by hmldns
Nautex AI MCP 服务器，作为编码代理的产品和项目管理工具，加快 AI 辅助开发，通过 CLI 界面配置的 Nautex.ai API 令牌实现需求沟通。
Repository-wide counts · Cached 2026-03-08
The Nautex MCP server is a publicly available project. Review the upstream repository for installation instructions, supported tools, compatibility, permissions, and current maintenance status.
Configuration, transport, authentication, and runtime requirements vary by project. Open the repository before connecting and use the smallest set of credentials and permissions required.
Open the Nautex repository to read the latest documentation.
Compare source, connection, and authentication details before choosing an implementation.
View the complete category
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Build-time snapshot · Retrieved 2026-10-05
This is an MCP server that integrates PRD and TRD building tool Nautex AI with the Coding Agents.
Supported agents:
Since LLM Coding Agents do not attend team meetings, there is the challenge of conveying complete and detailed product and technical requirements to them.
Nautex AI tool-chain manages step by step guiding of Coding Agents so they implement specification using small, relevant and testable steps.
Core principles are:
Nautex AI acts as an Architect, Technical Product Manager, and Project Manager for coding agents, speeding up AI-assisted development by communicating requirements effectively. This MCP server pulls guidance instructions from Nautex AI; tasks contain to-do items, references to the affected files, and requirements that are automatically synced for the Coding Agent's availability.
By Ivan Makarov
The chatbot conducts a briefing session with you, gathering questions and ideas until complete. It then generates comprehensive product and technical specifications.
(Example: A project I initiated to explore WebRTC.)
Product requirements:
Technical requirements:
You fill in details, clarify the specification, and resolve any TODOs flagged by the chatbot during the interview.
You'll occasionally need to review the code, so it's best to know in advance where to look and how everything is organized. This prevents the AI from making decisions—allowing it to focus on writing higher-quality code with greater attention to the task.
The image displays a file map generated by Nautex AI, with files linked to specific requirements and sections.
With the code location clarified, tasks are planned: Coding, Testing, and Review.
Reviews are scheduled early to demonstrate progress and verify alignment with goals.
The plan is structured in small, self-contained layers, building your project incrementally like floors in a skyscraper.
Configure the MCP server for your coding agent: connect to the Nautex cloud platform, select the project, and choose the implementation plan. The setup command writes all configuration to your project root.
In agent mode, instruct: "pull nautex rules, and proceed with the next scope."
At this stage, your specifications are synchronized in the .nautex directory and accessible to the Coding Agent. The MCP server continuously monitors their relevance.
That's it. You then review and accept substantial code segments that fully align with your expectations and requirements.
The fastest way to set up is via the web app onboarding flow, which generates a single command you copy and run in your project root:
uvx nautex setup --token <TOKEN> --project <PROJECT_ID> --plan <PLAN_ID> --agent <AGENT>
Parameters:
| Flag | Description |
|---|---|
--token, -t |
API token (create at nautex.ai) |
--project, -p |
Project ID |
--plan, -l |
Implementation plan ID |
--agent, -a |
Agent type: claude, cursor, codex, opencode, gemini, grok |
--yes, -y |
Skip confirmation prompts |
This validates your token, project, and plan, then writes all configuration to your project root:
.nautex/config.json — project config.nautex/.env — API token (git-ignored)Alternatively, run the interactive terminal UI:
uvx nautex setup
Follow the on-screen prompts to select your project, plan, and agent.
On macOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
On Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Check the latest instructions from the UV repo for details and updates.
All configuration is scoped per-project in your project root.
claude mcp add nautex -s local -- uvx nautex mcpCLAUDE.mdclaude mcp list and check for nautex: uvx nautex mcp.cursor/mcp.json.cursor/rules/nautex_workflow.mdcNote: After setup, Cursor may ask via popup whether to enable the new MCP — answer yes. In any case, go to File -> Preferences -> Cursor Settings -> Tools & Integrations and make sure the Nautex MCP toggle is enabled (green).
.codex/config.toml (project-local, backup created as config.toml.bak before first write)AGENTS.md/mcp command inside Codex to confirm nautex is listedopencode.json (project root, preserves unrelated fields, backup as opencode.json.bak if unparsable)AGENTS.mdstatus.gemini/settings.jsonGEMINI.md.grok/config.toml (project-local, backup created as config.toml.bak before first write; startup_timeout_sec = 20)AGENTS.mdgrok mcp list / grok mcp doctor nautex, or open /mcps in the Grok TUIOnce setup is complete, launch your coding agent and tell it:
Check nautex status
After confirming the connection works:
Pull nautex rules and proceed to the next scope
Proceed with the plan by reviewing progress and supporting the Agent with validation feedback and inputs.
Before running setup, prepare your project in the Nautex web app:
The web app onboarding flow will generate the setup command with all IDs pre-filled.
