"Exploring the term 'Make' or related resources" 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 AI agents to create webhook URLs, wait for incoming deliveries, inspect payloads, and replay or send signed webhook events to a local handler.

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

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

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.

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 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.

Enables AI agents to submit npm, PyPI, Go, or Maven packages and manifests to the free OSV.dev database and receive per-package and per-advisory results with CVE mappings, CVSS scores, severities, and the versions that fix each issue. It also supports filtering by minimum severity, de-duplicating alias advisories, and machine-readable error/warning rows for CI gates and upgrade recommendations.

Enables coding agents to verify whether a GitHub pull request is genuinely ready by checking draft state, conflicts, check results on the head commit, review requirements, merge queue status, and expected commit, returning a verdict with reasons and owners pinned to a commit and timestamp. It reports merged or closed PRs without judging them and never authorizes a merge.

Fail-closed merge gate for AI coding agents: verify_repo runs the tests that existed before the change against a git working tree and answers MERGE, BLOCK or INCONCLUSIVE. The agent names what to judge; the operator pins the baseline and the floors.

Enables AI agents to hire real human operators for tasks requiring physical presence, human perception, or judgment, such as verification, testing, data collection, and physical-world tasks.

Measures what MCP servers cost in context tokens: probes a URL or a local command now, measures a whole config file, and looks up the daily results for every server in the official MCP registry on mcpcost.ulehla.dev.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

Enables conversational analysis of n8n workflows and Make scenarios for silent-failure risks, with tools to explain findings, compare workflow versions, and list rules.

Lets an AI assistant statically scan HTML and JavaScript landing-page code that turns Meta's fbclid URL parameter into the _fbc cookie (and hand-written _fbp cookies), flagging known ways the click ID is changed, truncated, or kept from an older ad, without executing the code or contacting Meta.

Lets AI agents ask which tests a Playwright or Vitest project contains, which are skipped and why, by reading the test source files statically. Nothing is executed, written, or sent over the network, and answers come back as paged JSON with summaries, filters, and scan diagnostics.

Enables AI agents to drive the 1C:Enterprise test client directly, opening forms, reading and modifying data, working with tables, and recording or replaying action scenarios. Connects to local or remote test clients over their network protocol without a testing manager.

An MCP server that gives coding agents hands and eyes on an Android device, providing 19 tools for building, testing, installing, UI inspection, input, logs, and toolchain diagnosis. It returns concise, structured results to make the build-debug loop efficient.