Discover Awesome MCP Servers

long-context-mcp

An MCP server implementing Recursive Language Models (RLM) to process arbitrarily large contexts through a programmatic probe, recurse, and synthesize loop. It enables LLMs to perform multi-step investigations and evidence-backed extraction across massive file sets without being limited by standard context windows.

aris-md/mcp

A minimal, well-structured MCP server implementation for learning and experimentation that exposes three tools: web search, API search, and client ID processing. It demonstrates clean separation between tool, transport, and LLM layers while supporting multiple AI clients through the Model Context Protocol standard.

stockdata-mcp

An MCP server for stock research with 52 tools across FMP and Qualtrim backends, providing raw financial data plus derived analytics, DCF, AI commentary, and portfolio management. It handles caching, API budgeting, and credential management.

willow-mcp

An agent-neutral MCP server providing SQLite key/value storage, Postgres knowledge base, and Kart task queue functionality. Features SAP/1.0 authorization on every tool call for secure multi-application access.

MCP Web Chat

A server that enables WebChat functionality through MCP (Model-Control-Protocol), solving long-term connection issues while providing both common method calls and business API integration capabilities.

mcp-stackexchange

Wraps the StackExchange API v2.3 to enable reading StackExchange data (questions, answers, etc.) without authentication. Allows AI agents to query StackExchange content through natural language or direct tool calls.

Directmedia MCP

Provides programmatic access to the Directmedia Publishing 'Digitale Bibliothek' collection, a 1990s German electronic book library containing 101 volumes of classic literature and philosophy with text extraction, search, and navigation capabilities.

Karbon MCP Server

An unofficial Model Context Protocol server that connects MCP-compatible clients to Karbon practice management, enabling natural language interaction with contacts, work items, notes, timesheets, and more.

meta-ads-mcp

A self-hosted Model Context Protocol (MCP) server that gives AI agents secure, multi-tenant access to the Meta Marketing API for Facebook Ads and Instagram Ads. It enables campaign management, audience targeting, insights, lead management, and more across multiple ad accounts.

Sample Model Context Protocol Demos

Okay, here are some examples of how to use the Model Context Protocol with AWS, translated into Indonesian: **Judul: Kumpulan Contoh Penggunaan Protokol Konteks Model dengan AWS** **Pendahuluan:** Protokol Konteks Model (Model Context Protocol) adalah cara untuk menyediakan informasi kontekstual ke model machine learning Anda saat di-deploy. Informasi ini dapat mencakup data konfigurasi, kredensial, atau metadata lainnya yang dibutuhkan model untuk beroperasi dengan benar. Dengan AWS, Anda dapat memanfaatkan berbagai layanan untuk mengelola dan menyediakan konteks model ini. **Contoh 1: Menyediakan Kredensial AWS ke Model yang Berjalan di Amazon SageMaker** * **Bahasa Inggris:** "Let's say you have a model deployed on Amazon SageMaker that needs to access data from an S3 bucket. Instead of hardcoding the AWS credentials into the model code, you can use the SageMaker execution role to provide the necessary permissions. The model can then use the AWS SDK to assume the role and access the S3 bucket." * **Bahasa Indonesia:** "Katakanlah Anda memiliki model yang di-deploy di Amazon SageMaker yang perlu mengakses data dari bucket S3. Alih-alih memasukkan kredensial AWS secara langsung (hardcoding) ke dalam kode model, Anda dapat menggunakan peran eksekusi SageMaker untuk menyediakan izin yang diperlukan. Model kemudian dapat menggunakan AWS SDK untuk mengambil peran tersebut dan mengakses bucket S3." **Penjelasan:** * **SageMaker Execution Role:** Peran IAM yang diberikan ke instance SageMaker. Ini memberikan izin kepada instance untuk mengakses layanan AWS lainnya. * **AWS SDK:** Perpustakaan (library) yang memungkinkan model Anda berinteraksi dengan layanan AWS. * **Keuntungan:** Keamanan yang lebih baik (tidak ada kredensial yang di-hardcode), manajemen kredensial yang terpusat. **Contoh 2: Menggunakan AWS Secrets Manager untuk Menyimpan dan Mengakses Kunci API** * **Bahasa Inggris:** "Your model might need to call an external API that requires an API key. You can store the API key securely in AWS Secrets Manager and then retrieve it from your model at runtime. This prevents the API key from being exposed in your code or configuration files." * **Bahasa Indonesia:** "Model Anda mungkin perlu memanggil API eksternal yang memerlukan kunci API. Anda dapat menyimpan kunci API dengan aman di AWS Secrets Manager dan kemudian mengambilnya dari model Anda saat runtime. Ini mencegah kunci API terekspos dalam kode atau file konfigurasi Anda." **Penjelasan:** * **AWS Secrets Manager:** Layanan untuk menyimpan dan mengelola rahasia (secrets) seperti kunci API, kata sandi database, dan sertifikat. * **Runtime:** Waktu ketika model sedang berjalan dan memproses data. * **Keuntungan:** Keamanan yang ditingkatkan, rotasi rahasia yang mudah. **Contoh 3: Menggunakan AWS Systems Manager Parameter Store untuk Menyimpan Konfigurasi Model** * **Bahasa Inggris:** "You can use AWS Systems Manager Parameter Store to store configuration parameters for your model, such as the learning rate, batch size, or the path to a pre-trained model. This allows you to easily update the configuration without redeploying the model." * **Bahasa Indonesia:** "Anda dapat menggunakan AWS Systems Manager Parameter Store untuk menyimpan parameter konfigurasi untuk model Anda, seperti learning rate, ukuran batch, atau path ke model yang sudah dilatih sebelumnya (pre-trained model). Ini memungkinkan Anda untuk dengan mudah memperbarui konfigurasi tanpa perlu melakukan redeploy model." **Penjelasan:** * **AWS Systems Manager Parameter Store:** Layanan untuk menyimpan data konfigurasi dan rahasia. * **Learning Rate, Batch Size:** Contoh parameter yang sering digunakan dalam machine learning. * **Keuntungan:** Manajemen konfigurasi yang terpusat, pembaruan konfigurasi yang mudah. **Contoh 4: Menggunakan Amazon DynamoDB untuk Menyimpan Metadata Model** * **Bahasa Inggris:** "You can store metadata about your model in Amazon DynamoDB, such as the model version, training data used, and performance metrics. This metadata can be used for model tracking, auditing, and debugging." * **Bahasa Indonesia:** "Anda dapat menyimpan metadata tentang model Anda di Amazon DynamoDB, seperti versi model, data pelatihan yang digunakan, dan metrik kinerja. Metadata ini dapat digunakan untuk pelacakan model, audit, dan debugging." **Penjelasan:** * **Amazon DynamoDB:** Database NoSQL yang cepat dan scalable. * **Metadata:** Data tentang data (dalam hal ini, data tentang model). * **Keuntungan:** Pelacakan model yang lebih baik, kemampuan audit, dan debugging yang lebih mudah. **Contoh 5: Menggunakan AWS Lambda untuk Menyediakan Konteks Model Dinamis** * **Bahasa Inggris:** "You can use AWS Lambda to create a function that dynamically retrieves context information for your model based on the input data. For example, the Lambda function could retrieve user-specific data from a database and pass it to the model as context." * **Bahasa Indonesia:** "Anda dapat menggunakan AWS Lambda untuk membuat fungsi yang secara dinamis mengambil informasi konteks untuk model Anda berdasarkan data input. Misalnya, fungsi Lambda dapat mengambil data spesifik pengguna dari database dan meneruskannya ke model sebagai konteks." **Penjelasan:** * **AWS Lambda:** Layanan komputasi tanpa server (serverless) yang memungkinkan Anda menjalankan kode tanpa menyediakan atau mengelola server. * **Konteks Dinamis:** Informasi konteks yang berubah berdasarkan input. * **Keuntungan:** Fleksibilitas yang tinggi, kemampuan untuk menyediakan konteks yang dipersonalisasi. **Kesimpulan:** Contoh-contoh di atas menunjukkan beberapa cara untuk menggunakan Protokol Konteks Model dengan AWS. Dengan memanfaatkan layanan AWS seperti SageMaker, Secrets Manager, Parameter Store, DynamoDB, dan Lambda, Anda dapat mengelola dan menyediakan konteks model dengan aman dan efisien. Pilihan layanan yang tepat akan bergantung pada kebutuhan spesifik model dan aplikasi Anda. **Catatan:** Pastikan untuk selalu mengikuti praktik terbaik keamanan AWS saat mengelola kredensial dan data sensitif.

wiki-mcp-server

A lightweight personal wiki MCP server that allows AI assistants to save, search, and link markdown notes with backlinks and full-text search, functioning as a file-based second brain.

Verified Wages MCP

Provides current, source-cited US minimum wage, tipped wage, and FLSA overtime data for AI agents, with tools to query rates, verify wage floors, and list scheduled changes. It refreshes state-level wage information from DOL sources and refuses to guess when data is unavailable.

MFlowy

Enables users to interact with MFlowy, an MCP-native modular ML workflow engine with data analysis, model training, and orchestration.

insights-mcp-server

Here are a few possible translations, depending on the context: * **Red Hat Insights MCP Server POC:** This is the most direct translation and likely the best if the audience is familiar with the acronyms and technical terms. * **POC Server MCP Red Hat Insights:** (Less common, but possible if emphasizing the "Proof of Concept" aspect) * **Proof of Concept (POC) Server MCP Red Hat Insights:** (More explicit, spelling out "Proof of Concept") **Explanation of Choices:** * **POC:** "Proof of Concept" is often used directly in Indonesian technical contexts, or abbreviated as "POC." * **MCP Server:** "MCP Server" is likely best left as is, unless you know what "MCP" stands for and can translate that appropriately. * **Red Hat Insights:** This is a product name and should generally be left as is. **Recommendation:** Unless you have a specific reason to do otherwise, I recommend using the first option: **Red Hat Insights MCP Server POC** This is the clearest and most concise translation for a technical audience.

mcp-watermelon

MCP server for Watermelon.ai that exposes all 13 public API endpoints as tools, enabling AI assistants to manage contacts, conversations, messages, custom fields, and webhooks.

web-ai-mcp

Provides free access to AI models (GPT-4o mini, Claude 3 Haiku, Llama 3.1) via DuckDuckGo AI chat without login, using stealth browser automation.

pyMSO5000 MCP Server

Enables AI agents to control Rigol MSO5000 oscilloscopes through VISA, including acquisition, channels, trigger, timebase, waveform generator, display, and front-panel controls, with risk-based permission gating for direct SCPI operations.

flare-mcp

MCP server for Flare Network enabling natural language queries of FTSO price feeds, FAssets, balances, and FDC attestations.

Ashfords Law Firm MCP Server

Enables law firm staff to automate case intake, conflict-of-interest checks, and attorney assignment through secure MCP tools without exposing sensitive data directly to LLMs.

TestMu AI Test Manager MCP

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

@nestr/mcp

MCP server that connects AI assistants like Claude to your Nestr workspace, enabling task, project, role management, and organizational insights through natural language.

delivery-intelligence-mcp

Enables delivery leads to query explainable programme health, prioritized risks, dependency impacts, change request effects, blocked decisions, and evidence-backed claims with refusal on unsupported assertions, all via deterministic tools and telemetry.

Toy MCP Server

A simple reference implementation demonstrating MCP server basics with two toy tools: generating random animals and simulating 20-sided die rolls.

Velo Payments API MCP Server

An MCP server that enables interaction with Velo Payments APIs for global payment operations, automatically generated using AG2's MCP builder from the Velo Payments OpenAPI specification.

responsible-gambling-mcp

Enables users to calculate safe gambling budgets based on financial situation and assess gambling habits with risk levels and recommendations.

mcp-server-starter-demo

A minimal TypeScript MCP server that provides echo and text_stats tools for text validation and word/character counting over stdio.

mcp-interaction-studio

MCP server for Salesforce Interaction Studio that enables listing and managing datasets, campaigns, segments, and performance stats via natural language.

LeetCode MCP (Model Context Protocol)

Okay, I understand. You want me to translate the phrase "MCP Server to generate Leetcode Notes" into Indonesian. Here's the translation: **Server MCP untuk menghasilkan Catatan Leetcode** Here's a breakdown of why this translation works: * **MCP Server:** This is kept as "Server MCP" because "MCP" is likely an acronym or proper noun and is often left untranslated. * **to generate:** This translates to "untuk menghasilkan" (to produce/to generate). * **Leetcode Notes:** This is translated to "Catatan Leetcode". "Notes" becomes "Catatan" (notes), and "Leetcode" is kept as is, as it's a proper noun. Therefore, the most natural and accurate translation is: **Server MCP untuk menghasilkan Catatan Leetcode**

diff-explainer

AI-powered git diff analysis with human-readable explanations, risk flags, and review checklists. Enables to explain any diff text or currently staged git changes through an MCP server.

Jira Extended MCP Server

Enables AI agents to manage Jira Cloud projects with full CRUD operations, bulk actions, sprint and release management, and issue linking using natural language.