FRENES

Developer tools · MCP server

Context rot detection: MCP server, setup and security

Context rot detection is an open-source MCP server published on GitHub that lets an AI agent monitor agent context health and receive recommendations for session recovery. Its repository has 13 stars and its last commit is from February 18, 2026 and it is licensed under MIT.

Last commit 8 months ago

What Context rot detection does

According to its repository: “Monitors agent context health: token utilization, context quality degradation and session fatigue, with 0-100 health scores and recovery recommendations.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Context rot detection is listed under Developer tools, a category of 475 servers in the directory, where it ranks #359 by stars.

Installing and connecting Context rot detection

No package or remote server is declared in the official MCP registry: you install it from source, following the repository README.

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: TypeScript.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Context rot detection. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on February 17, 2026, the milos-product-maker/context-rot-detection repository has 13 stars and 4 forks, with 2 open issues. It has more stars than 31% of the servers in the directory. Last change 8 months ago.

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

StorybookStorybook : 91.4K91.4KFront End ChecklistFront End Checklist : 74.6K74.6KTldrawTldraw : 51.1K51.1KCodebase MemoryCodebase Memory : 47.4K47.4KCCXTCCXT : 44.5K44.5KPostHog MCP ServerPostHog MCP Server : 40.4K40.4KClaude task masterClaude task master : 28.3K28.3KAgent Skills Search ServerAgent Skills Search Server : 26.6K26.6KContext rot detectionContext rot detection : 1313
GitHub stars of the most followed servers in the category (log scale); Context rot detection in orange. Checked October 25, 2026.

Verdict

Context rot detection is still a modest project: read its code and compare it with the alternatives before using it.

Strengths

  • Permissive licence (MIT)

Limitations

  • No commit for 8 months
  • Install from source

Frequently asked questions

How do I install Context rot detection?

No package is declared in the MCP registry: clone the milos-product-maker/context-rot-detection repository, follow its README, then declare the start command in your client's MCP configuration.

Is Context rot detection free?

The code is published under the MIT licence and can be used free of charge.

Is Context rot detection safe?

No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.

Is it an official server?

Nothing says so: it is a project published by milos-product-maker on GitHub. Check who maintains it before giving it access.

What are the alternatives to Context rot detection?

In the same category: Radar Kubernetes MCP Server, Repowise, Jscpd, and Apify MCP server. Radar Kubernetes MCP Server and Repowise score higher on our grid.

Related articles

Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score