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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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    MCP server that connects Claude to Dependency-Track for natural language vulnerability triage, analysis, and management.
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    MIT
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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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    Enables AI agents to shorten URLs, manage links, and track click analytics through 8 first-class tools, designed for use with Claude Desktop, Cursor, and other MCP clients.
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    MIT
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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 AI assistants to read and modify KiCAD PCB designs through the KiCAD IPC API, providing tools for board queries, footprint placement, track creation, DRC, and export.
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    MIT
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    A Python-based MCP server that facilitates structured problem-solving through sequential thinking, branching, and confidence scoring. It allows users to track assumptions and manage multiple concurrent reasoning sessions to break down complex tasks.
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    MIT
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    An MCP server that helps AI agents plan and track software projects using the Conducted Development methodology, providing artifact validation, kickoff guidance, and project rituals.
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    Enables MCP-compatible coding agents to debug applications using runtime log data, by providing tools to start a local log-ingestion server, track debugging sessions and hypotheses, and correlate logs to specific executions.
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    MIT
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    Kernel-attached MCP server for Jupyter notebooks, enabling agents to manage cells, track dependencies, and execute code with minimal re-execution and condensed outputs.
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    A Model Context Protocol server that manages structured technical spike investigations and generates Architecture Decision Records through an enforced four-phase workflow. It enables developers to track exploration branches, document adversarial challenges, and maintain persistent state across multiple sessions.
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    16
    MIT