MCP server directory

Want a ready-to-use AI experience for DataWorks without manual MCP setup? DataWorks Agent is Alibaba Cloud's built-in intelligent assistant for data development and operations. It connects to your DataWorks workspace out of the box, so you can use natural language to explore metadata, develop nodes, troubleshoot tasks, and manage resources—no local MCP server configuration required. 👉 Get started: https://dataworks.data.alibabacloud.com/product/agent?source=github A Model Context Protocol (MCP) server that provides tools for AI, allowing it to interact with the DataWorks Open API through a standardized interface. This implementation is based on the Aliyun Open API and enables AI agents to perform cloud resources operations seamlessly.

4 tools · Explore workspace metadata, Filter available tools by category or name

Single tool to control them all — VeyraX MCP is the only connection you need to access all your tools in any MCP-compatible environment. VeyraX MCP (Model Context Protocol) is an open protocol that allows you to provide custom tools to agentic LLMs. By connecting once to the VeyraX platform, you can use all tools you’ve already integrated with VeyraX in any MCP-compatible environment—such as Claude, Cursor, VS Code, or Windserf—without juggling multiple authentications. - Single Authentication: Connect once in VeyraX, then use your tools across all MCP clients without separate logins or credentials. - Instant Access to All Tools: Any tool you connect to VeyraX is immediately available in your favorite editor or AI assistant. - 5-Minute Setup: Connect VeyraX to any MCP client in under five minutes.

3 tools · Add new tools once and use them everywhere, Connect all your VeyraX tools to any MCP client

To add the MCP server in any config driven MCP Client, following is how the commands and arguments will look like For macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh For Windows: You can also visit the installation steps of uv for more details here You can run the MCP server using uvx using the following command To configure VideoDB Director MCP server in Claude, you can run the following command You can manually configure the MCP Server by following these steps: 1. Open the claude_desktop_config.json file

3 tools · get_video, search_video

The official Trello MCP (Model Context Protocol) is a cloud-based bridge between your Trello account and compatible external AI assistants. Once configured, it lets those tools interact with your Trello boards, cards, and tasks using natural language. With Trello MCP, you can manage work without constantly switching between apps. Your AI assistant can read Trello data, search across it, and take actions on your behalf based on the permissions you grant. At launch: Each Trello MCP connection supports one workspace. Multi-workspace support is planned for a future release.

6 tools · Create and organize boards, Handle checklists

MCP server for the Delinea Secret Server and Platform APIs - 11 Aug 2026 — MCP Protocol v2 (spec revision 2026-07-28, streamable HTTP) and experimental StrongDM API support are here — see the release notes. - 11 Aug 2026 — We're the original providers of the "no secret visibility to the LLM" vault use case — beware of copycats ;) - Automatic authentication against Secret Server - Extensive Secret Server tool set for managing folders, secrets, users, groups and roles. Includes inbox and access request helpers and coding agent utilities. - ChatGPT compatibility tools (search and fetch) for controlled AI interactions. - Optional Delinea Platform user management tools - Optional experimental StrongDM (SDM) tools — access grants, entitlement audits, user/role lifecycle, health and activity reports (see docs/strongdm.md; install with pip install "delinea-mcp[strongdm]") - Streamable HTTP (/mcp), legacy Server-Sent Events (/mcp/sse) and STDIO transports - OAuth 2.0 with dynamic client registr

6 tools · create_secret_with_generated_password, handle_access_request

ai-memory is a persistent memory system for AI assistants. It works with any AI that supports MCP -- Claude, ChatGPT, Grok, Llama, and more. It stores what your AI learns in a local SQLite database, ranks memories by relevance when recalling, and auto-promotes important knowledge to permanent storage. Install it once, and every AI assistant you use remembers your architecture, your preferences, your corrections -- forever. > Configuring the LLM backend (xAI Grok, OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Qwen, Mistral, Groq, Together, Cerebras, OpenRouter, Fireworks, LMStudio, vLLM, llama.cpp server, or local Ollama)? See docs/integrations/llm-backends.md — the MCP env-block recipe is the same regardless of installation path.

5 tools · Coordinate multi-agent workflows, memory_lineage

The Model Context Protocol (MCP) is an open protocol designed to allow AI agents, IDEs, and automation tools to consume, query, and analyze structured data through context-aware APIs. This server wraps Blockscout APIs and exposes blockchain data—balances, tokens, NFTs, contract metadata—via MCP so that AI agents and tools (like Claude, Cursor, or IDEs) can access and analyze it contextually. - Contextual blockchain data access for AI tools - Multi-chain support via Blockscout PRO API configuration with Chainscout metadata enrichment - Versioned REST API: Provides a standard, web-friendly interface to all MCP tools. See API.md for full documentation. - Custom instructions for MCP host to use the server - Intelligent context optimization to conserve LLM tokens while preserving data accessibility - Smart response slicing with configurable page sizes to prevent context overflow - Opaque cursor pagination using Base64URL-encoded strings instead of complex parameters - Automatic truncation o

5 tools · get_address_by_ens_name, get_address_info

> [!NOTE] > This server interacts with the Unstructured API to manage sources, destinations, and workflows. It is not actively maintained and is kept here for reference. > > If you want to parse and transform files into structured output (markdown, JSON, HTML, or plain text), use the Unstructured Transform MCP server instead. > > Unstructured Transform brings production-grade document processing to your agents as a hosted MCP server. It gives them the ability to turn 60+ file types into structured data that's ready for your applications, vector databases, and any downstream processes by parsing, enriching, chunking, and embedding files directly inside their current session. > > You can start using it by signing up here.

5 tools · create_source_connector, invoke_firecrawl_crawlhtml

Airtable is the database and operations layer for your agents — whether running product, marketing, sales, ops, HR, or a custom business app. It combines structured data with multiplayer visual surfaces (grid, kanban, calendar, gallery, timeline) humans and agents share — plus sync integrations to Jira, Salesforce, Zendesk, Google Drive, Databricks, and the rest of your stack, all backed by enterprise governance. This repo is the canonical, official home for the plugins, skills, and other artifacts we ship to make Airtable as fluent to agents as it already is to builders and users. Today that ships as an installable plugin for Claude Code, Codex, and Cursor, and as a standalone collection of skills installable via the skills CLI for any agent that consumes agentskills.io–format skills.

4 tools · create_base, create_record

基于 MCP 协议的腾讯云 COS MCP Server，无需编码即可让大模型快速接入腾讯云存储 (COS) 和数据万象 (CI) 能力。 - 🖼️ 获取图片信息 - 🔍 图片超分辨率 - ✂️ 图片裁剪 - 📲 二维码识别 - 🏆 图片质量评估 - 🅰️ 文字水印 - 🎬 元数据/自然语言检索 (MateInsight) - 📄 文档转 PDF - 🎥 视频封面 - 使用其他 MCP 能力获取的文本/图片/视频/音频等数据，可直接上传到 COS 云端存储。 - 本地数据快速通过大模型转存到 COS 云端存储/备份。 - 通过大模型实现自动化：将网页里的视频/图片/音频/文本等数据批量转存到 COS 云端存储。 - 自动化将视频/图片/音频/文本等数据在云端处理，并转存到 COS 云端存储。 - 说明: 腾讯云 COS 的密钥，用于身份认证，请妥善保管，切勿泄露。 - 获取方式: 1. 访问 腾讯云密钥管理。 2. 新建密钥并复制生成的 SecretId 和 SecretKey。 - 示例: mybucket-123456 - 说明: 存储桶名称，用于存放数据，相当于您的个人存储空间。 - 获取方式: 1. 访问 存储桶列表。 2. 复制存储桶名称。如果没有存储桶，可点击“创建存储桶”，一般选择默认配置即可快速完成创建。

6 tools · Download files from COS, Enhance images with super-resolution

The Membrane MCP Server is a Model Context Protocol (MCP) server, it provides actions for connected integrations on Membrane as tools. Here's our official AI Agent Example that shows you how to use this MCP server in your application. - Node.js (v18 or higher) - A Membrane account To run the development server locally, start it with: The server will be live at http://localhost:3000 ⚡️ Deploy your own instance of this MCP server to any cloud hosting service of your choice. The project includes a Dockerfile for easy containerized deployment.

4 tools · apps, Authenticate with a Membrane token

Official Model Context Protocol server for OpenWeb Ninja APIs. Gives any MCP-compatible AI agent (Claude, Cursor, Cline, and others) real-time access to web search, local business data, jobs, e-commerce, real estate, finance, news, and more, through a single connection. One tool per API product (41 tools), each exposing that API's operations, plus a subscribe tool for adding an API's free tier on demand — 42 tools in total. Schemas are generated directly from OpenWeb Ninja's OpenAPI specs, so the server always matches the live APIs.

6 tools · jsearch, local_business_data

- AppControl running with MCP API enabled - Windows only There are two ways to install the server: as an MCPB package (recommended for Claude Desktop) or as a standalone executable. 1. Download appcontrol.mcpb from the latest release. 2. Open Claude Desktop app, go to Settings -> Extensions -> Advanced Settings and click "Install Extension". Choose your newly downloaded .mcpb file — Claude Desktop will install it automatically. 3. Restart Claude Desktop app by right-clicking the Claude tray icon and selecting "Quit" then start it again.

6 tools · get_binary, get_monitoring

An official implementation of an MCP (Model Context Protocol) server for ScreenshotOne. A few more words about why it was built and some thoughts about the future of MCP. ScreenshotOne also offers an official hosted MCP server. See the ScreenshotOne MCP integration for setup instructions and the latest updates. - render-website-screenshot: Render a website screenshot and return an image or cached response. - get-usage: Get ScreenshotOne API quota and concurrency usage. Always install dependencies and build it first:

2 tools · Check API usage, Render website screenshots

Connect LLMs and AI agents to live web data using MCP (Model Context Protocol). The Decodo MCP Server lets you scrape websites, search engines, eCommerce platforms, and social media directly from AI tools like Claude, Cursor, and Windsurf, all without the need to build scraping infrastructure from scratch. - Structured outputs in JSON, Markdown, and screenshots - Server-side JavaScript rendering and anti-bot handling - 125M+ IPs across 195+ locations The Decodo MCP Server is a web scraping layer for AI agents. It connects MCP-compatible clients to Decodo's Web Scraping API, enabling:

6 tools · amazon_pricing, chatgpt

A Model Context Protocol server for retrieving and analyzing data or running tasks for Ramp using Developer API. In order to get around token and input size limitations, this server implements a simple ETL pipeline + ephemeral sqlite database in memory for analysis by an LLM. All requests are made to demo by default, but can be changed by setting RAMP_ENV=prd. Large datasets may not be processable due to API and/or your MCP client limitations. Tools that can be used to setup, process, query, and delete an ephemeral database in memory.

4 tools · execute_query, get_ramp_categories

The MCP server for Bitrix24 provides AI assistants with structured access to the Bitrix24 API. It delivers up-to-date method descriptions, parameters, and valid values, allowing assistants to work with precise data instead of guesswork. This reduces code errors and accelerates Bitrix24 integration development.

Hologres MCP Server serves as a universal interface between AI Agents and Hologres databases. It enables seamless communication between AI Agents and Hologres, helping AI Agents retrieve Hologres database metadata and execute SQL operations. Add the following configuration to the MCP client configuration file: Install MCP Server using the following package: Add the following configuration to the MCP client configuration file: The server supports Streamable HTTP transport for remote deployment scenarios where STDIO is not available.

6 tools · execute_hg_ddl_sql, execute_hg_select_sql

> [!WARNING] > This local MCP server is deprecated. Use the hosted Atlan MCP at mcp.atlan.com/mcp instead. > > The local install path (Docker, uv, or pip install atlan-mcp-server) is in maintenance-only mode — no new features, support not guaranteed. The hosted endpoint is the recommended way to integrate Atlan with Claude Desktop, Cursor, Codex, Databricks UC, and other MCP clients. See the Atlan MCP overview for setup. The Atlan Model Context Protocol server allows your AI agents to interact with Atlan services.

6 tools · create_dq_rules, create_glossaries

Expose 2slides.com tools for MCP clients (e.g., Claude Desktop). Before using this MCP server, you need to obtain an API key from 2slides.com/api. Edit ~/Library/Application Support/Claude/claude_desktop_config.json and add: json { "mcpServers": { "2slides": { "command": "npx", "args": ["2slides-mcp"], "env": { "API_KEY": "YOUR_2SLIDES_API_KEY" } } } } Then fully restart Claude Desktop. In a chat, open the tools panel and you should see the tools below. Generate a PowerPoint presentation using a pre-built theme (Fast PPT). Use themes_search first to find a themeId.

6 tools · slides_create_like_this, slides_create_pdf_slides

Model Context Protocol) (MCP) is a standardized protocol designed to manage context between large language models (LLMs) and external systems. This repository provides an installer and an MCP Server for Singlestore, enabling seamless integration. With MCP, you can use Claude Desktop, Claude Code, Cursor, or any compatible MCP client to interact with SingleStore using natural language, making it easier to perform complex operations effortlessly. 💡 Pro Tip: Not sure what the MCP server can do? Just call the /help prompt in your chat!

5 tools · create_notebook_file, get_user_info

This repository contains code to locally run an MCP server that can access [Scout Monitoring](https://www.scoutapm.com) data via Scout's API. We provide a Docker image that can be pulled and run by your AI Assistant to access Scout Monitoring data. This puts Scout Monitoring's performance and error data directly in the hands of your AI Assistant. For Rails, Django, FastAPI, Laravel and more. Use it to get traces and errors with line-of-code information that the AI can use to target fixes right in your editor and codebase. N+1 queries, slow endpoints, slow queries, memory bloat, throughput issues - all your favorite performance problems surfaced and explained right where you are working.

5 tools · get_app_endpoints, get_app_error_groups

> [!WARNING] > > This was a prototype GoLang implementation of an MCP (Model Context Protocol) server for incident.io, providing comprehensive tools to interact with the incident.io API. Built following industry-standard Go project layout patterns. > > In March 2026, it was superseded by an officially supported MCP Server. > > Read more and configure it here: https://docs.incident.io/ai/remote-mcp > > We're not maintaining or accepting further pull requests or changes to this repository going forward. > > Thank you to everyone who provided feedback in the past. Please direct all future MCP related feedback to support@incident.io 🙏

This local MCP server is deprecated. We support Remote MCP Server now, which can be connected via this url: https://mcp.fibery.io/mcp. See the Fibery MCP Server guide for setup instructions. This repository will no longer receive feature updates. Please migrate to the remote server. To install Fibery MCP Server for Claude Desktop automatically via Smithery: - A Fibery account with an API token - Python 3.10 or higher - uv 1. Install the tool using uv: bash uv tool install fibery-mcp-server 2. Then, add this configuration to your MCP client config file. In Claude Desktop, you can access the config in Settings → Developer → Edit Config: json { "mcpServers": { "fibery-mcp-server": { "command": "uv", "args": [ "tool", "run", "fibery-mcp-server", "--fibery-host", "your-domain.fibery.io", "--fibery-api-token", "your-api-token" ] } } } Note: If "uv" command does not work, try absolute path (i.e. /Users/username/.local/bin/uv)

3 tools · fibery_command, fibery_graphql

- 🔍 Hybrid search — BM25 (full-text) + vector KNN (pgvector) fused via Reciprocal Rank Fusion - 📂 Multi-user — each user sees only their own memories; workspaces for shared team knowledge - 🛠️ 11 MCP tools — store, retrieve, recall, list, search by tag, get, update, delete, move, list workspaces, dream report - 💤 Dreaming — a guided consolidation pass (dream MCP prompt + dream_report tool): finds near-duplicate and conflicting memories, merges them into insights non-destructively, driven by the client LLM - 🌐 Web UI — browse, search, edit and delete memories in the browser; export/import JSON - 🔒 OAuth 2.1 — full in-process authorization server, always enabled - 🐘 PostgreSQL — pgvector for embeddings, tsvector for full-text search - 📊 Progressive disclosure — search returns compact snippets by default; call get_memory(name) only for what you need, reducing token usage - 🔁 Deduplication — automatically detects near-identical memories before saving, preventing noise accumulation

5 tools · dream, list_memories

This repository contains the implementation of the Model Context Protocol (MCP) server for the EduBase platform. It allows MCP clients (for example Claude Desktop) and LLMs to interact with your EduBase account and perform tasks on your behalf. It supports stdio, SSE and streamable HTTP transport protocols. EduBase is an innovative, modular, online educational platform that makes learning more enjoyable, simpler and interactive, suitable for educational institutions or enterprises. EduBase revolutionizes digital learning with its unique combination of features:

5 tools · Analyze exam results, edubase_filebin

Command-line interface for interacting with Reflag services. The CLI allows you to manage apps, flags, authentication, and generate TypeScript types for your Reflag flags. With this tool, you can streamline your flagging workflow directly from your terminal. Install the CLI as a development dependency in your project: Run the new command from your project's root directory to initialize the CLI, create a flag, and generate TypeScript types in one step: If you're migrating from the Bucket CLI, here are the key changes to be aware of:

6 tools · Configure MCP connection, Create a new flag

Inkeep MCP Server powered by your docs and product content. - An account on Inkeep to manage and provide the RAG - uv Python project manager Note the full path of the project, referred to as <YOUR_INKEEP_MCP_SERVER_ABSOLUTE_PATH> in a later step. 1. Log in to the Inkeep Dashboard 2. Navigate to the Projects section and select your project 3. Open the Integrations tab 4. Click Create Integration and choose API from the options 5. Enter a Name for your new API integration. 6. Click on Create 7. A generated API key will appear that you can use to authenticate API requests.

3 tools · Get API integration details, Retrieve setup instructions

- MCP Server for IBM Instana - 📚 Quick Links - Architecture Overview - Workflow - Prerequisites - Option 1: Install from PyPI (Recommended) - Option 2: Development Installation - Installing uv - Setting Up the Environment - Header-Based Authentication for Streamable HTTP Mode - 1. API Token Authentication (Direct API Calls) - 2. Session Token Authentication (UI-Initiated Calls) - 3. JWT Token Authentication (IBM Platform Integration) - Starting the Local MCP Server - Server Command Options - Using the CLI (PyPI Installation) - Using Development Installation - Starting in Streamable HTTP Mode - Using CLI (PyPI Installation) - Using Development Installation - Starting in Stdio Mode - Using CLI (PyPI Installation) - Using Development Installation - Tool Categories - Using CLI (PyPI Installation) - Using Development Installation - SSL Certificate Verification - Using the CLI option - Using the environment variable - Using a custom CA bundle - Verifying Server Status - Common Startup Issues

5 tools · app, events

The open-source GEO engine that gets your brand mentioned in ChatGPT, Claude, Gemini, Perplexity, and Grok. Audit, generate, fix, and track the pages large language models cite — one CLI, file-based, MIT. When someone asks ChatGPT, Perplexity, Claude, Gemini, or Google AI Overviews a question your business should answer, do those engines cite your domain? auto-geo is the full loop for making that happen and proving it's happening: Everything is file-based and committable — tracked prompts, check history, config. No server, no account, no database. One install away:

6 tools · check, doctor

A purpose-driven Model Context Protocol (MCP) server for managing Unleash feature flags. This server enables LLM-powered coding assistants to create and manage feature flags following Unleash best practices. To share feedback, join our community Slack or open an issue on GitHub. This MCP server provides tools that integrate with the Unleash Admin API, allowing AI coding assistants to: - Create feature flags with proper validation and typing. - Detect existing flags to prevent duplicates or encourage reuse. - Evaluate changes to decide when a feature flag is needed. - Stream progress for visibility during operations. - Handle errors gracefully with helpful hints. - Follow best practices from the Unleash documentation.

6 tools · create_flag, detect_flag

This MCP server integrates with Fewsats and allows AI Agents to purchase anything in a secure way. 1. balance - Retrieve the balance of the user's wallet - Input: None - Returns: Current wallet balance information 2. payment_methods - Retrieve the user's payment methods - Input: None - Returns: List of available payment methods 3. pay_offer - Pays an offer with the specified ID from the l402_offers - Input: - offer_id (string): String identifier for the offer - l402_offer (object): Offer details containing: - offers: Array of offer objects with ID, amount, currency, description, title - payment_context_token: Payment context token string - payment_request_url: URL for payment request - version: API version string - Returns: Payment status response

4 tools · balance, pay_offer

> The official Apollo.io Model Context Protocol server for prospecting, lead enrichment, sales intelligence, and outreach workflows. Provider: Official, first-party server built and maintained by Apollo.io. Bring Apollo.io prospecting, enrichment, and outreach directly into your favorite MCP-compatible AI application — GitHub Copilot, Visual Studio Code, Cursor, Claude Code, Claude Desktop, and Claude Cowork. - Apollo.io: <https://www.apollo.io/> - Model Context Protocol: <https://modelcontextprotocol.io/> - License: MIT

5 tools · apollo_analytics_sync_report, apollo_emailer_campaigns_add_contact_ids

>The Audiense Insights MCP has been migrated to a remote model. For more information on how to use the new remote MCP, please reach us at support@audiense.com. This server, based on the Model Context Protocol (MCP), allows Claude or any other MCP-compatible client to interact with your Audiense Insights account. It extracts marketing insights and audience analysis from Audiense reports, covering demographic, cultural, influencer, and content engagement analysis. Before using this server, ensure you have:

5 tools · Analyze content engagement, Compare influencers against a baseline

Let Your Agents Instantly Understand Any GitHub Repo An MCP server that gives coding agents instant insight into any Git repository — no guessing, no hallucination. Share the following with your agent to get started: Works with Claude Code, Claude Desktop, Cursor, OpenClaw, and any MCP-compatible client. The @latest tag ensures you always get the most recent version. Agents that integrate with external libraries are flying blind. They read docs (if they exist), guess at APIs, and hallucinate patterns that don't match the actual code. The result: broken integrations, wrong function signatures, outdated usage patterns, hours of debugging.

3 tools · ask_repo, Get architecture explanations with file citations

Reporting across 19 ad platforms and full campaign creation on 14 of them (write actions on 16), spanning every major buying channel, with built-in confirmations for destructive actions. Create campaigns. Adjust budgets. Pause underperformers. Generate creatives. Pull performance data. All through natural conversation, across Google, Meta, LinkedIn, Microsoft, Reddit, TikTok, X, and more. This is the first Model Context Protocol (MCP) server that gives AI agents a credit card. The official Google Ads MCP server is read-only by design: per Google's documentation, it is "strictly read-only" and "cannot modify bids, pause campaigns, or create new assets." Most third-party ad MCP servers cover a single platform. We built Synter to do both halves of the job, across every major buying channel, from one server.

6 tools · create_search_campaign, generate_image