"Enabling deep research modes in AI tools like Kimi and ChatGPT" matching MCP servers:

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

Enables ChatGPT to control Mobile Safari on a real USB-connected iPhone through an OpenAI Secure MCP Tunnel and Appium MCP, including device selection, WDA preparation, and Safari session automation.

Enables capturing web evidence, freezing content-addressed snapshots, and comparing research agents on the same sources with traceable access and quotations.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

Enables generating JSON Schema from sample data, generating TypeScript types, validating data against schemas, creating mock data from schemas, and comparing schemas for differences.

Webhook management and testing tools for AI agents. Provides tools for sending, validating, generating, and debugging webhooks.

Enables AI agents to safely read, search, and inspect a local git repository, including PR-style diffs and changed-file summaries. It also supports opt-in allowlisted test runs.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables validating JSON/JSONC, getting/setting values with jq-like paths, pretty-printing or minifying, validating against Ajv JSON Schema, and diffing local files or inline strings without network calls.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Enables agents to work with Robot Framework by discovering tests and resources, drafting tests from requirements, dry-running and executing suites, and parsing output.xml results.

Enables AI agents to run configured test suites, inspect failures and logs, compare runs, and draft or file bug reports only after human confirmation, with safeguards such as selector validation, environment isolation, timeouts, and redaction.

MCP server providing code review, auto-fix, test running, linting, formatting, and refactor suggestions for Claude Code.

Enables AI agents to interact with Ethereum smart contracts through Anvil, providing capabilities to read Solidity code, simulate and execute transactions, manipulate blockchain state, query events, and test contracts in isolated environments for development and auditing workflows.

Provides 32 remote tools that let agents validate common data formats, optimize or minify code and content, and generate synthetic test data such as fake records, IDs, and passwords.

Enables deliverable conformance and evidence-chain checks by turning requirements into executable specs, validating numbers against a provenance ledger, and auditing PDF/OOXML metadata and archive manifests.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Code linting and style checking tools for AI agents, exposed as an MCP server. Supports style checks, naming conventions, complexity analysis, dead code detection, and import analysis.

Spring AI Playground is a self-hosted web UI platform for building low-code tools and dynamically exposing them via built-in MCP server for AI agents.

Exposes read-only and write probes over a remote Streamable-HTTP /mcp endpoint so users can verify that ChatGPT Web discovers and calls remote tools, and test whether write actions actually execute on their plan. Real HWPX document editing is not yet supported; this P0 release only measures connectivity and read/write action boundaries.

Bridges AI agents with VS Code's debugger, enabling breakpoint management, step execution, variable inspection, and stack tracing via CLI commands.

Enables deterministic auditing of multi-hop agent trajectories through cyclic loop detection, diversity scoring, and execution drift prevention via the Model Context Protocol.

Provides AI agents with web accessibility analysis tools via MCP, enabling checks for alt text, heading hierarchy, color contrast, ARIA validation, and form accessibility.

Enables AI agents and test runners to control Android devices via local ADB, including listing devices, tapping, swiping, typing, sending system keys, launching apps, and dumping UI hierarchy. Communicates over MCP stdio without exposing network listeners.

Zero-Config, Fully AI-Managed End-to-End Testing for all code gen platforms.

Reads a small-business website like a customer would and shows where they get stuck. Every AI answer has to quote the page, and the server checks the quote before it counts.

Validates and tests llguidance grammars (Lark format), providing grammar validation, batch test execution, and syntax documentation.

Checks a site for AI crawlers: which of 11 AI crawlers its robots.txt lets in, whether its llms.txt follows llmstxt.org, and what its home page says without scripts. Findings with evidence; no scores, no model calls, no telemetry.