MemoryLens MCP

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

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

Tool	Description
`list_processes`	Lists running .NET processes available for profiling, discovered from their diagnostic IPC endpoints
`snapshot`	Captures a single memory snapshot of a target process
`compare_snapshots`	Captures two snapshots with configurable delay and compares them
`analyze`	Runs the rule engine against a captured snapshot and returns findings
`get_rules`	Lists all available analysis rules with their metadata

Built-in Rules

ID	Severity	Category	Description
ML001	critical	leak	Event handler leak detected
ML002	critical	leak	Static collection growing unbounded
ML003	high	leak	Disposable object not disposed
ML004	high	fragmentation	Large Object Heap fragmentation
ML005	medium	retention	Object retained longer than expected
ML006	medium	allocation	Excessive allocations in hot path
ML007	medium	retention	Closure retaining unexpected references
ML008	low	allocation	Array/list resizing without capacity hint
ML009	low	pattern	Finalizer without Dispose pattern
ML010	low	pattern	String interning opportunity

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