"Hybrid Memory Models Combining Relational, Graph, and RAG Approaches" matching MCP servers:

An MCP server that wraps the @apitomy/data-models library for querying, validating, and editing OpenAPI and AsyncAPI documents.

This MCP server enables intelligent API testing automation by combining RAG knowledge retrieval with tool execution capabilities. It allows QA engineers to perform natural language-driven API testing with contextual knowledge support.

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

An MCP server that provides tools for exploring large OpenAPI schemas without loading entire schemas into LLM context. Perfect for discovering and analyzing endpoints, data models, and API structure efficiently.

An MCP server that connects to a Swagger specification and helps an AI to build all the required models to generate a MCP server for that service.

Enables browser automation and API testing for AI agents using Playwright and Chrome DevTools Protocol, with numbered element references, Set-of-Mark screenshots, flow recording/replay, element memory, captcha handoff, and HTTP API test suites.

An MCP server that allows users to query and retrieve request and response JSON schemas directly from Swagger/OpenAPI specifications. It supports automatic reference resolution and path parameter matching to help AI models interact with API interfaces.

A standalone Model Context Protocol server that enables AI models to access the Australian Pharmaceutical Benefits Scheme (PBS) API data, including medicine listings, pricing, and availability information.

A Model Context Protocol server for executing GraphQL queries, allowing AI models to interact with GraphQL APIs through introspection and query execution.

An MCP server providing read-only access to Linear issues for language models, allowing them to fetch issue details and comments using a Linear API token.

Exposes add, subtract, multiply, and divide tools for performing arithmetic operations over stdio via the Model Context Protocol.

Exposes FastAPI routes as MCP tools with auto-discovery, Pydantic schema extraction, and built-in observability.

MCP server for loading and exploring OpenAPI/Swagger specifications, enabling AI assistants to dynamically browse API contracts by loading specs, searching endpoints, inspecting schemas, and retrieving operations.

A TypeScript-based MCP server that provides backend API handling and facilitates communication between microservices. Features an organized structure with controllers, routes, and models for easy extensibility and maintenance.

A bridge that exposes any GraphQL API as tools consumable by Large Language Models via the Model Context Protocol (MCP), automatically discovering and translating API capabilities with zero maintenance required.

A server that enables Large Language Models to discover and interact with REST APIs defined by OpenAPI specifications through the Model Context Protocol.

Enables AI to interact with multiple Swagger/OpenAPI specifications by listing endpoints, exploring models, and generating TypeScript code. It centralizes the management of various APIs through a configuration file, allowing for seamless integration of documentation into AI workflows.