"Understanding Inference Models" matching MCP servers:

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.

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

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.

MCP server that fetches API responses and generates ready-to-use model/POJO classes in your configured language, with built-in login/refresh token handling.

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.

Enables MCP clients to check Standard Compute endpoint health, list accessible models, fetch live plan pricing, and verify API keys with a small completion probe.

A proxy server that bridges AI agents and external APIs by dynamically translating OpenAPI specifications into standardized MCP tools, enabling seamless interaction without custom integration code.

An MCP server that crawls API documentation websites and exposes their content to AI models, enabling them to search, browse, and reference API specifications.

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

Exposes REST API endpoints defined in an OpenAPI Specification as MCP tools, allowing AI models to call them via the ModelContext Protocol.

A Bun-based MCP server that allows AI models to query Swagger/OpenAPI documentation from local files or remote URLs. It enables users to search for APIs, retrieve detailed endpoint definitions, and fetch schemas to facilitate code generation and API integration.

MCP server that exposes GraphQL operations as typed tools for AI models, with schema exploration, validation, execution, hot reload, OAuth, prompts, MCP Apps widgets, and OpenTelemetry support.