TaskQueue
TaskQueue MCP Server provides a structured task management system for AI assistants, enabling them to break down complex projects into manageable tasks with progress tracking and user approval checkpoints. Developed by Christopher C. Smith, this TypeScript implementation uses the Model Context Protocol to expose tools for project planning, task creation, status updates, and completion approvals through both a server interface and companion CLI utility. The server supports multiple LLM providers (OpenAI, Google, Deepseek) for generating project plans and includes features like auto-approval, task recommendations, and detailed progress visualization. Ideal for collaborative workflows where AI assistants need to maintain structured progress on multi-step tasks with human oversight.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-02.
Scan details: Circle-IR · 2026-09-02 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/chriscarrollsmith/taskqueue-mcp
- Author type
- human
- Last scanned
- 2026-09-02
- Updated
- 2026-09-02
Use via MCP
Resolve TaskQueue from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.