"A server with Bybit integration capable of interacting with P2P" matching MCP servers:

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Provides a read-only MCP bridge that lets Claude Web inspect workspace files, git state, and recorded test results while Codex remains the sole executor of changes.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

MCP server providing tools to verify task completion (tests pass and changes match claim) and confirm bug reproduction before fixing.

Free, MIT-licensed MCP testing harness with Jay: risk-based testing workflows, exploratory and regression checks, accessibility investigation, bug triage, disposable demos, and protected local evidence reports. Runs locally via @testers-ai/carbon-mcp; the coding agent supplies model, browser, and shell access, and analytics defaults off in the npm edition.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

Enables intelligent analysis of regression test failures and automatic discovery of solutions in JIRA. Analyzes test logs using AI-driven algorithms and matches errors with relevant JIRA issues through natural language interactions.

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.

Generates a pytest suite from acceptance criteria withheld from the coding agent, so the tests can disagree with the code; qikly_run never returns the criteria. The CLI also scores an existing suite by planting faults, and ships as an agent Skill (qikly --install-skill). Runs locally (uvx --from "qikly[mcp]" qikly-mcp): your code and API key stay on your machine.

AI-controlled browser farm — run N isolated Chromium instances with 36 MCP tools for device emulation, screenshots, network/geolocation simulation, and diagnostics. Cross-platform: Windows GUI + headed/headless on Linux/macOS.

Zero-dependency MCP server and CLI that proxies, inspects, and analyzes JSON-RPC message streams between MCP clients and servers.

Validates Mermaid diagrams with comprehensive grammar parsing supporting 28+ diagram types. Processes markdown files, ZIP archives, and direct input with detailed error reporting and enterprise-grade performance capabilities.

Enables MCP clients to inspect, edit, and validate Arma Reforger addons in Workbench, including Enforce Script source, prefab/world files, resource GUIDs, and Workbench startup diagnostics.

A shared, concurrency-safe ledger for LLM code audits. It enables multiple agents to collaboratively report, verify, and resolve bugs with deduplication and hallucination resistance.

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.

Enables MCP clients like Claude Desktop and Claude Code to drive a real browser with SeleniumBase automation. Offers three variants: WebDriver-based general automation, Pure CDP Mode for bot-detection bypass, and a broad SB() API with MFA and downloads.

Verificate combines 17 applications each designed to address a separate issue with AI Coding supported with a Frontier AI Model. This allows you to Vibe Code with confidence and even dropped down to a more basic model with a better outcome.

Free MCP tools for context engineering audits of AI agents, offering on-demand analysis of system prompts, tool definitions, and context packing.

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.

MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.

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

A Model Context Protocol (MCP) server for interacting with SonarQube code quality platform.

Audits a module against its test suite and reports the cases the tests are structurally unable to see, then proves each gap with a real failing test rather than a warning. Covers Python, TypeScript, JavaScript, Java, Rust and Go.

A local MCP server that routes QA reviews through deterministic profiles and risk-based escalation while keeping source evidence and final decisions with the host.

Translation quality evaluation MCP Server powered by xCOMET. Provides quality scoring (0-1), error detection with severity levels, and optimized batch processing for AI-assisted translation workflows.

VAST validation MCP server that checks ad tags against IAB VAST specifications, returning structured JSON with rule IDs, XPath locations, and spec references.

Enables agents to verify completion by cold-running an acceptance command and reporting whether it exits with code 0, returning a pass/fail receipt with output and refusal reasons.

MCP bridge that lets any MCP client drive the Alethia runtime for agent-native E2E testing. It provides a per-step policy gate that blocks destructive actions with a cryptographic audit trail.

Enables a language model to run four deterministic data checks it cannot argue with — column-contract validation with an explicit quarantine list, arithmetic reconciliation of opening/movements/closing, distinct entity counting against row counts, and duplicate detection returned for human review — each answering with structured findings and row/column locators. Failures come back as results with reasons, so the model reports exactly which rows and columns are broken rather than asserting the data is clean.