching to avoid exceeding them. * **Data Cleaning:** Real-world data is often messy. You may need to clean and preprocess your BirdNET-Pi and eBird data to handle missing values, inconsistencies, and errors. * **Statistical Analysis:** For a more rigorous analysis, consider using statistical methods to assess the agreement between BirdNET-Pi and eBird, taking into account factors like detection probability and observer effort. * **Alternative Libraries:** Consider using libraries like `geopy` for more advanced geocoding and distance calculations, and `pandas` for data manipulation and analysis. **Indonesian Translation of Key Concepts:** * **Cross-reference:** *Referensi silang* atau *membandingkan* * **BirdNET-Pi data:** *Data BirdNET-Pi* * **eBird observations:** *Pengamatan eBird* * **Natural language:** *Bahasa alami* * **Validation:** *Validasi* atau *pengesahan* * **Misidentifications:** *Salah identifikasi* * **Concordance:** *Kesesuaian* * **Radius:** *Jari-jari* * **Time window:** *Jendela waktu* * **Species:** *Spesies* atau *jenis burung* * **Confidence score:** *Skor kepercayaan* * **Effort:** *Upaya* atau *usaha* * **False negatives:** *Negatif palsu* * **API Key:** *Kunci API* **Example Indonesian Output (based on the Python code):** ``` BirdNET-Pi mendeteksi Northern Cardinal pada 2024-10-27 07:30:00, dikonfirmasi oleh eBird. BirdNET-Pi mendeteksi American Robin pada 2024-10-27 08:15:00, dikonfirmasi oleh eBird. BirdNET-Pi mendeteksi California Quail pada 2024-10-27 09:00:00, dikonfirmasi oleh eBird. BirdNET-Pi mendeteksi Great Horned Owl pada 2024-10-27 10:30:00, TIDAK dikonfirmasi oleh eBird. Pengamatan eBird terdekat: ['Northern Cardinal', 'American Robin', 'California Quail'] ``` This translates to: ``` BirdNET-Pi detected Northern Cardinal on 2024-10-27 07:30:00, confirmed by eBird. BirdNET-Pi detected American Robin on 2024-10-27 08:15:00, confirmed by eBird. BirdNET-Pi detected California Quail on 2024-10-27 09:00:00, confirmed by eBird. BirdNET-Pi detected Great Horned Owl on 2024-10-27 10:30:00, NOT confirmed by eBird. Nearby eBird sightings: ['Northern Cardinal', 'American Robin', 'California Quail'] ``` Remember to adapt the code and analysis to your specific needs and data. Good luck!

MongoDB Lens

Full featured MCP Server for MongoDB database analysis.

PDF Reader MCP Server

Menyediakan alat untuk membaca dan mengekstrak teks dari file PDF, mendukung baik file lokal maupun URL.

MCP Server Firecrawl

Sebuah server yang menyediakan kemampuan web scraping dan pencarian konten cerdas menggunakan Firecrawl API, memungkinkan agen AI untuk mengekstrak data terstruktur dari situs web dan melakukan pencarian konten.