"Search for Dify" matching MCP servers:

Exposes dify-eval's evaluation capabilities as an MCP server, enabling AI agents in MCP clients to run regression evaluations, compare runs, and manage human review workflows via natural language.

Enables validation of Dify Workflow DSL YAML files through MCP protocol. Allows users to check the validity and structure of Dify workflow definition files.

Enables agents to discover, search, compare, and inspect tested MCP tools by problem or keyword, with certification verdicts, scorecard summaries, certified scope, and explicit not-tested details.

MCP server for managing test cases, cycles, and results in Zephyr Scale on self-hosted Jira. Enables AI agents to create, search, update, and delete test artifacts through natural language.

Translates a lockfile diff into a human-readable upgrade plan. For every dependency bump (npm or PyPI) returns semver class, breaking changes from GitHub release notes, CVEs fixed in the range, migration guide links, and a clear per-package recommendation. Bulk tool ranks up to 50 package changes in parallel by risk (security > caution > review > likely-safe > safe).

A reference MCP server that enforces identity-aware tool access using role-based permissions (Reader, Analyst, Auditor) and supports multiple identity routes like Entra RBAC, Copilot Studio, and Okta XAA. It provides read-only access to test evidence with explicit denial paths for unauthorized tools.

Enables auditing any public website, returning a scored plain-English report that flags issues costing customers, covering speed, phone experience, search visibility, contact options, writing quality, and modernity.

Enables AI agents to inspect Shadow Monitor capture files locally, with tools to load bundles, find errors, search network requests, and replay user actions.

Real-time Core Web Vitals (LCP) telemetry, DOM bloat analysis, and AI search crawler (GEO) readiness audits for AI coding assistants.

An MCP server that enables AI agents to drive Zephyr for Jira (Server/Data Center) test management via ZAPI. It exposes operations for test cycles, folders, executions, test steps, step results, and ZQL search over stdio for use with any MCP client.

Enables AI assistants to query the Google Play Developer Reporting API for Android production stability data, including crash and ANR issue clusters, de-obfuscated stack traces, release track discovery, startup and crash metric trends, version-to-version comparisons, and anomaly alerts. It also provides guided prompts for incident triage, baseline profile verification, and weekly stability reporting with built-in token compression and caching.

Enables users to browse, retrieve, and search practical guide topics and ready-made snippets for configuring Delphi projects, DUnitX runners, CodeCoverage CLI invocations, and CI pipelines so coverage works correctly.

Checks the outcome after an agent handoff, when a request, 200 or ack only shows the handoff. Reads one field of one exact public HTTPS JSON record, with a same-site known-good control, for 20-120 s. Returns PROVED, DISPROVED or INCONCLUSIVE with a signed receipt you can verify. 5 free checks, then USD 1 per completed check, by card (Stripe Checkout) or existing Link wallet. INCONCLUSIVE is free.

Enables to interact with Jira AIO Test Case Management System, allowing retrieval of test cases, projects, folders, and search functionality.

Zero-sidecar MCP server embedded directly in the Godot 4 editor as a GDScript EditorPlugin — no Node or Python sidecar. Reflection-driven tools over any engine class, validate-before-write GDScript, scene/script/resource authoring, and a runtime play-test loop (play, screenshot, input, assert).

Enables AI assistants to interact with Inflectra Spira for project, test, and requirements management, including artifact search, CRUD operations, and workspace configuration.

Enables AI assistants to control Cadence Xcelium and SimVision simulators in real time for automated RTL and gate-level debugging. It provides 25 tools for signal inspection, watchpoints, binary search, and simulation state management.

Enables checking npm packages for known security vulnerabilities using the OSV API before installation. Supports both single package checks and bulk vulnerability scanning for multiple packages at once.

Enables comparison of mobile app screenshots against design mockups, with Calorix as the first integration target.

Enables automated access to LoadRunner Cloud APIs for retrieving performance test data, managing projects, and collecting test results. Supports integration with AI clients for building performance engineering workflows and dashboards.

An MCP server that automatically generates TypeScript Page Object classes and comprehensive Cypress test suites for any web page, supporting element interactions, workflow tests, and both positive and negative testing scenarios.

Enterprise-grade code intelligence platform providing AI-powered code analysis, semantic search, security scanning, and automated refactoring capabilities. Integrates with local AI models for zero-cost operations while delivering comprehensive development workflow automation.

A simple server implementing the Model Context Protocol (MCP) that echoes messages back, designed for testing MCP clients.

A testing server that demonstrates Model Context Protocol features, providing access to user data, todos, and system information through resources, tools for user management and calculations, and prompt templates for various analyses.

Mobile automation suite of tools including an MCP and libraries for test authoring & execution. Android first, iOS support coming.

MCP server for operating and testing Android devices with AI agents. Provides observation and action tools for interactive control and spreadsheet-driven test runs.

A lightweight Python-based microservice that provides simple echo functionality, receiving messages and returning them back to the client for diagnostic and testing purposes within the MCP framework.

Provides AI assistants with tools for Arm architecture development, migration, and optimization, including knowledge base search, code migration analysis, container inspection, assembly performance analysis, and workload performance testing.

Retrieves captured bug context and evidence for coding agents, enabling progressive disclosure of sessions, manifests, and window correlations to diagnose and fix issues.

Enables MCP-compatible AI coding agents to obtain clean failing-test context, ranked suspect lines, runtime value comparisons, bounded code search, and patch verification so they can fix Python test failures with fewer tokens.