What is the 'Neural Link'?

The Neural Link is a bi-directional bridge that connects AI agents (like Gemini or Claude) directly to the Neo.mjs runtime. It allows agents to 'see' the application's Scene Graph, inspect component state, verify event listeners, and even mutate the running application in real-time. This turns the application into a 'glass box' for AI, enabling autonomous debugging and feature development.

Why is Neo.mjs called an 'Application Engine' instead of a framework?

Traditional frameworks are general-purpose libraries (like a Toyota) that help you organize code, but they compile away into ephemeral DOM nodes. Neo.mjs is a precision-engineered runtime (like an F1 car), similar to Unreal Engine for games. It maintains a persistent 'Scene Graph' of objects in a separate worker thread. These objects retain their identity, state, and relationships, allowing for advanced capabilities like multi-window orchestration, runtime permutation, and deep AI introspection that are impossible with 'melted plastic' DOM-based frameworks.

What is 'Context Engineering'?

Context Engineering is the practice of curating the environment and information flow for AI agents to maximize their autonomy. In Neo.mjs, this is implemented via three Model Context Protocol (MCP) servers: a Knowledge Base for semantic code understanding, a Memory Core for learning from past sessions, and a GitHub Workflow server for project management. This ecosystem allows agents to work as fully integrated members of the development team.

What is 'Object Permanence' in the context of Neo.mjs?

In Neo.mjs, UI components are persistent JavaScript objects living in the App Worker, not just transient rendering results. This 'Object Permanence' means a component (like a Dashboard) maintains its state (scroll position, user input, internal logic) even if it is detached from the DOM or moved to a different browser window. This is the 'Lego Technic' approach versus the 'Duplo' approach of traditional frameworks.

What is an 'Agent Operating System'?

Neo.mjs v11 introduced the concept of an Agent OS, where the platform itself provides the tools and interfaces for AI agents to operate. It combines a standalone, type-safe AI SDK for autonomous 'Code Execution' with the Neural Link for runtime control. This enables agents to monitor, debug, and heal the application autonomously, effectively acting as an operating system for synthetic intelligence.

How does Neo.mjs handle multi-window applications?

Neo.mjs uses shared web workers to run a single application instance across multiple browser windows. All windows share the same application state and data in real-time. Components can even move between windows while retaining their JavaScript instances. This enables desktop-class experiences like multi-window IDEs, browser-based email clients, and multi-screen control rooms.

What makes Neo.mjs different from React, Angular, or Vue?

Neo.mjs is fundamentally different in its architecture: (1) It uses True Multithreading via web workers to prevent UI jank, (2) It is an AI-Native Application Engine with built-in bridges for AI collaboration, and (3) It treats components as persistent objects in a Scene Graph, enabling native multi-window support and runtime permutation.

Frontmatter

id	7405
title	MCP Server: Implement Knowledge Query Endpoint
state	Closed
labels	enhancementhelp wantedgood first issuehacktoberfestai
assignees	Mahita07
createdAt	Oct 7, 2025, 12:14 PM
updatedAt	Nov 2, 2025, 1:30 PM
githubUrl	https://github.com/neomjs/neo/issues/7405
author	tobiu
commentsCount	3
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Nov 2, 2025, 1:30 PM

MCP Server: Implement Knowledge Query Endpoint

Name: Neo.mjs Application Engine
Author: Neo.mjs

Closed v11.0.0 enhancementhelp wantedgood first issuehacktoberfestai

tobiu commented on Oct 7, 2025, 12:14 PM

This ticket is for implementing the core search functionality of the Knowledge Base MCP server. This involves exposing the refactored query logic via the POST /documents/query endpoint.

Acceptance Criteria

The server is extended to implement the POST /documents/query endpoint.
The endpoint accepts a JSON body with a query string and optional filters.
The endpoint uses the refactored query module to perform a search against the ChromaDB collection.
The endpoint returns a structured JSON response containing the search results, as defined in the design ticket.
Appropriate error handling is implemented.

Agent Instructions

To work on this ticket, please start your session with the following prompt:

Please follow the instructions in @AGENTS.md.

Once you are initialized, I want to work on a sub-task for the "Architect AI Tooling as a Model Context Protocol (MCP) Servers" epic.

My task is to implement: MCP Server: Implement Knowledge Query Endpoint

You will need to read the epic context from @.github/ISSUE/epic-architect-ai-tooling-as-mcp.md and the specific ticket details from @.github/ISSUE/ticket-implement-knowledge-query-endpoint.md before we begin.

tobiu added the enhancement label on Oct 7, 2025, 12:14 PM

tobiu added parent issue #7399 on Oct 7, 2025, 12:14 PM

tobiu added the help wanted label on Oct 7, 2025, 12:14 PM

tobiu added the good first issue label on Oct 7, 2025, 12:14 PM

tobiu added the hacktoberfest label on Oct 7, 2025, 12:14 PM

tobiu added the ai label on Oct 7, 2025, 12:14 PM

Mahita07 Oct 8, 2025, 4:48 AM

@tobiu Could you please assign this ticket to me ?

tobiu Oct 8, 2025, 11:38 AM

Sure and thx!

tobiu assigned to @Mahita07 on Oct 8, 2025, 11:38 AM

tobiu removed parent issue #7399 on Oct 24, 2025, 11:26 AM

tobiu Nov 2, 2025, 1:30 PM

already resolved

tobiu closed this issue on Nov 2, 2025, 1:30 PM