"Information about the VIX (Volatility Index)" matching MCP servers:

Provides a stable, side-effect-free MCP tool contract that enables AI workflows to be prototyped, demoed, tested, or dry-run without connecting to databases, APIs, files, or other real data sources. It enables swapping in a real data server later without changing the AI-facing interface.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enables MCP clients and agents to query synthetic football match state, key moments, win probability, and player profiles, and to verify claims and on-screen overlay numbers against match state.

Enables coding agents to audit public or localhost pages, gather structured SEO evidence, and retrieve compact reports, finding details, and next actions for triage.

Enables AI agents to safely run test suites over MCP, returning concise JUnit-based results, trimmed tracebacks, rerun-failed support, and flaky-test detection while handling timeouts, process cleanup, and bounded output.

MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.

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

Enables MCP clients to review Ceramic and ordinary websites with desktop/mobile browser QA, deterministic design and accessibility checks, optional visual judgement, and repeatable fix verification backed by preserved screenshots and reports.

Enables an AI to statically lint Dockerfiles and compose files without Docker by parsing their true structure, reporting line-numbered findings with concrete fixes, and analyzing cache order, .dockerignore matching, and safe fix previews as diffs.

Enables agents to lint OpenAPI specifications and detect breaking changes between two versions of an API, normalising Swagger 2.0 and OpenAPI 3.0/3.1 into a single model with safe $ref resolution. It exposes schema-walking and example-validation tools over stdio, classifying each change as breaking, potentially breaking, or safe based on request-versus-response variance.

Exposes a set of CLI tools (test generation, documentation generation, linting, test running, code search) to AI assistants via MCP, allowing them to perform these tasks through natural language.

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.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

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.

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.

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

Automated Playwright E2E test repair powered by a self-improving, governed MCP server that runs failing tests, collects failure artifacts, reasons about root causes, validates and applies fixes, and re-runs to verify.

An MCP server that turns Zod schemas into mocks, violations, and contract tests, enabling AI agents to reason about API contracts without manually crafting payloads.

Enables AI assistants to access RenderDoc capture data and assist with graphics debugging by retrieving draw calls, shader information, buffers, textures, and pipeline state.

Turns JUnit XML test reports from CI into queryable QA metrics, letting AI assistants answer plain-English questions about failures, flaky tests, regressions pinned to a commit, root-cause clusters, and performance drift. Also generates ready-to-share QA health reports and self-contained HTML dashboards without logging into separate tools.

Rarefaction is a Model Context Protocol (MCP) server that gives a coding agent the call graph and code context for one symbol straight from the project's language server (Language Server Protocol, LSP). Ask about a function, method or type and get, in one call: its signature and doc, its body, who calls it (tests marked), what it calls, who implements it, the tests that reach it and the short list

Enables AI assistants to take measurements from a Siglent SDM3045X bench multimeter over USB or LAN, including single readings, timed samples, fast bursts, and device information. Optionally exposes raw SCPI commands for advanced instrument control.

MCP server that generates ISO/IEC/IEEE 29119-3 compliant test plan drafts from project information, providing a structured resource for the standard's test plan outline and a tool for draft creation.

Allows LLM agents to query Playwright JSON test results for run summaries and failure information.

Enables coding agents to examine a rendered web page by cross-checking what the browser reports about the DOM/CSS against what is actually painted in pixels, pinpointing which element is broken and the evidence why. Reports visual problems without requiring a baseline, and is designed to grow into design-token extraction and accessibility/UX audits built on Playwright.

Enables natural-language validation of IFC models against Türkiye's building-permit BIM regulation, including compliance checks, EK-9 form generation, and queries about required classes and properties.

Local MCP server for querying VCD waveform files via SQLite; enables AI agents to ask precise questions about signal values, transitions, and clock cycles without dumping raw VCD text.

A real security scanning MCP server for medical information systems, supporting port scanning, vulnerability detection, medical system identification, and compliance report generation.

Enables defect management on the SEPP platform, allowing users to query defects, retrieve user project information, and set up automated monitoring with alerts for new or unresolved defects via webhook or email.

Enables AI assistants to analyze and inspect RenderDoc capture files, providing tools to examine draw calls, textures, buffers, and shader information. It allows developers to perform graphics debugging and resource analysis through natural language interactions.

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.

An MCP server that provides elicitation tools to interactively request user input, such as selecting options, providing information, or performing manual tests.

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.

Provides a portable local MCP interface for RTL simulation, diagnostics, and verification workflows with Verilator, currently exposing server information, tool discovery, and bounded text diagnostic parsing while lint and simulation execution remain planned.

Enables RTL simulation and hardware verification with Verilator through automatic testbench generation, natural language queries about simulations, waveform analysis, and protocol-aware testing for Verilog/SystemVerilog designs.

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.

A server that converts Allure test reports into LLM-friendly formats, enabling AI models to better analyze test results and provide insights about test failures and potential fixes.

Enables AI agents to run evidence-based technical SEO, AEO, LLMO and GEO checks on public pages, fix the issues found, and verify the fixes by re-running the same checks.

Enables mobile device management, app upload, automation session execution, and test result viewing through the Kobiton platform, integrated with AI coding assistants.