MemoryLens.Mcp 2.1.1

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<h1 align="center">MemoryLens MCP</h1>

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<p align="center"> On-demand .NET memory profiling with concrete, AI-actionable code fix suggestions — no profiler to install. </p>

<a href="https://glama.ai/mcp/servers/MarcelRoozekrans/memorylens-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/MarcelRoozekrans/memorylens-mcp/badge" alt="memorylens-mcp MCP server" /> </a>

Hosted deployment

A hosted deployment is available on Fronteir AI.

Quick Start

npx (any MCP client)

{
  "mcpServers": {
    "memorylens": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "memorylens-mcp"]
    }
  }
}

The npm package ships no server code — it is a launcher that installs the MemoryLens.Mcp .NET global tool at a matching version and execs it, so the .NET 10 SDK must be on PATH. Subsequent starts skip the install entirely and work offline.

VS Code / Visual Studio (via dnx)

Add to your MCP settings (.vscode/mcp.json or VS settings):

{
  "servers": {
    "memorylens": {
      "type": "stdio",
      "command": "dnx",
      "args": ["MemoryLens.Mcp", "--yes"]
    }
  }
}

Claude Code Plugin

claude install gh:MarcelRoozekrans/memorylens-mcp

.NET Global Tool

dotnet tool install -g MemoryLens.Mcp

Docker

docker build -t memorylens-mcp .
docker run -i --rm --pid=host --cap-add=SYS_PTRACE \
  -v /tmp:/tmp \
  -v "$PWD:/workspace" memorylens-mcp

Profiling from a container needs ptrace and the host PID namespace, and on Docker Desktop that namespace is the Linux VM rather than your desktop — see docs/docker.md before choosing this route.

-v /tmp:/tmp is what makes list_processes return anything — the runtime's diagnostic sockets live in the temp directory — and it is also what keeps snapshots alive after --rm, since they are written to /tmp/memorylens-snapshots inside the container. -v "$PWD:/workspace" is only so .memorylens.json is picked up; nothing is written there.

Prerequisites

• .NET 10 SDK, 10.0.4xx feature band (pinned in global.json)

Running a filtered subset of the tests, e.g. dotnet test --filter <name>, will exit with code 9 and print error: 1, failed: 0. That's the test project's discovery-collapse guard (--minimum-expected-tests) firing because the filter left fewer tests than expected — it is not a test failure, and a full dotnet test run is unaffected.

How Collection Works

MemoryLens collects heap data in-process over EventPipe, the .NET runtime's built-in diagnostics channel. There is no profiler to install, no download on first use, and no external tool on PATH.

snapshot attaches to a running .NET process by pid, induces a collection, and aggregates the heap into per-type counts and sizes. Snapshots are written as small JSON files under your temp directory and referenced by a short id.

On Linux and in containers, attaching to another process's diagnostic endpoint may require matching UID or SYS_PTRACE — see docs/docker.md.

Available MCP Tools

Built-in Rules

Configuration

Create a .memorylens.json file in your project root to customize rule behavior:

{
  "rules": {
    "ML001": { "enabled": true, "severity": "critical" },
    "ML002": { "enabled": true, "severity": "critical" },
    "ML003": { "enabled": true, "severity": "high" },
    "ML004": { "enabled": true, "severity": "high" },
    "ML005": { "enabled": true, "severity": "medium" },
    "ML006": { "enabled": true, "severity": "medium" },
    "ML007": { "enabled": true, "severity": "medium" },
    "ML008": { "enabled": true, "severity": "low" },
    "ML009": { "enabled": true, "severity": "low" },
    "ML010": { "enabled": true, "severity": "low" }
  }
}

Usage Examples

Single Snapshot

Capture a memory snapshot of a running process to inspect current memory state:

> /memorylens
> Take a snapshot of my running API (PID 12345)

Claude will call snapshot with the target PID, then analyze the returned snapshot id and present findings ordered by severity.

Before/After Comparison

Detect memory growth by comparing two snapshots taken with a delay:

> /memorylens
> Check if my app has a memory leak — compare before and after processing 1000 requests

Claude will call compare_snapshots with a delaySeconds value (default 10 seconds) between the two captures, then analyze the diff to identify objects that grew between snapshots.

License

MIT