"Implementing a Local Inpainting Function with Mask Generation Using Canvas" matching MCP servers:

Middleware that converts a serverless handler with OpenAPI spec into an MCP endpoint following stateless MCP principles.

Enables programmatic interaction with the Canvas LMS API through the Model Context Protocol. It currently supports listing courses with enrollment type filtering and provides interactive help prompts for tool usage.

An MCP server that generates AI agent tools from Postman collections and requests. This server integrates with the Postman API to convert API endpoints into type-safe code that can be used with various AI frameworks.

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 Claude to interact with the Canvas LMS API to manage courses, assignments, grades, and communications. It supports both remote and local deployments, allowing users to query academic data and submit assignments via natural language.

Provides a testable gateway path for running a local MCP-style server in hosted-like conditions, with tenant-isolated reads, approval-denied writes, deterministic failures, and smoke-test verification.

Provides AI assistants with access to OpenAPI specifications, enabling API discovery, schema retrieval, and direct API execution with support for OAuth 2.0 and other authentication methods.

An open standard server implementation that enables AI assistants to directly access APIs and services through Model Context Protocol, built using Cloudflare Workers for scalability.

Enables natural language interaction with FastAPI applications through Google's Gemini AI using MCP tools. Provides CRUD operations for user management and application health monitoring through conversational prompts.

Enables Claude to create local, persistent mock REST APIs on demand from plain-language descriptions, with data written to disk so it survives restarts.

Enables LLMs to make HTTP requests with OAuth2, session cookies, retry logic, and cURL command generation, while providing security features like SSRF protection and TLS enforcement.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Enables LLMs to make HTTP requests using structured cURL commands with support for multiple authentication methods, custom headers, and comprehensive request/response control.

Parses Swagger 2.0 and OpenAPI 3.x specifications, exposing API endpoints, schemas, and authentication through MCP tools with local caching to reduce token usage.

Enables LLMs to send HTTP requests to a local development server, allowing interaction with APIs running on localhost.

Enables AI assistants to fetch and understand API endpoint definitions from Apifox projects in real-time. Supports retrieving complete API specifications including request methods, parameters, headers, and response schemas to improve development efficiency and code generation quality.

An MCP server for Tern that enables the generation, verification, and management of webhook handlers across over 16 platforms and various frameworks. It provides tools to create verified endpoints, debug signature failures, and manage failed event replays via dead letter queues.

Self-healing API proxy MCP that sends REST requests, diagnoses failures using Claude, patches requests, and retries. Successful fixes are remembered for instant reuse.

A TypeScript-based MCP server that enables AI assistants to execute HTTP requests with full control over methods, headers, and body content. It facilitates seamless interaction with web APIs and endpoints through a versatile tool that handles automatic JSON formatting and response truncation.

An MCP server that gives AI coding agents real-time access to the public-apis dataset, enabling search, live probing, and code snippet generation for 1,500+ free public APIs.