"Help with Writing Articles" matching MCP servers:

Enables AI agents to measure Chinese, Japanese and Korean page titles by rendered pixel width and judge whether they fit Google's search result column, flagging titles that would be truncated or cut near the edge. It also reads tags from local HTML files or directories and applies the same width, boilerplate and keyword-zone checks.

Enables independent, read-only adversarial review of candidate test cases and bug findings, returning a review delta that authoring agents reconcile before writing final artifacts.

Finds and removes AI writing tells with a deterministic ruleset. Runs locally by default; an optional pass can call a model if you turn it on.

Connects Claude to a live Chrome browser's accessibility tree to generate stable, framework-agnostic test selectors and perform accessibility audits. It enables users to read element roles and states exactly as a screen reader would to help write, debug, and migrate test suites using natural language.

Enables deterministic, LLM-free checking of text against a prose editorial standard, returning violations with rule IDs, locations, severity, confidence, and suggested fixes, while also supporting safe fixes and writing measurements.

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.

Provides content guard checks as MCP tools, enabling an assistant to verify keyword coverage, translation drift, internal links, regulated claims, and sitemap lastmod while writing pages.

MCP server that scores tool descriptions, estimates token costs, simulates agent tool selection, and generates reliability reports to help AI agents choose the right tools and reduce wasted tokens.

Delegates test writing to a headless second coding agent in an isolated git worktree, then builds, runs the tests, verifies that only test paths changed, and returns a compact report plus optionally applies the verified tests to your checkout. It also flags failures that point to bugs in the production code rather than the tests, keeping test code and terminal logs out of the main agent's context.

A lightweight, configurable server that fetches coding guidelines, security rules, and validation patterns from external sources to help development teams maintain code quality standards in WordPress projects.

MCP server for Android device control via ADB and scrcpy, enabling AI agents to see and interact with Android devices.

Enables an assistant to verify whether claims about AI systems hold against supplied evidence bytes, replay conformance vectors, and inspect related terms and artifact digests offline without fetching, writing, or sending data.

A test MCP server that exercises all protocol features including tools, resources, prompts, and sampling to help client builders validate implementations.

An MCP server for checking OWASP Top 10 vulnerabilities during API development testing. It analyzes API information and returns security assessment results to help developers identify potential security issues.

Integrates with Zebrunner Test Case Management to help QA teams manage test cases, test suites, and test execution data through AI assistants. Features intelligent validation, automated test code generation, and comprehensive reporting capabilities.

An MCP protocol test server with 35 test tools across 3 transport modes for compatibility verification, plus a development Skill for the Stepfun AI desktop assistant to help write MCP servers.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

Indexes Robot Framework and Python files from robotws, stores metadata in SQLite, and provides generation context to help Cursor's LLM draft testcases in team style.

MCP server that provides 8 tools for Java memory leak investigation: \- Class histograms, GC pressure snapshots, JFR recordings, heap dumps, MAT leak suspects analysis, async-profiler allocation profiles \- Structured confidence-based verdicts (none/low/medium/high) requiring independent signal corroboration \- Designed for use inside Claude Code with two slash commands

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

Lets an AI agent add websites, set up and tune uptime, user-flow, security, speed, SEO, domain and compliance monitors, run scans, and review results. It also explains what broke in a failed run and proposes fixes the agent can apply with the user.

Enables safe evaluation of trading-oriented MCP tools through a no-execution stdio mock with read-only signal listings, deterministic paper previews, and confirmation requests that always fail closed.

Provides a local MCP inspection workspace for testing permission-gated note reading and writing, showing rules, request payloads, execution status, and before/after SHA-256 hashes so developers can verify whether denied writes actually left files unchanged.

Enables LLMs to automate browsers and interact with web pages using structured accessibility snapshots rather than screenshots or vision models.

Verification browser for coding agents. Headless-by-default WebKit windows with DOM eval, screenshots, pixel-diff with a real match percentage and heatmap, and live hand-off to a human. One static binary, no Chromium.

A TypeScript MCP server delivering a vendored 'dangerous skills' corpus and adversarial fixtures over MCP for security-research testing, supporting SEP-2640 skill delivery with archive-safety hardening.

Enables interaction with Kiwi TCMS test management system through MCP, supporting read and write operations on test plans, cases, runs, and executions, with API key authentication.

Enables Windows UI Automation through MCP, allowing agents to inspect, search, navigate, and interact with desktop UI elements via the UIAutomation framework. Supports actions such as focus, invoke, set-value, toggle, and event waiting, with CLI and VS Code extension variants.

Enables AI agents to compare database tables using read-only tools, with credentials managed locally and never exposed to the model.

Exposes data validation as tools so an AI agent can check whether data is actually correct before using it, scoring CSV files with per-row problem details, catching impossible dates, inferring schemas, and generating readable reports.