"Toggl Track" matching MCP servers:

Enables querying projects, fetching findings, triggering analysis, uploading CycloneDX BOMs, and checking async token status for OWASP Dependency-Track.

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

Exposes a set of honesty SDK TypeScript libraries as MCP tools so a coding agent can check grounded citations, corroborate evidence, test payout and mutation invariance, score trust and anonymous authenticity, validate provenance claims, track a claims registry and tamper-evident audit chain, and grade recommendations against outcomes. It also scaffolds two runtime kits for agent authorization receipts and pre-call cost governance.

An MCP server that unpacks and structures Playwright trace.zip archives so AI agents can perform root-cause analysis on CI failures, with 16 focused tools for inspection, DOM/UI analysis, root-cause analysis, and performance analysis.

Enables AI coding assistants to distribute iOS and Android builds to BetaDrop testers, manage share links, track releases, and manage API tokens entirely through natural language prompts.

Provides AI assistants with token-efficient access to Google Play Android Vitals, enabling them to diagnose ANRs, track crashes, analyze startup performance, and compare app versions for release optimizations.

Detects schema mismatches between data producers and consumers through static analysis, supporting extraction, comparison, code generation, and automated validation with watch mode for MCP tools, APIs, and service contracts.

Provides tools for AI agents to interact with the Orange Juice Online Judge API by managing problems and code submissions. Users can list problems, retrieve detailed descriptions, submit source code, and track submission status through natural language.

Enables comprehensive test management with JIRA's Zephyr system, allowing users to create and manage test plans, execute test cycles, track progress, and generate reports through natural language.

Static analysis engine that detects schema mismatches between data producers (like MCP servers) and consumers (like client code), preventing runtime errors by validating contracts at development time.

A local MCP server for prompt evaluation, enabling users to define test cases, run prompts against multiple LLM providers, score outputs with deterministic and LLM-graded assertions, and track quality over time, all within an AI coding environment.

Enables agents and developers to test HTTP client retry behavior against a synthetic service that emits 429/503 responses followed by 200, with Retry-After headers and server-observed attempt traces, plus optional sharing of synthetic evidence findings.

Enables AI agents to perform one-shot triage of Playwright trace files, pinpointing root causes like backend API failures or locator issues and generating self-healing code fixes in a single low-token call.

Enables coding agents to request, track, and verify independent cross-model reviews with structured findings, attachment hashes, and provenance receipts via Chrome Bridge, Codex, or Responses transports.

A dual-track testing server that combines CLI test execution with Playwright-based browser testing and persistent SQLite logging. It enables automated test pipelines, Git integration, and evidence-based requirement generation to streamline the development lifecycle.

A professional tool for automatically generating enterprise-standard tracking code for H5, mini-programs, and native applications. It streamlines event tracking for API calls, user clicks, and UI interactions while supporting JIRA integration for task-specific tracking.

MCP server that lets agents drive a browser and record every action into a replayable trace, then replay it deterministically with network request assertions. It exposes tools for navigating, clicking, filling, and verifying requests.

Enables users to analyze project materials and build traceability from requirements to coverage, identifying gaps.

Enables creating, validating, and simulating Crack-style story chat through a unified MCP server, with template and keyword management, automated project audits, session play, and persona-separated testing to prevent omniscience.

An MCP-compliant server that enables the execution of pytest test suites and the storage of results into a QA platform database. It allows AI models to trigger test runs, track execution progress, and retrieve historical test data through specialized tool interfaces.