Nautex
Integrates Nautex AI's specification and planning tool with coding agents to manage requirements, tasks, and code generation in small, testable steps.
README
This is an MCP server that integrates PRD and TRD building tool Nautex AI with the Coding Agents.
Supported agents:
- Claude Code
- Cursor
- Codex
- OpenCode
- Gemini CLI
Motivation
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:
- start from foundational parts, de-risk them, then build up;
- do not overwhelm Coding Agents by large problem at once;
- plan project files map and link them to requirements and to tasks: Coding Agents don't get lost, you know how to navigate brand new code base;
- manage developer attention for verification and validation in right moment for review.
How It Works
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
<details> <summary>Usage Flow Presentation (unfold me)</summary>
Requirements Specifications
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:

Specification Refinement
You fill in details, clarify the specification, and resolve any TODOs flagged by the chatbot during the interview.

Codebase Map and Project Files
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.

Agent Tasks
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.

Integration
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.

Coding with Coding Agents
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.

</details>
Setup
Quick Setup (one command)
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 |
--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)- MCP config — agent-specific (see below)
- Agent rules — merged into existing rule files without overriding your content
Interactive Setup (Terminal UI)
Alternatively, run the interactive terminal UI:
uvx nautex setup

Follow the on-screen prompts to select your project, plan, and agent.
<details> <summary>How to Install UV</summary>
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. </details>
What Gets Written Per Agent
All configuration is scoped per-project in your project root.
<details> <summary>Claude Code</summary>
- MCP: registered via
claude mcp add nautex -s local -- uvx nautex mcp - Rules: managed section added to
CLAUDE.md - Verify: run
claude mcp listand check fornautex: uvx nautex mcp</details>
<details> <summary>Cursor</summary>
- MCP config:
.cursor/mcp.json - Rules:
.cursor/rules/nautex_workflow.mdc
Note: 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).
</details>
<details> <summary>Codex</summary>
- MCP config:
.codex/config.toml(project-local, backup created asconfig.toml.bakbefore first write) - Rules: managed section added to
AGENTS.md - Verify: use the
/mcpcommand inside Codex to confirmnautexis listed </details>
<details> <summary>OpenCode</summary>
- MCP config:
opencode.json(project root, preserves unrelated fields, backup asopencode.json.bakif unparsable) - Rules: managed section added to
AGENTS.md - Verify: invoke the Nautex MCP tool from OpenCode and run
status</details>
<details> <summary>Gemini CLI</summary>
- MCP config:
.gemini/settings.json - Rules: managed section added to
GEMINI.md</details>
Start Coding
Once 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.
Prerequisites
Before running setup, prepare your project in the Nautex web app:
- Sign up and create an API token
- Create PRD and TRD documents (chat with the bot to capture requirements)
- Create a files map of the project
- Create an implementation plan
The web app onboarding flow will generate the setup command with all IDs pre-filled.
Projects built with nautex
Best practice from the community
<a href="https://discord.gg/nautex" target="_blank"><img src="doc/join_discord.png" alt="drawing" width="200"/></a>
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.