Tostr

Frontloading Agentic AI Code Context

Tostr is a CLI and MCP agent context engine which greatly reduces token costs and context bloat for agentic LLM coding assistants by pre-computing an llm-described AST with outputs in the highly-efficient .tost format.

Features

🌴 Pre-computed Abstract Syntax Tree

Tostr scrapes your project when you parse it, building a comprehensive Abstract Syntax Tree IR (Intermediate Representation) of the entire OOP code structure and stores it in a local SQLite database.

⛓️ Heuristic Dependency Graph Resolution

Tostr resolves dependencies between structures in your code, building a dependency graph to allow agents to traverse inbound or outbound method calls efficiently.

🔌 MCP and CLI interfaces

Tostr has both a CLI and MCP interface, allowing llms to boot up the mcp server for larger development sessions, while allowing agents or human developers to utilize the CLI for individual actions or quick, manual AST traversals.

⛓️‍💥 Automatic Incremental Change Diffs

While the MCP server is running, Tostr identifies the subtree of the AST which was updated on file save, add, or delete, then re-scrapes and re-describes exactly the section that was updated, ensuring that the AST is instantly up-to-date during development.

🗄️ Lightweight SQLite Cache

The AST IR and Dependency Graph is cached to an on-drive SQLite .db file to vastly increase efficiency of agent AST traversals, as well as allow the AST to be directly queried via sql commands.

💭 Semantic Vector Embedding

Using local ONNX (Open Neural Network Exchange) weights from the all-MiniLM-L6-v2 embedding model, Tostr embeds the descriptions of each struct, allowing for far more accurate semantic search of specific structs than the traditional line blocking approach.

🌍 Language Support Matrix

Tostr is designed to map the macro-architecture of your codebase. Most supported languages receive high-density Structural AST Skeletons and AI Semantic Descriptions, while multi-hop cross-file dependency resolution is currently optimized specifically for deep backend monoliths (Java). Some formats (e.g. HTML) have no extractable sub-structures and are indexed at the file level — a single described, searchable node per file rather than a skeleton of classes and functions.

| Language | Extensions | Structural AST Parsing | AI Semantic Descriptions | Cross-File Dependency Graph | | :--- | :--- | :---: | :---: | :---: | | ☕ Java | .java | ✅ | ✅ | ✅ | | 🐍 Python | .py | ✅ | ✅ | ✅ | | 🌐 HTML | .html, .htm | 📄 File-level | ✅ | — | | 🔷 TypeScript / JavaScript | .ts, .tsx, .js, .jsx, .mjs, .cjs | 🚧 Coming Soon | 🚧 Coming Soon | 🚧 Coming Soon | | 🎯 C# | .cs | 🚧 Coming Soon | 🚧 Coming Soon | 🚧 Coming Soon | | 🐹 Go | .go | 🚧 Coming Soon | 🚧 Coming Soon | 🚧 Coming Soon |

Tostr is still in active development, so this list will quickly expand and grow with more language support. If you want to add support for your favorite language, you can also take a look at CONTRIBUTING.md to help us out!

> Note for AI Agents: For languages where dependency tracking is marked "Coming Soon" (or "—" for file-level formats), the MCP server will cleanly omit the dependency fields. Agents should rely on tostr skeleton and semantic search to navigate these codebases.

60 Second Quickstart

Zero config required. Paste these into a terminal — shown for Claude Code; [other agents below](#connecting-the-mcp-to-your-agent).

pipx install tostr                          # Installing the CLI onto your PATH

tostr add-agent claude --global             # Tells the agent to prefer Tostr for navigation
claude mcp add tostr -- tostr start-mcp     # connect the MCP server

cd path/to/project                          # Navigate to your project repository root

tostr parse . --no-llm                      # build the local AST cache (no API key needed)

tostr status .                              # confirm the parse succeeded

# now explore from the CLI — or just ask your agent:
tostr skeleton . --files-only
tostr search "authentication" --filter class

For richer descriptions and sharper semantic search, add a GEMINI_API_KEY and drop --no-llm — see [Getting Started](#getting-started).

Getting Started

Prerequisites

• Requires Python 3.12+
• Requires a Google Gemini API Key for descriptions

Installation

Tostr is available on PyPI and can be installed via pip or pipx. Due to its dependencies, it is highly recommended to install it using pipx to keep it in an isolated environment:

pipx install tostr

> If you don't have pipx, you can download it easily via brew install pipx on mac or python -m pip install --user pipx; python -m pipx ensurepath on windows.

Alternatively, you can install it via standard pip:

pip install tostr

or with uv for faster installation:

uv tool install tostr

If you wish to utilize tostr's struct descriptions, you will also need to configure a Google Gemini API key and save it as an environment variable. This is optional, as the embedding will just fall back to using code bodies and UIDs when a description isnt generated.

To create a new API key:

1. Go to the Google AI Studio and log in with your google email.
2. Once logged in, in the bottom left click the Get API Key button.
3. In the top right, click Create API Key. You may need to create a new project before making an API key. You can just name it tostr
4. Name the key something like Tostr API Key. This name does not matter for the rest of the steps.
5. Click the button next to the new key that says copy API key to copy the string to your clipboard. It should be a long random string with 39 characters.
6. Save this key as an environment variable called GEMINI_API_KEY on your computer.

DISCLAIMER: While tostr does not use any gemini features that require a payment method, you will very quickly hit rate limits on a free tier.

I would suggest setting up a payment method in the Google AI Studio so you can get the limits of the Tier 1 payment tier. Once set up, using tostr should cost only a couple cents per project if anything, since it uses the Gemini Flash-Lite model for all its description generation. You can very easily set a spend limit in Google's UI if you like by going to the Spend tab after creating your key.

Installing Environment Variables on Mac:

To expose your API key to tostr in a specific terminal session, run this command:

export GEMINI_API_KEY=[your api key]

> This will only save the key in the current session. To save the key permanently and system-wide, follow the instructions here

Installing Environment Variables on Windows:

In order to save environment variables on Windows, follow these steps.

1. Press the windows key and type environment variables
2. Click Edit the system environment variables to open the System Properties window.
3. Decide where to store your variable.

• User variables: Only accessible by your specific Windows account.
• System variables: Accessible by all users on the computer (requires Administrator privileges).

1. Click New... under the chosen section
2. Enter GEMINI_API_KEY in the name, and paste your API key from the Google AI Studio
3. Click OK on all open windows to save the settings.

> Note: You must restart any open command prompts for them to recognize the new variable.

Connecting the MCP to your agent

Tostr can be used as an MCP (Model Context Protocol) server, allowing your favorite AI coding agent to interact directly with your project's AST and dependency graph.

Generic Configuration

Most MCP-compatible agents use a JSON configuration file. You can generally add Tostr by adding the following to your mcpServers configuration:

{
  "mcpServers": {
    "tostr": {
      "command": "tostr",
      "args": ["start-mcp"],
      "env": {
        "GEMINI_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

> Note: If tostr is not in your system PATH, you may need to provide the absolute path to the executable (e.g., /Users/YOUR_NAME/.local/bin/tostr). You can find this path by running which tostr on macOS/Linux or where tostr on Windows.

Claude Code: one-line install

If you're on Claude Code, skip the JSON entirely — paste this into your terminal and you're connected:

claude mcp add tostr --env GEMINI_API_KEY=YOUR_API_KEY_HERE -- tostr start-mcp

or even simpler, if you configure your projects to use no-llm:

claude mcp add tostr -- tostr start-mcp

Claude Code's CLI writes the config for you, no file editing required.

Popular Agents with MCP Support

Below are instructions and links for setting up MCP servers in common AI coding environments:

• Claude Desktop: Official Setup Guide
• Cursor: Cursor MCP Documentation
• Cline (VS Code): Cline Documentation
• Codex: Codex Documentation

tostr add-agent — teach your agent to prefer Tostr

Connecting the MCP server gives your agent the Tostr tools; it doesn't tell it when to reach for them over raw read/grep. add-agent installs that guidance into your agent's instructions file (CLAUDE.md, .clinerules, etc.) so the agent defaults to skeleton/search/inspect for code navigation.

tostr add-agent claude        # install into ./CLAUDE.md
tostr add-agent cursor        # install into ./.cursor/rules/tostr.mdc
tostr add-agent all           # install into every supported agent
tostr add-agent claude -g     # install into your global ~/.claude/CLAUDE.md instead
tostr add-agent --list        # show supported agents and their config paths

Supported agents: claude, cline, copilot, codex, cursor.

It is safe to re-run and non-destructive: the guidance is written between managed markers, so installing into a file that already has your own content just upserts that block and leaves everything else untouched (re-running an unchanged install is a no-op). Agents whose config is a dedicated file (Cursor's tostr.mdc) are written whole.

To uninstall, use tostr remove-agent — it strips the managed block (deleting the file only if it becomes empty) or removes the dedicated file:

tostr remove-agent claude
tostr remove-agent all

Available Flags (add-agent):

• --global, -g: Install into the agent's global config instead of the current project. Only some agents have a global location (e.g. claude, codex). Default is False
• --force, -f: Overwrite a dedicated agent file (e.g. Cursor's tostr.mdc) even if it isn't Tostr-managed. Default is False
• --list, -l: List supported agents and where they install, then exit.
• --debug, --no-debug / -d, -nd: Enable debug logging. Default is False

Setting up Tostr

Tostr separates authoring configuration from building the cache:

• tostr.toml (project root, committed) holds your project settings, and .tostrignore (project root, committed) holds your ignore rules. These are yours — you edit them and they survive any cache wipe.
• .tostr/ (hidden, gitignored) is generated and disposable. tostr parse rebuilds it from scratch; tostr clean removes it.
• tostr.lock.json (project root, generated-but-committed) is an optional third category — the AST equivalent of a package-lock.json. You produce it with tostr export, commit it, and it lets a teammate's first tostr parse reuse your LLM-generated descriptions instead of paying to regenerate them. See [tostr export](#tostr-export) below.

tostr init — scaffold project files (optional)

tostr init .

This lays down the editable project files so you have something concrete to configure:

• creates tostr.toml at the root, pre-filled with documented defaults;
• creates .tostrignore at the root, materialized from the default templates (environment files, build artifacts, node_modules/, venv/, target/, etc.) for your language(s);
• creates the empty .tostr/ directory and adds it to your .gitignore.

init does not parse and never needs an API key. It is also idempotent: it never overwrites an existing tostr.toml or .tostrignore (pass --force to overwrite). init is entirely optional — if you're happy with the defaults you can skip straight to tostr parse, which falls back to the same built-in defaults without writing any files.

Available Flags:

• --force, -f: Overwrite existing authored files (tostr.toml, .tostrignore) instead of leaving them untouched. Default is False
• --debug, --no-debug / -d, -nd: Enable debug logging. Default is False

tostr parse — build the database

tostr parse .

This does the actual work: it parses the AST, resolves dependencies, generates descriptions, embeds them, and writes .tostr/cache.db. It reads your configuration (or the built-in defaults) and authors nothing. Run it whenever you want to (re)build the cache.

The --language flag overrides the configured language for this run only. If omitted, parse uses the language from tostr.toml (defaulting to auto, which parses every file with a supported extension and treats them all as valid dependency nodes). Choosing a specific language parses only that extension.

> Tostr currently supports .java, .py, and .html/.htm, so the options for --language are java, python, and html.

If you are running tostr on a project that already has an existing database but you want to reparse from the start, use the --no-cache flag.

If a committed tostr.lock.json is present (see [tostr export](#tostr-export)), parse automatically seeds descriptions from it: for any struct whose code is unchanged since the lockfile was written (matched on a content hash), it reuses the committed description instead of calling the LLM, then re-embeds locally for free. This is what lets a teammate run git clone && tostr parse and get the shared descriptions without an API key for the unchanged majority of the code — only genuinely new or changed code hits the LLM.

The --llm flag selects which LLM strategy generates descriptions for this run only. Resolution is --llm > the strategy configured in tostr.toml > the gemini default. Gemini is the only built-in default and reads GEMINI_API_KEY from the environment; if that key is missing and no other strategy is configured, parse stops and tells you to configure a binding or set the key (use --no-llm to skip descriptions entirely). Pass --llm ollama to describe against a local Ollama model instead, or --llm none to disable description generation (equivalent to --no-llm).

> Configuring a strategy's details (model name, host, etc.) is done per-strategy in tostr.toml; see the strategy configuration docs for the available keys.

Available Flags:

• --use-cache, --no-cache: Load the existing cache if it exists (use --no-cache to force a full reparse from scratch). Default is True
• --language, -l: Override the configured language for this run (e.g., java, python). Omit to use tostr.toml (defaults to auto).
• --llm: Override the LLM strategy for this run (gemini, ollama, or none to disable). Trumps tostr.toml. Omit to use the configured strategy (default gemini).
• --no-llm: Skip LLM-generated descriptions (no API key required); equivalent to --llm none. Embeddings still run, falling back to code context. Default is False
• --debug, --no-debug / -d, -nd: Enable debug loggi

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: rubyTanuki
• Source: rubyTanuki/tostr
• License: MIT

Install and usage instructions live in the source repository linked above.