Memorylens MCP

MemoryLens MCP

On-demand .NET memory profiling with concrete, AI-actionable code fix suggestions — no profiler to install.

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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

/plugin install memorylens --marketplace MarcelRoozekrans/memorylens-mcp

The plugin runs the NuGet tool through dnx, so it needs the .NET 10 SDK and nothing else.

.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 , 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.

Family

MemoryLens MCP is part of a small family of .NET projects for AI work:

• AI.Sentinel: Security monitoring middleware for IChatClient: prompt injection, hallucination and PII detection with an audit trail.
• Minos.NET: Typed, source-generated decision questions with calibrated answers.
• Rag.NET: A modular retrieval-augmented generation pipeline on Microsoft.Extensions.AI.
• Roslyn CodeLens MCP: Semantic .NET code intelligence for AI agents over MCP.

Built on ZeroAlloc

MemoryLens MCP uses these ZeroAlloc libraries, which are source generators and analyzers with no reflection at runtime:

• ZeroAlloc.Analyzers

License

MIT