"Exploring Memory Persistence in DeepSeek Technology" matching MCP servers:

Goodday‑MCP is a lightweight Model Context Protocol (MCP) server designed to seamlessly integrate with the Goodday project management platform via its API v2. It enables querying of projects, tasks, and users—without altering any data—making it ideal for secure context-aware applications

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.

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.

MCP server for reading, writing, and organizing API documentation in Apidog.

A generic MCP server that dynamically exposes any OpenAPI-documented REST API to LLMs by auto-discovering endpoints. It provides tools for exploring API capabilities and making authenticated requests directly through natural language interfaces.

Runs, tests and edits the saved HTTP requests in a Kurlo API workspace (Git-backed YAML): list collections and environments, send requests with their auth and pm.* tests, write new requests and tests, and diagnose the last failure. Secret values are sent to the API but masked in every tool result, and protected environments are refused.

A small native HTTP client whose MCP server (plunger mcp) lets an AI agent send requests safely: undefined variables are refused, secrets are filled in by name and masked in results, and every request is recorded in a history you can see in the window. MIT licensed, for Windows, with a Linux build of the CLI and MCP server.

Point Claude at any API. JitAPI figures out which endpoints to call and in what order — automatically. Register any OpenAPI spec, search endpoints in plain English, and orchestrate multi-step workflows across multiple APIs. No API keys required — works out of the box with local embeddings.

Enables LLMs to dereference and query OpenAPI/Swagger specifications, search endpoints and schemas, validate payloads, extract security schemes, and generate production-ready integration code in multiple languages.

Enables AI agents to create, run, and save API requests and collections as local Gud API files, which are git-committable and viewable in VS Code-compatible editors.

Enables AI coding assistants to discover, inspect, and run API requests in Bruno collections locally, keeping all data private.

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.

MCP server that provides tools for exploring and testing APIs through Swagger/OpenAPI documentation.

Enables direct integration with YApi API documentation platform in MCP-compatible editors like Cursor, allowing users to query API details, list interfaces, and search endpoints by simply pasting YApi URLs.

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.

Enables autonomous agents and CI pipelines to execute declarative multi-step REST API workflows in a single tool call, with in-memory variable chaining, assertions, and built-in SSRF protection.

A Model Context Protocol server that creates tools from API configurations defined in YAML files, allowing easy integration of external APIs into an MCP ecosystem without coding.

Enables AI tools to create and manage API requests and collections in Thunder Client, with automated collection and folder creation.

Enables validation of OpenAPI 2.0 and 3.0 specifications in JSON or YAML format using APIMatic's comprehensive API validation service through the Model Context Protocol.

A Model Context Protocol server that provides tools for fetching and posting HTTP data, with built-in prompts for URL summarization and API analysis.

A Model Context Protocol server that loads multiple OpenAPI specifications and exposes them to LLM-powered IDE integrations, enabling AI to understand and work with your APIs directly in development tools like Cursor.

Generates API documentation in Confluence from Swagger/OpenAPI specs or NestJS controller files, including automatic Postman collection generation.

Enables AI agents to create, manage, and execute API collections, requests, and environments in Insomnia-compatible formats. It supports direct synchronization with the local Insomnia app database and importing from OpenAPI, Postman, and cURL.

An MCP (Model-Controller-Processor) server for accessing League of Legends client data. This server provides a collection of tools that communicate with the League of Legends Live Client Data API to retrieve in-game data.

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 microservice control plane server that integrates with Windsurf IDE by fetching API information from Eolink OpenAPI and exposing it as MCP resources, enabling seamless API integration and management in the development environment.

A server based on Model Context Protocol that provides predefined prompt templates for tasks like code review and API documentation generation, enabling more efficient workflows in Cursor/Windsurf editors.

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

Generates MCP servers from Postman API requests, automatically creating JavaScript tools that enable LLMs to interact with any API documented in Postman collections. Supports authentication configuration and deployment via STDIO or SSE.

Enables an agent to send arbitrary HTTPS API requests (GET, HEAD, POST, PUT, PATCH, DELETE, uploads) to saved services using stored auth keys, with request previews, idempotent write recording, and built-in Cookiejar site creation and deployment. Requests are pinned to the saved origin and auth header, with the service itself deciding what each request may do.