3. Agentic Development Tools

OpenMC ships a set of tools designed for AI coding agents (such as Claude Code) that agents can use to navigate and understand the codebase.

3.1. Motivation

Agentic tools like Claude Code are skilled at using grep to navigate and understand large code bases. However, grep can only find exact text matches — it cannot discover code that is conceptually related but uses different naming. Without a “global view” of the codebase that a human developer will build up over time, the agent is generally blind to any file it hasn’t tokenized fully. While it can grep to see who else calls a function, it remains blind if other areas might be related but not share identical naming conventions.

This problem is mitigated somewhat by using a model with a longer context window. OpenMC has somewhere around ~1 million tokens of C++ and ~1 million tokens of python. While Claude Code in early 2026 only has a context window of 200k tokens, beta versions have extended context windows of 1M tokens, and it’s not unreasonable to assume that models may be available in the near future that greatly exceed these limits.

However, even assuming the entire repository can be fit within a context window, there are several downsides to doing this. Model performance degrades significantly as context size increases. Benchmark results are greatly improved if the model has less garbage to pick through. Additionally, API usage is typically billed as tokens in/out per turn. As the context file grows these costs become much larger. As such, there is still significant motivation to solving the above problem, so as to ensure only relevant information is drawn into context so as to maximize model performance and minimize costs.

3.2. Setup

The tools are registered as an MCP (Model Context Protocol) server in .mcp.json at the repository root. AI agents that support MCP (such as Claude Code) discover them automatically on session start. The underlying Python scripts can also be run directly from the command line.

All tools run entirely locally — no API keys or external service accounts are required. Python dependencies are installed automatically into an isolated virtual environment at .claude/cache/.venv/ on first use.

3.3. RAG Semantic Search

The RAG (Retrieval-Augmented Generation) semantic search addresses this problem — it finds code by meaning, not just text match, surfacing related code across subsystems that grep would miss entirely. Two MCP tools are provided:

• openmc_rag_search — Given a natural-language query, returns the most relevant code chunks with file paths, line numbers, and a preview. Can search code, documentation, or both. Can also find code related to a given file.

• openmc_rag_rebuild — Rebuilds the search index. Should be called after pulling new code or switching branches.

3.3.1. How it works

The search pipeline runs entirely on your local CPU:

1. Chunking. All C++, Python, and RST files are split into overlapping fixed-size windows (~1000 characters, 25% overlap). This ensures every line of code appears in at least one chunk and most lines appear in two.

2. Embedding. Each chunk is embedded into a 384-dimensional vector using the all-MiniLM-L6-v2 sentence-transformer model (22 million parameters). This model runs on CPU with no GPU required. No API key is needed — the model weights are downloaded once from Hugging Face and cached locally.

3. Indexing. The vectors are stored in a local LanceDB database on disk. Building the full index takes approximately 5 minutes on a machine with 10 CPU cores. The index is stored in .claude/cache/rag_index/ and persists across sessions.

4. Searching. Your query is embedded using the same model, and the closest chunks are retrieved by vector similarity. Results include the file path, line range, file type, similarity distance, and a text preview.

3.3.2. Requirements

No system dependencies beyond Python 3.12+ with pip. An internet connection is required on first use to download the Python packages and embedding model weights; subsequent runs are fully offline. The Python packages (sentence-transformers, lancedb) and their dependencies (including PyTorch, ~2GB) are installed automatically into an isolated virtual environment on first use.