v0.2.0-alpha

Initial public release for closed testing.

Tostr is designed to solve LLM context bloat by mapping codebases into a combined syntax tree and dependency multigraph, allowing AI agents to surgically retrieve context rather than grepping massive files.

🚀 Core Features in v0.1.0

• Pre-compiled AST Caching: Scrapes your OOP project on initialization into a lightweight, local SQLite .db cache.
• Semantic Dependency Graph: Resolves dependencies semantically, building a graph that allows agents to traverse inbound (<), outbound (>), and heuristic (~) method calls.
• Dual Interface (CLI & MCP): * Full CLI access for human developers (tostr skeleton, tostr inspect).
  • Built-in Model Context Protocol (MCP) server for direct integration with agentic tools (like Claude Desktop).
• Automatic Incremental Diffs: The MCP daemon watches your file system and re-scrapes only the specific AST subtrees that change on save.
• Token Circuit Breakers: Hard --max-lines limits and truncation warnings protect agent context windows from accidental bloat.

⚠️ Known Limitations (Alpha)

As this is an initial 0.1.0 release, there are a few limitations we are actively working on:

• Currently functional only for Java.
• Semantic search currently only supports onnx-compiled MiniLM-L6-v2
• Description generation uses the Gemini API, requiring a google developer gemini API key saved as an environment variable GEMINI_API_KEY

📦 Installation

The recommended way to install Tostr globally without polluting your system Python environment is via pipx:

pipx install tostr