Announcing the Zero-OOM Enterprise Engine for mcp-injector

Today, we're thrilled to announce the rollout of the Zero-OOM Enterprise Engine for mcp-injector (v0.3.0). This major architectural rewrite fundamentally solves one of the hardest challenges in AI developer tools: reliably indexing and navigating massive, enterprise-scale monorepos without melting the user's local machine.

The Problem with Massive Monorepos

As AI agents become more sophisticated, they are being deployed against increasingly massive codebases. Repositories containing 50,000+ files, sprawling Java microservices, and deep monorepo structures present a unique challenge. Previous versions of mcp-injector relied on global in-memory maps to track state. While extremely fast for typical repositories, this design hit a hard scalability ceiling on enterprise workloads, often resulting in Out-Of-Memory (OOM) panics or severe database locking contention.

Asynchronous Background Indexer

To eliminate these bottlenecks, we've completely eradicated legacy global in-memory maps. In their place, we have implemented a high-performance sequential asynchronous background indexer.

This new architecture utilizes isolated local variables and sequential SQLite inserts via InsertSymbolNodes. By streaming data directly to the disk-backed WAL database rather than hoarding it in RAM, mcp-injector can now process 50,000+ files while maintaining a flat, predictable memory footprint. You get the parsing speed you expect, but without the memory exhaustion.

OS-Level Auto-Remediation

At massive scales (100k+ files), we discovered that the Linux operating system itself becomes the bottleneck, specifically hitting the default inotify watch limits (ENOSPC). Instead of simply crashing or failing silently, the new daemon features OS File Watcher Auto-Remediation.

When the daemon intercepts an inotify queue limit crash, it safely pauses indexing, opens a direct /dev/tty interactive bridge to securely prompt the user for an automatic sudo sysctl limit increase, and dynamically resumes indexing without ever requiring a daemon restart.

Bulletproof Data Loss Prevention

As AI agents gain autonomy, protecting the integrity of your source code is paramount. We've introduced the API-Level Compression Guardrail. To prevent the notorious "Compression Trap"—where an AI accidentally overwrites your actual code with compressed FOLDWORK: CODE COMPRESSED placeholders—we've mandated a new write bridge.

Here's how an AI agent now interacts with the daemon to write a file, completely preventing the Compression Trap:

// The agent calls the injector_write_file tool
{
  "name": "injector_write_file",
  "arguments": {
    "path": "src/auth/handler.go",
    "content": "func Login() {\n  // Full, uncompressed code goes here\n}"
  }
}

What else is new?

• Surgical Scope Filtering: path_prefixes for Project Maps and search_paths for Semantic Search allow agents to isolate their operations to specific microservices, drastically reducing payload bloat.
• Architectural Diagramming: The new injector_diagram tool uses BFS traversal to generate Mermaid sequence diagrams, complete with a 500-node context circuit breaker to protect LLM context windows.
• Raw Regex Fallback: The injector_regex_search tool provides a direct bridge to native git grep -nE, bypassing FTS5 tokenization for flawless punctuation and regex matching.
• Implicit State Synchronization: Read tools now automatically wait for pending background indexing to complete using a non-blocking sync hook. The old injector_sync tool is now deprecated.

The Zero-OOM Enterprise Engine is live today in v0.3.0. Check out the Changelog for the full list of improvements, or update your daemon by re-running the installation script.