ApeRAG - Alternatives & Competitors
Hybrid RAG platform with MCP integration for intelligent knowledge management
ApeRAG is a production-ready Retrieval-Augmented Generation (RAG) platform that integrates graph-based, vector, and full-text search capabilities. It enables the construction of knowledge graphs and supports MCP (Model Context Protocol), allowing AI assistants direct interaction with knowledge bases. Features include advanced document parsing, multimodal processing, intelligent agent workflows, and enterprise management tools. Deployment is streamlined via Docker and Kubernetes, with extensive support for customization and scalability.
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Graphlit MCP Server
Integrate and unify knowledge sources for RAG-ready AI context with the Graphlit MCP Server.
Graphlit MCP Server provides a Model Context Protocol interface, enabling seamless integration between MCP clients and the Graphlit platform. It supports ingestion from a wide array of sources such as Slack, Discord, Google Drive, email, Jira, and GitHub, turning them into a searchable, RAG-ready knowledge base. Built-in tools allow for document, media extraction, web crawling, and web search, as well as advanced retrieval and publishing functionalities. The server facilitates easy configuration, sophisticated data operations, and automated notifications for diverse workflows.
369 49 MCP -
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Ragie Model Context Protocol Server
Seamless knowledge base retrieval via Model Context Protocol for enhanced AI context.
Ragie Model Context Protocol Server enables AI models to access and retrieve information from a Ragie-managed knowledge base using the standardized Model Context Protocol (MCP). It provides a retrieve tool with customizable query options and supports integration with tools like Cursor and Claude Desktop. Users can configure API keys, specify partitions, and override tool descriptions. Designed for rapid setup via npx and flexible for project-specific or global usage.
81 18 MCP -
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RAG Documentation MCP Server
Vector-based documentation search and context augmentation for AI assistants
RAG Documentation MCP Server provides vector-based search and retrieval tools for documentation, enabling large language models to reference relevant context in their responses. It supports managing multiple documentation sources, semantic search, and real-time context delivery. Documentation can be indexed, searched, and managed with queueing and processing features, making it highly suitable for AI-driven assistants. Integration with Claude Desktop and support for Qdrant vector databases is also available.
238 29 MCP -
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Driflyte MCP Server
Bridging AI assistants with deep, topic-aware knowledge from web and code sources.
Driflyte MCP Server acts as a bridge between AI-powered assistants and diverse, topic-aware content sources by exposing a Model Context Protocol (MCP) server. It enables retrieval-augmented generation workflows by crawling, indexing, and serving topic-specific documents from web pages and GitHub repositories. The system is extensible, with planned support for additional knowledge sources, and is designed for easy integration with popular AI tools such as ChatGPT, Claude, and VS Code.
9 2 MCP -
5
MCP Local RAG
Privacy-first local semantic document search server for MCP clients.
MCP Local RAG is a privacy-preserving, local document search server designed for use with Model Context Protocol (MCP) clients such as Cursor, Codex, and Claude Code. It enables users to ingest and semantically search local documents without using external APIs or cloud services. All processing, including embedding generation and vector storage, is performed on the user's machine. The tool supports document ingestion, semantic search, file management, file deletion, and system status reporting through MCP.
10 3 MCP -
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Trieve
All-in-one solution for search, recommendations, and RAG.
Trieve offers a platform for semantic search, recommendations, and retrieval-augmented generation (RAG). It supports dense vector search, typo-tolerant neural search, sub-sentence highlighting, and integrates with a variety of embedding models. Trieve can be self-hosted and features APIs for context management with LLMs, including Bring Your Own Model and managed RAG endpoints. Full documentation and SDKs are available for streamlined integration.
2,555 229 MCP -
7
Vectara MCP Server
Secure RAG server enabling seamless AI integration via Model Context Protocol.
Vectara MCP Server implements the open Model Context Protocol to enable AI systems and agentic applications to connect securely with Vectara's Trusted RAG platform. It supports multiple transport modes, including secure HTTP, Server-Sent Events (SSE), and local STDIO for development. The server provides fast, reliable retrieval-augmented generation (RAG) operations with built-in authentication, rate limiting, and optional CORS configuration. Integration is compatible with Claude Desktop and any other MCP client.
25 8 MCP -
8
mcp-local-rag
Local RAG server for web search and context injection using Model Context Protocol.
mcp-local-rag is a local server implementing the Model Context Protocol (MCP) to provide retrieval-augmented generation (RAG) capabilities. It performs live web search, extracts relevant context using Google's MediaPipe Text Embedder, and supplies the information to large language models (LLMs) for enhanced, up-to-date responses. The tool is designed for easy local deployment, requiring no external APIs, and is compatible with multiple MCP clients. Security audits are available, and integration is demonstrated across several LLM platforms.
89 18 MCP -
9
Biel.ai MCP Server
Seamlessly connect IDEs to your company’s product documentation using an MCP server.
Biel.ai MCP Server enables AI tools such as Cursor, VS Code, and Claude Desktop to access and utilize a company’s product documentation and knowledge base through the Model Context Protocol. It provides a hosted RAG layer that makes documentation searchable and usable, supporting real-time, context-rich completion and answers for developers. The server can be used as a hosted solution or self-hosted locally or via Docker for advanced customization.
2 2 MCP -
10
Zettelkasten MCP Server
A Zettelkasten-based knowledge management system implementing the Model Context Protocol.
Zettelkasten MCP Server provides an implementation of the Zettelkasten note-taking methodology, enriched with bidirectional linking, semantic relationships, and categorization of notes. It enables creation, exploration, and synthesis of atomic knowledge using MCP for AI-assisted workflows. The system integrates with clients such as Claude and supports markdown, advanced search, and a structured prompt framework for large language models. The dual storage architecture and synchronous operation model ensure flexibility and reliability for managing personal or collaborative knowledge bases.
114 21 MCP -
11
Agentic Long-Term Memory with Notion Integration
Production-ready agentic long-term memory and Notion integration with Model Context Protocol support.
Agentic Long-Term Memory with Notion Integration enables AI agents to incorporate advanced long-term memory capabilities using both vector and graph databases. It offers comprehensive Notion workspace integration along with a production-ready Model Context Protocol (MCP) server supporting HTTP and stdio transports. The tool facilitates context management, tool discovery, and advanced function chaining for complex agentic workflows.
4 2 MCP -
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OpenAI WebSearch MCP Server
Intelligent web search with OpenAI reasoning model support, fully MCP-compatible.
OpenAI WebSearch MCP Server provides advanced web search functionality integrated with OpenAI's latest reasoning models, such as gpt-5 and o3-series. It features full compatibility with the Model Context Protocol, enabling easy integration into AI assistants that require up-to-date information and contextual awareness. Built with flexible configuration options, smart reasoning effort controls, and support for location-based search customization. Suitable for environments such as Claude Desktop, Cursor, and automated research workflows.
75 18 MCP -
13
Octagon Deep Research MCP
AI-powered, enterprise-grade deep research server for MCP clients.
Octagon Deep Research MCP provides specialized AI-driven research and analysis via seamless integration with MCP-enabled applications. It offers comprehensive multi-source data aggregation, advanced analysis tools, and generates in-depth reports across various domains. The solution emphasizes high performance with no rate limits, enterprise-grade speed, and universal compatibility for teams needing thorough research capabilities.
70 12 MCP -
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MemoryMesh
A knowledge graph server for structured AI memory and context management.
MemoryMesh is a knowledge graph server designed to help AI models maintain structured, consistent memory, especially for interactive storytelling and RPG contexts. It is based on the Model Context Protocol (MCP), as explicitly stated, and retains core MCP server functionalities. By utilizing dynamic, schema-based configuration, the server enables creation and management of nodes and relationships, offering comprehensive tools for data integrity, feedback, and event tracking. MemoryMesh emphasizes flexibility, supporting both predefined and dynamic schemas for guiding AI interactions.
313 43 MCP -
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MCP-searxng
MCP server bridging agentic systems with SearXNG web search
MCP-searxng enables agentic systems to interface with web search engines via the SearXNG platform by implementing the Model Context Protocol. It supports both command-line and local server deployment, providing flexible integration options. Users can configure custom SearXNG server URLs and connect through clients like uvx or claude desktop. The tool simplifies access to structured web search within agentic workflows.
107 19 MCP -
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MCP Server for the RAG Web Browser Actor
Local MCP server enabling LLMs to browse and extract web content via RAG Web Browser integration.
Implements a Model Context Protocol (MCP) server designed for integration with LLMs and Retrieval-Augmented Generation (RAG) pipelines, providing automated web search and web page extraction capabilities. Runs locally and interacts with the RAG Web Browser Actor in Standby mode, responding to queries by fetching, scraping, and returning cleaned content from the web as Markdown. Supports Google Search queries and direct URL fetching through a standardized 'search' tool interface, offering multiple output formats and programmable arguments. Deprecated in favor of mcp.apify.com but illustrates MCP server use for local web browsing integrations.
194 25 MCP -
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GIS MCP Server
Empower AI with advanced geospatial operations via Model Context Protocol.
GIS MCP Server provides a Model Context Protocol (MCP) server implementation that enables Large Language Models to access and perform sophisticated GIS operations. It bridges AI assistants with Python geospatial libraries such as Shapely, GeoPandas, PyProj, Rasterio, and PySAL. The server supports a wide range of spatial analysis, coordinate transformations, raster and vector data processing, and geospatial intelligence tasks. By integrating with MCP-compatible clients, it enhances AI tools with precise and extensible spatial capabilities.
70 21 MCP -
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RAE Model Context Protocol (MCP) Server
An MCP server enabling LLMs to access RAE’s dictionary and linguistic resources.
Provides a Model Context Protocol (MCP) server implementation for the Royal Spanish Academy API, facilitating integration with language models. Offers tools such as search and word information retrieval, exposing RAE’s dictionary and linguistic data to LLMs. Supports multiple transports including stdio and SSE, making it suitable for both direct and server-based LLM interactions.
3 3 MCP -
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Pearch.ai MCP
Natural Language People Search and Candidate Sourcing API for Seamless ATS Integration
Pearch.ai MCP provides a high-precision people search API that interprets natural language queries to deliver top-quality candidate results. Designed for easy integration with Applicant Tracking Systems and hiring platforms, it leverages scientific methods and is trusted by recruiters. The tool is implemented as a Model Context Protocol (MCP), facilitating standardized model context handling and deployment via packages like FastMCP or through Smithery. The solution prioritizes ease of use, reliability, and high candidate match accuracy.
5 4 MCP -
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SearXNG MCP Server
MCP-compliant server integrating the SearXNG API for advanced web search capabilities
SearXNG MCP Server implements the Model Context Protocol and integrates the SearXNG API to provide extensive web search functionalities. It features intelligent caching, advanced content extraction, and multiple configurable search parameters such as language, time range, and safe search levels. The server exposes tools for both web searching and URL content reading, supporting detailed output customization through input parameters. Designed for seamless MCP deployments, it supports Docker and NPX-based installation with rich configuration options.
321 56 MCP -
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mcp-paperswithcode
Discover and analyze research papers and code repositories through the PapersWithCode API.
mcp-paperswithcode provides a Model Context Protocol (MCP) interface for AI assistants to discover, search, and analyze research papers, related code repositories, datasets, authors, conferences, and research areas. The tool enables seamless integration with the PapersWithCode API, offering powerful methods for context-aware research exploration. It supports searching, retrieving metadata, extracting content, and listing relationships between academic resources, enhancing an AI model's ability to access and utilize scientific content.
14 7 MCP -
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GhidraMCP
AI-powered binary analysis through Model Context Protocol integration with Ghidra
GhidraMCP is a Ghidra plugin that implements the Model Context Protocol (MCP), enabling seamless connectivity between Ghidra and AI-powered assistants for advanced binary analysis. It allows users to interact with binaries using natural language, automate security and code analysis, and retrieve detailed program insights through a socket-based architecture. The plugin offers functions for exploring binary structures, analyzing memory layouts, and identifying vulnerabilities, providing a flexible and efficient reverse engineering workflow.
77 13 MCP -
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Content Core
AI-powered content extraction and processing platform with seamless model context integration.
Content Core is an AI-driven platform for extracting, formatting, transcribing, and summarizing content from a wide variety of sources including documents, media files, web pages, images, and archives. It offers intelligent auto-detection and engine selection to optimize processing, and provides integrations via CLI, Python library, Raycast extension, macOS Services, and the Model Context Protocol (MCP). The platform supports context-aware AI summaries and direct integration with Claude through MCP for enhanced user workflows. Users can access zero-install options and benefit from enhanced processing capabilities such as advanced PDF parsing, OCR, and smart summarization.
85 20 MCP -
24
OpenZIM MCP Server
Transforms ZIM archives into intelligent, structured knowledge engines for LLMs.
OpenZIM MCP Server provides structured, intelligent access to ZIM-format knowledge bases, enabling large language models to efficiently search, navigate, and understand content in offline archives. Dual operation modes allow support for both advanced and simple LLM integrations. It features smart navigation by namespace, context-aware discovery, intelligent search, and relationship mapping to optimize knowledge extraction and utilization.
8 2 MCP -
25
In Memoria
Persistent memory and instant context for AI coding assistants, integrated via MCP.
In Memoria is an MCP server that enables AI coding assistants such as Claude or Copilot to retain, recall, and provide context about codebases across sessions. It learns patterns, architecture, and conventions from user code, offering persistent intelligence that eliminates repetitive explanations and generic suggestions. Through the Model Context Protocol, it allows AI tools to perform semantic search, smart file routing, and track project-specific decisions efficiently.
94 17 MCP -
26
EntraID MCP Server (Microsoft Graph FastMCP)
A modular FastMCP server for managing Microsoft Graph resources with advanced context and security.
EntraID MCP Server provides a modular, resource-oriented implementation of a FastMCP server tailored for Microsoft Graph API interactions. It supports advanced operations across users, groups, applications, service principals, MFA, and sign-in logs, offering extensibility and strong security practices. Resource modules use a centralized authentication client, while comprehensive permission management, consistent context handling, error reporting, and logging are built in. The tool is designed for ease of development and extensibility, supporting standardized model context protocols.
29 9 MCP -
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MCP RSS Aggregator
Fetch and read RSS feeds in Claude Desktop via Model Context Protocol.
MCP RSS Aggregator enables Claude Desktop to access and display content from user-selected RSS feeds using the Model Context Protocol (MCP). It supports OPML and JSON formats for managing feed configurations, allowing users to import or customize their subscriptions easily. The tool organizes feeds by categories, filters content from different sources, and presents articles with well-formatted titles, snippets, and links. Integration with Claude Desktop allows seamless aggregation and delivery of real-time news content for users.
16 9 MCP -
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OSP Marketing Tools for LLMs
Comprehensive marketing content creation and optimization tools for LLMs using MCP.
OSP Marketing Tools for LLMs offers a suite of marketing content creation and optimization utilities designed to operate with Large Language Models that support the Model Context Protocol (MCP). Built on Open Strategy Partners’ proprietary methodologies, it provides structured workflows for product value mapping, metadata generation, content editing, technical writing, and SEO guidance. The suite includes features for persona development, value case documentation, semantic editing, and technical writing best practices, enabling consistent and high-quality marketing outputs. Designed to integrate seamlessly with MCP-compatible LLM clients, it streamlines complex marketing processes and empowers efficient collaboration across technical and non-technical teams.
252 40 MCP -
29
Agentset MCP
Open-source MCP server for Retrieval-Augmented Generation (RAG) document applications.
Agentset MCP provides a Model Context Protocol (MCP) server designed to power context-aware, document-based applications using Retrieval-Augmented Generation. It enables developers to rapidly integrate intelligent context retrieval into their workflows and supports integration with AI platforms such as Claude. The server is easily installable via major JavaScript package managers and supports environment configuration for namespaces, tenant IDs, and tool descriptions.
22 10 MCP -
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CRIC物业AI MCP Server
行业级物业AI智能体,基于MCP协议的多场景助手
CRIC物业AI MCP Server is a server-side implementation based on the Model Context Protocol (MCP), offering intelligent assistance specifically for the property management sector. It integrates multi-modal large models and RAG technology to provide industry research, legal insights, community governance, project operations, and copywriting support. The platform leverages extensive proprietary datasets and real-time data monitoring to deliver accurate, context-driven services for diverse property-related business scenarios.
1 3 MCP -
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Agent-MCP
Multi-Agent Collaboration Protocol for Coordinated AI Software Development
Agent-MCP enables advanced multi-agent orchestration for AI-driven software development, allowing specialized agents to collaborate via a shared persistent context. Its system manages parallel execution, persistent memory graphs, and real-time task coordination with robust visualization tools. Designed for experienced developers, it features intelligent task management and conflict prevention for scalable and complex codebases. The protocol emphasizes maintaining and sharing context among agents through standardized mechanisms.
1,021 124 MCP -
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mindsdb
Connect, unify, and query data at scale with an open-source AI platform.
MindsDB enables seamless connection to and unification of data from hundreds of enterprise sources, allowing for highly accurate responses across large-scale federated systems. It provides an open-source server with built-in support for the Model Context Protocol (MCP) to facilitate standardized interaction with AI-driven question answering over diverse data sets. The platform offers tools for preparing, organizing, and transforming both structured and unstructured data via knowledge bases, views, and scheduled jobs. Its agent framework and SQL interface empower users to configure data-centric agents, automate workflows, and interact with data conversationally.
35,487 5,735 MCP -
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Octocode MCP
Enterprise-grade AI context server for codebase research and analysis.
Octocode MCP is a Model Context Protocol (MCP) server designed to enable AI assistants to search, analyze, and extract insights from millions of GitHub repositories with high security and token efficiency. It offers intelligent orchestration for deep code research, planning, and agentic workflows, streamlining the process of building and understanding complex software projects. The platform features robust tools and commands, such as /research for expert code research, designed to support developers and AI systems with context-rich information.
577 45 MCP -
34
RivalSearchMCP
Advanced MCP server for web research, discovery, and trend analysis.
RivalSearchMCP is an advanced Model Context Protocol (MCP) server designed to streamline web research, content discovery, and trend analysis. It offers tools for multi-engine web search, intelligent content retrieval, website analysis, and AI-driven content insights. The platform includes integrated trends analysis, research workflows with progress tracking, and automated generation of LLMs.txt documentation files. Its anti-detection features, real-time content streaming, and flexible data export options make it ideal for complex research and automation workflows.
10 7 MCP -
35
Wren Engine
Semantic Engine for MCP Clients and AI Agents
Wren Engine is a semantic engine designed for Model Context Protocol (MCP) clients and AI agents. It enables precise access to enterprise data by providing semantic understanding, trusted aggregations, and business term clarity across numerous data sources. With a focus on accurate context, calculation, and governance, it equips AI systems to interact safely and meaningfully with complex enterprise data. As part of the MCP ecosystem, it connects LLMs with databases and enterprise tools through standardized protocols.
496 138 MCP -
36
pluggedin-mcp-proxy
Unified proxy server for Model Context Protocol data exchanges and AI integrations
Aggregates multiple Model Context Protocol (MCP) servers into a single, unified proxy interface, supporting real-time discovery, management, and orchestration of AI model resources, tools, and prompts. Enables seamless interaction between MCP clients such as Claude, Cline, and Cursor, while integrating advanced document search, AI document exchange, and workspace management. Provides flexible transport modes (STDIO and Streamable HTTP), robust authentication, and comprehensive security measures for safe and scalable AI data exchange.
87 15 MCP -
37
1mcp-app/agent
A unified server that aggregates and manages multiple Model Context Protocol servers.
1MCP Agent provides a single, unified interface that aggregates multiple Model Context Protocol (MCP) servers, enabling seamless integration and management of external tools for AI assistants. It acts as a proxy, managing server configuration, authentication, health monitoring, and dynamic server control with features like asynchronous loading, tag-based filtering, and advanced security options. Compatible with popular AI development environments, it simplifies setup by reducing redundant server instances and resource usage. Users can configure, monitor, and scale model tool integrations across various AI clients through easy CLI commands or Docker deployment.
96 14 MCP -
38
Mastra
A TypeScript framework for building scalable AI-powered agents and applications.
Mastra is a modern TypeScript-based framework designed for developing AI-powered applications and autonomous agents. It offers model routing to integrate over 40 AI providers, a graph-based workflow engine, advanced context management, and production-ready tools for observability and evaluation. Mastra features built-in support for authoring Model Context Protocol (MCP) servers, enabling standardized exposure of agents, tools, and structured AI resources via the MCP interface.
18,189 1,276 MCP -
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magg
Meta-MCP aggregator and manager for LLM capability extension.
Magg is a server that implements the Model Context Protocol (MCP), acting as a central aggregator and proxy for multiple MCP servers. It enables Large Language Models (LLMs) to dynamically discover, add, configure, and manage external tools at runtime. By aggregating tools from different MCP servers under unified namespaces, it streamlines capability management and introduces features such as configuration persistence, authentication, and real-time notifications. Magg offers both command-line and Docker deployment, with support for HTTP, stdio, and in-memory transport.
62 14 MCP -
40
Neo4j MCP Clients & Servers
Seamless natural language and knowledge graph integration for Neo4j via Model Context Protocol.
Neo4j MCP Clients & Servers provide standardized interfaces that enable large language models and AI assistants to interact with Neo4j databases and cloud services using natural language through the Model Context Protocol (MCP). It includes multiple servers for translating natural language to Cypher queries, managing graph memory, handling Neo4j Aura cloud services, and supporting interactive data modeling. Multiple transport modes such as STDIO, HTTP, and SSE offer flexibility for various deployments including cloud and local environments.
797 209 MCP -
41
FHIR MCP Server
A Model Context Protocol server for seamless interaction with FHIR resources and AI tools.
FHIR MCP Server implements a full Model Context Protocol server, enabling large language model agents to perform comprehensive CRUD operations on FHIR-compliant healthcare data. It offers standardized integration with various clinical data sources, natural-language query capabilities, and supports secure authentication via OAuth2. The server includes semantic search, AI-powered document processing, terminology resolution, Docker deployment, and is optimized for use with MCP-compatible clients like Claude Desktop.
34 5 MCP -
42
Maven Tools MCP Server
Universal Maven Central dependency intelligence server for JVM build tools via the Model Context Protocol.
Maven Tools MCP Server provides an MCP-compliant API delivering rich Maven Central dependency intelligence for JVM build tools like Maven, Gradle, SBT, and Mill. It enables AI assistants to instantly analyze, interpret, and recommend updates, health checks, and maintenance insights by reading maven-metadata.xml directly from Maven Central. With Context7 integration, it supports orchestration and documentation, enabling bulk analysis, stable version filtering, risk assessment, and rapid cached responses. Designed for seamless integration into AI workflows via the Model Context Protocol.
14 2 MCP -
43
Alibaba Cloud Tablestore MCP Servers
Reference MCP server implementations for Alibaba Cloud Tablestore in Java and Python
Alibaba Cloud Tablestore MCP Servers provides multiple implementations (Java and Python) of MCP (Model Context Protocol) server-side components. It includes sample projects and a production-ready RAG-based knowledge Q&A system, enabling management of context and knowledge for AI applications on Tablestore. The libraries showcase standardized approaches for integrating Tablestore with modern AI use cases requiring context handling.
148 38 MCP -
44
Multi Database MCP Server
A unified server for structured, multi-database access via the Model Context Protocol.
Multi Database MCP Server provides a standardized interface for AI assistants to access and manage multiple databases concurrently through the Model Context Protocol. It supports automatic tool generation for SQL queries, transactions, schema exploration, and performance analysis for each connected database. Built using Clean Architecture, it is fully compatible with OpenAI Agents SDK, enabling seamless integration. The platform simplifies configuration and interaction with MySQL and PostgreSQL databases in a robust, modular environment.
304 49 MCP -
45
Aiven MCP Server
Model Context Protocol server enabling LLMs to access and manage Aiven cloud data services.
Aiven MCP Server implements the Model Context Protocol (MCP) to provide secure access to Aiven's PostgreSQL, Kafka, ClickHouse, Valkey, and OpenSearch services. It enables Large Language Models (LLMs) to seamlessly integrate and interact with these cloud data platforms, supporting full stack solution development. The server offers streamlined tools for project and service management via standardized APIs and supports integration with platforms like Claude Desktop and Cursor. Environment variable configuration and explicit permission controls are used to ensure secure and flexible operations.
11 11 MCP -
46
AgentMail Toolkit
Integrates agent frameworks and protocols with AgentMail API for seamless interoperability.
AgentMail Toolkit provides seamless integration of popular agent frameworks and protocols, including the Model Context Protocol (MCP), OpenAI Agents SDK, and Vercel AI SDK, with the AgentMail API. It supports both Python and Node environments, enabling developers to build and manage agent workflows across different platforms and standards.
32 7 MCP -
47
Multi-Database MCP Server (by Legion AI)
Unified multi-database access and AI interaction server with MCP integration.
Multi-Database MCP Server enables seamless access and querying of diverse databases via a unified API, with native support for the Model Context Protocol (MCP). It supports popular databases such as PostgreSQL, MySQL, SQL Server, and more, and is built for integration with AI assistants and agents. Leveraging the MCP Python SDK, it exposes databases as resources, tools, and prompts for intelligent, context-aware interactions, while delivering zero-configuration schema discovery and secure credential management.
76 19 MCP -
48
MCP Link
Convert Any OpenAPI V3 API to an MCP Server for seamless AI Agent integration.
MCP Link enables automatic conversion of any OpenAPI v3-compliant RESTful API into a Model Context Protocol (MCP) server, allowing instant compatibility with AI-driven agent frameworks. It eliminates the need for manual interface creation and code modification by translating OpenAPI schemas into MCP endpoints. MCP Link supports robust feature mapping and authentication, making it easy to expose existing APIs to AI ecosystems using a standardized protocol. The tool is designed for both developers and organizations seeking to streamline API integration with AI agents.
572 68 MCP -
49
Snowflake Cortex AI Model Context Protocol (MCP) Server
Tooling and orchestration for Snowflake Cortex AI via Model Context Protocol.
Provides an MCP server that brings Snowflake Cortex AI, object management, and SQL orchestration to the MCP ecosystem. Enables Cortex Search, Analyst, and Agent services for structured and unstructured data querying, along with automated SQL execution and object management. Designed for integration with MCP clients to streamline AI-powered data workflows and context-sensitive operations within Snowflake environments.
176 60 MCP -
50
Pica MCP Server
A Model Context Protocol (MCP) server for seamless integration with 100+ platforms via Pica.
Pica MCP Server provides a standardized Model Context Protocol (MCP) interface for interaction with a wide range of third-party services through Pica. It enables direct platform integrations, action execution, and intelligent intent detection while prioritizing secure environment variable management. The server also offers features such as code generation, form and data handling, and robust documentation for platform actions. It supports multiple deployment methods, including standalone, Docker, Vercel, and integration with tools like Claude Desktop and Cursor.
8 5 MCP
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