codex-mcp-doctor — npm doctor for MCP

The description states that codex-mcp-doctor is a diagnostic and security scanner for Codex, intended to catch misconfigurations, prompt injection, rug-pulls, and homoglyph attacks in MCP servers. It is described as a zero-dependency tool, built by one person (LUO GANGAN), and submitted to the OpenAI 2026 hackathon.

The project appears to be a developer tool aimed at improving security and reliability of Codex-based systems. However, there is no evidence of revenue, customers, or adoption. The author’s own write-up is minimal — limited to a tagline and a few technology tags.

The description states that codex-mcp-doctor is a diagnostic and security scanner for Codex. It is described as a zero-dependency tool that addresses issues such as broken configurations, prompt injection, rug-pulls, and homoglyph attacks in MCP servers.

• The product is built using Python.
• It is related to the MCP (Model Communication Protocol) and Codex frameworks.
• It was submitted to the OpenAI 2026 hackathon.

Inference Based on the name and tagline, it appears to be a command-line utility or script that checks for misconfigurations or security flaws in Codex environments. However, no code, documentation, or usage examples are provided.

The description states that MCP servers fail silently, and that codex-mcp-doctor is intended to catch such issues. It positions itself as a diagnostic tool for Codex environments, with an emphasis on security.

• The author claims it catches:
  • Broken configurations
  • Prompt injection
  • Rug-pulls
  • Homoglyph attacks

These are all security-related concerns in AI systems.

Inference The positioning is that of a lightweight, no-dependency tool for developers or operators working with Codex and MCP. It is not described as a commercial product or SaaS offering.

The description does not state the target customer or ideal customer profile (ICP) explicitly.

• The tool is aimed at users of Codex and MCP, which are likely developers or system operators working with AI models.
• It is described as a diagnostic tool, suggesting it may be used in development or deployment environments.

Inference The ICP appears to be developers or DevOps engineers working with Codex-based systems. However, no explicit customer segment or persona is defined.

There is no evidence of pricing or business model in the description.

• The tool is described as zero-dependency and open-source (implied by its nature as a CLI utility).
• No mention of monetization, subscriptions, or licensing.

Inference It appears to be a free, open-source tool. There is no indication of a commercial offering or pricing model.

The description states that the tool is built with:

• Python
• MCP
• Codex
• GitHub

It is described as a zero-dependency diagnostic tool.

Inference The tool likely runs in a CLI environment, and its delivery mechanism is not specified. It may be distributed via npm or GitHub, but this is not confirmed.

There is no evidence of traction, customers, or adoption.

• The project was submitted to the OpenAI 2026 hackathon.
• It is described as a solo effort (1 person team).
• No usage metrics, user feedback, or deployment data are provided.

Inference The tool is likely in early development or prototype stage. There is no evidence of real-world use or product-market fit.

The description does not mention any competitors.

• It is described as an npm doctor for MCP.
• It targets security and configuration issues in Codex environments.

Inference The competitive context is unclear, but it likely competes with general diagnostic tools for AI systems or developer tooling. No direct competitors are named.

• No evidence of real-world use or adoption: The project was submitted to a hackathon and lacks any traction.
• Minimal description: There is no detailed write-up, documentation, or usage examples.
• Solo development: Only one person is listed as the team member — raises questions about scalability and long-term maintenance.
• No commercialization path: No indication of monetization, partnerships, or product roadmap.

1. What is the intended use case for this tool beyond the hackathon?
2. Is there a plan to make it available via npm or other distribution channels?
3. How does it detect prompt injection or rug-pulls in practice?
4. Are there any known limitations or edge cases in its current implementation?
5. What is the roadmap for future development and maintenance?