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"Toggl Track" matching MCP servers:

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    Enables fetching and analyzing Toggl time tracking data with intelligent parsing of Fibery entity references from task descriptions. Features smart caching, user filtering, and aggregated reporting to help track time spent on specific projects and entities.
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    Enables Claude to monitor and inspect running processes through a lightweight wrapper that captures stdout/stderr logs and stores process metadata in SQLite. Users can track any command execution and query process status, logs, and history through natural language.
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    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.
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    MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.
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    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.
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    An MCP server that captures and analyzes function-level profiling data from NetEase Minecraft Bedrock Edition mods via the native Tracy profiler, enabling AI to identify performance hotspots and verify optimizations.
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    Enables brand visibility monitoring across major AI platforms like ChatGPT, Claude, Gemini, and Perplexity. It allows users to track visibility scores, analyze competitor data, and receive actionable insights to improve AI-generated brand recommendations.
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    Enables monitoring and debugging of Hatchet workflows and jobs through MCP clients like Claude Code. Users can list workflows, track run statuses, query queue metrics, and retrieve job results using natural language commands.
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    MCP server implementation that enables LLMs to interact with Rollbar error tracking data, allowing users to list and analyze errors, view occurrences, track deployments, and access project information.
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    Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.
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    Enables querying Superlines AI visibility analytics data to track brand performance, citations, and competitor insights across AI-powered search engines like ChatGPT, Perplexity, Gemini, and Claude.
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    MIT