"Toggl Track" matching MCP servers:

An MCP server that enables AI clients to autonomously discover, analyze, fix, test, submit, and track open-source contributions on GitHub through specialized tools.

Adds persistent task management to AI coding assistants in IDEs, allowing them to track multi-step tasks across sessions and maintain organized workflow with project-scoped todo lists.

Graph-native bounded working memory for coding agents with typed memories, causal retrieval, current-vs-stale state queries, and compact small-model briefs.

Human-in-the-loop feedback for AI coding agents. Pause your agent mid-task, give it new directions, attach screenshots, and keep it on track — without restarting the conversation.

Enables STDIO-compatible AI assistants to queue coding work onto the official Google Antigravity CLI and track status, read reports or patches, query capabilities, or cancel running tasks without blocking. Supports trusted direct execution plus optional isolated or read-only modes depending on the platform.

Controls coding agents by enforcing structured implementation plans with PRs and commits as work units, requiring user review at each commit checkpoint to prevent agents from going off track. Includes a visualization dashboard for tracking progress in real-time.

Enables a Grok Bot to reach a local MCP server over loopback to discover eligible projects, start or continue Codex CLI jobs in sandboxed modes, track results, and handle separate OpenAI Responses text requests.

A graph-based MCP server that provides AI coding agents with persistent memory to store patterns, track complex relationships, and retrieve knowledge across sessions. It leverages graph structures to handle temporal queries and relational paths that traditional vector stores often miss.

An MCP server that bridges CLI coding agents like Claude Code, Codex, opencode, and Antigravity into any MCP client, enabling synchronous and asynchronous task execution, follow-up input, and a structured code review tool.

Enables coding agents to track cross-repo dependencies, surface affected consumers before changing shared artifacts, and monitor pinned versions for vulnerabilities.

Enables programmatic execution of coding tasks and autonomous file operations using Claude AI. It allows agents to search codebases, run shell commands, and track file changes through the Model Context Protocol.

Connects AI assistants to the Repovive competitive-programming platform, enabling them to browse problems and contests, judge and submit code for real verdicts, track ratings, communicate with users, and author verified problems and rounds.