"Information about RDKit (chemistry software toolkit)" matching MCP servers:

Checks a repository's own guardrails and fails any that are present but have stopped refusing anything. Three read-only tools: installation status, per-step verification that issues a receipt, and a tail of the append-only ledger recording every question and answer.

Enables AI agents to perform WCAG 2.2 accessibility audits, including contrast ratio checks for color pairs and text-over-image, generate EU legal accessibility declarations, and produce aria-live monitoring snippets, all through local, multilingual tools.

9 MCP servers for React + TypeScript development automation — component scaffolding, dependency auditing, WCAG accessibility checking, test generation, TypeScript enforcement, and monorepo management. 134 tests, CI on Node 20+22.

Enables AI agents to inspect Unity UI Toolkit screens, examine element structure and styles, and capture screenshots without entering Play Mode or building a Player.

Enables AI agents to create webhook URLs, wait for incoming deliveries, inspect payloads, and replay or send signed webhook events to a local handler.

An MCP server that reads test reports and provides regression analysis tools for comparing runs, identifying regressions, fixes, and persistent failures.

Provides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.

Comprehensive MCP server for end-to-end UI development, offering tools to generate components, manage design tokens, audit accessibility, autofix issues, inspect live pages, compare screenshots, and more across multiple frameworks.

Enables AI assistants to automate Godot 4 projects via headless tooling, live editor control, and runtime remote control, including scene building, testing, and observation.

Provides utilities to help maintain MCP repositories, including testing tools, parameter type support, and multiple transports (STDIO, SSE, Streamable HTTP).

Enables regex testing, replacement, splitting, validation, explanation, escaping, common patterns, and benchmarking through an MCP server.

Enables reproducible evaluation of AI coding agents by exposing repository inspection, code editing, test running, and deterministic verification through MCP tools.

Enables AI assistants to interact with iOS Simulators and Android Emulators, allowing autonomous app development, UI interaction, profiling, and debugging through natural language.

Gives your AI the super power of traveling back in time to find what went wrong and fix the bug timeline.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.

Enables AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

Enables coding agents to drive and inspect running Shiny apps (R and Python) by setting inputs, clicking, reading outputs, and getting detailed error information.