enola

A deterministic map of your codebase for AI coding agents — the real architecture, extracted from your source, not guessed.

enola is a local Model Context Protocol (MCP) server. Point it at one or more repositories and it builds a precise graph of your code's architecture — modules, types, routes, dependencies, and how they all connect — then exposes tools your AI agent can use to read, traverse, and reason about that structure. So before your agent writes a line of code, it already knows the shape of the thing it's editing.

Why enola

AI coding agents are powerful, but they're non-deterministic. On every task they re-discover your codebase from scratch — grepping, opening files, inferring how things fit together — and they get it subtly wrong often enough to matter. That guessing costs you time (wrong turns, re-prompts) and tokens (re-reading the same files, every session).

enola removes the guessing from the part that should never be guessed: the structure.

It gives your agent a deterministic architectural ground truth — a graph of your code's real types and relationships, built by parsers and graph algorithms, not by a language model. Run it twice on the same commit and you get the same answer, every time. The agent starts from facts instead of assumptions.

The result is the difference between vibe coding — prompt, hope, fix — and AI-augmented engineering: fewer wrong turns, fewer tokens burned, and work you can reproduce. enola adds determinism where AI lacks it, and your agent spends its intelligence on the actual problem instead of re-learning your repo.

enola is a first step, not a replacement. It runs before your agent explores, so it knows where to look and what connects to what. It doesn't replace grep, file reading, or code search — it makes them precise.

Related MCP server: ChunkHound

What it is

Under the hood, enola models your codebase as a graph of architectural types — which we call kinds — and the relations between them. That's the whole concept: not a magic "knowledge graph," just a deeply technical, structural map of what your code actually contains.

The kinds (the nodes):

• module — a package or directory

• symbol — a function, method, struct, interface, type, class, or constant

• route — an HTTP/API endpoint

• storage — a database table or data store

• dependency — an import relationship

• service — a whole repository (used when you analyze several at once)

The relations (the edges) connect them: declares, imports, calls, implements, depends_on, and more. On top of this graph, enola builds a small set of tools your agent can call to answer real structural questions with exact answers.

For the full mental model and internals, see ARCHITECTURE.md.

Who it's for

• Anyone pairing with an AI coding agent — Claude Code, Cursor, Copilot, Opencode, or any MCP-compatible tool.

• Teams working across multiple repos — a backend, a web frontend, a mobile app. enola links them into one cross-repo graph so an agent can follow a call from the web client all the way into the service that answers it.

• Anyone about to refactor — and wanting to know the blast radius before touching code.

The tools (and how they work together)

The workflow is simple: generate the map once, then ask. After that, your agent has seven tools on top of the graph:

impact_analysis is the one to know. Before a refactor, it computes the full set of code that transitively depends on what you're about to change — grouped by how many hops away it is, and aware of cross-repo dependencies. Instead of your agent guessing what a change might affect (and missing things), it gets the exact dependent set. That's determinism turned into a concrete payoff: safer changes, planned in the right order, the first time.

See ARCHITECTURE.md for every tool's full parameters.

See it in action

The examples below ask different models to explain the authentication and authorization flow across three repositories — a web UI client, a backend, and a custom auth provider — using the enola snapshot as context.

Claude Code

Opencode

Quick start

Install

Grab a prebuilt binary — no Go toolchain or C compiler required:

curl -fsSL https://raw.githubusercontent.com/enola-labs/enola/main/install.sh | sh

This installs enola to ~/.local/bin. If that's not on your PATH, add it:

export PATH="$HOME/.local/bin:$PATH"

Binaries are published for Linux, macOS (amd64/arm64), and Windows (amd64). You can also download a specific build from the Releases page, or build from source.

Connect it to your agent

Claude Code — register enola as an MCP server with one command. This assumes the enola binary is on your PATH (the install script above puts it in ~/.local/bin):

claude mcp add enola enola /path/to/enola/mcp-arch.yaml

The shape is claude mcp add <name> <command> [args…]: the first enola names the server, the second is the binary, and the trailing path is the config it reads. That config's repo: is only the default repository — you can still snapshot any repo by passing repo_path to generate_snapshot. Verify it registered with claude mcp list, then start Claude Code and ask it to generate a snapshot.

Cursor / other MCP clients — add enola to your client's MCP configuration. For example, in Cursor's mcp.json:

{
  "mcpServers": {
    "enola": {
      "command": "enola",
      "args": ["/path/to/enola/mcp-arch.yaml"]
    }
  }
}

Use it

Open a project and ask your agent to map it:

"Generate an architectural snapshot of /path/to/my/project"

That's it. The snapshot takes milliseconds even on large repos, and your agent now has the tools above plus a ready-to-read summary at .enola/llm_context.md. From here, just ask your questions naturally:

"I just joined this project — based on the snapshot, give me a tour: the main modules, how they relate, and where to start reading."

"I need to add an API endpoint for user preferences. Which packages should I touch, and in what order?"

"Are there cyclic dependencies or layer violations I should know about before refactoring?"

"What would break if I refactor internal/server? Show me the impact analysis."

Working across several repos? Generate the first, then add the rest with append mode — enola links them into one cross-repo graph:

"Generate a snapshot of /path/to/go-service with append mode"

"If I change the auth service, which other services are impacted?"

Regenerate after major changes so the map stays current. enola only re-parses files whose contents changed, so refreshes are fast.

Supported languages

Framework- and platform-specific detection for each language is described in ARCHITECTURE.md → Supported languages.

Python and Ruby are parsed with tree-sitter and contribute call and dependency edges to the graph, so traverse, find_path, and impact_analysis reach into them — not just modules and routes.

Build from source

Prerequisites: Go 1.25+ and a C compiler (for the tree-sitter bindings).

go build -o enola ./cmd/enola   # or: go install ./cmd/enola

To run a one-shot snapshot without starting the MCP server:

enola --generate [config_path]   # config_path defaults to mcp-arch.yaml

Artifacts are written to the configured output.dir (default .enola/). Configuration is documented in ARCHITECTURE.md → Configuration.

Explain a repository at a glance

enola --explain [repo_path] is a one-shot mode that generates a snapshot, computes statistics over the fact graph, and prints a human-readable report to stdout — no MCP server started, no artifacts written to .enola/.

When to use it:

• New contributor getting a first orientation — module count, architecture pattern, hottest packages.

• Pre-refactor sanity check — cycles, layer violations, blast radius of top modules.

• Quick audit without spinning up an AI agent.

# Use the config in the current directory (mcp-arch.yaml)
enola --explain

# Analyze a specific repository path
enola --explain /path/to/repo

The report covers seven sections:

• Overview — path, analysis time, active languages, total fact count

• Architectural kinds — counts of modules, symbols, routes, storage, dependencies, services

• Symbol breakdown — functions, methods, structs, interfaces, and other kinds

• API & data surface — route count broken down by HTTP method, plus storage count

• Dependencies — external, internal, and stdlib import counts

• Architecture — detected pattern with confidence, cyclic dependencies, layer violations, cross-repo edges

• Impact analysis (hotspots) — top modules ranked by fan-in + fan-out coupling, with criticality tier and blast radius

No artifacts are written; .enola/ is not touched. For a persistent snapshot with agent-readable output, use --generate or the MCP server.

For interactive per-module blast-radius queries with configurable depth, see the impact_analysis tool reference in ARCHITECTURE.md → The tools.

Learn more

• ARCHITECTURE.md — the concept, the fact model, the pipeline, and the full tool reference.

• examples/ — ready-made per-language and multi-repo configs.

License

Apache License 2.0 — see LICENSE.

Acknowledgements

enola bundles third-party components under their own licenses; see NOTICE. Swift parsing uses the tree-sitter-swift grammar by Alex Pinkus (MIT), vendored under internal/extractors/swiftextractor/grammar/.