"A guide to CI/CD (Continuous Integration and Continuous Deployment)" matching MCP servers:

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

MCP server for NodeBB forums that enables AI assistants to access forum data, search topics, and perform semantic search via the Model Context Protocol.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

he Maximo MCP Server bridges AI agents (Claude, Cursor, Antigravity) with your IBM Maximo environment. It empowers your AI to autonomously discover Object Structures (APIs), inspect schemas, and query live data via OSLC. Instead of hallucinating field names, the AI validates queries instantly and ca

A guide for implementing Model Context Protocol (MCP) servers that provide AI models with external tools like web search, text manipulation, and mathematical operations.

A semantic-memory MCP server that stores text 'memories' with provenance and enables recall by meaning (vector search), keyword (FTS5), or structured filters.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.

Routes requests to multiple downstream Model Context Protocol servers and provides a LangGraph.js-based agent with RAG capabilities for development assistance.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.