Regsorm

Regsorm / bsl-context

Public

MCP-сервер контекста платформы 1С (типы, методы, перечисления + валидация BSL-выражений)

12
3
100% credibility
Found May 29, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Rust
AI Summary

bsl-context is a smart assistant that helps developers working with the 1C business application platform. It reads the built-in platform documentation and lets you search for types, methods, properties, and constructors. You can also paste your code and have it checked against the platform rules — catching wrong enumeration values, incorrect argument counts, unknown type members, and more. It supports different validation depths so you can choose between quick checks and deeper analysis.

How It Works

1
💡 You need help with 1C code

You are working on a business application written in the 1C programming language and want to make sure your code uses the right methods and types for the platform.

2
🔍 You find a smart assistant for your code

You discover bsl-context, a tool that acts like a knowledgeable colleague who knows every method, type, and rule of the 1C platform by heart.

3
⚙️ You connect the assistant to your work

You point the tool to your installed 1C platform files, and it quietly reads through all the built-in documentation to build its knowledge base.

4
📋 You ask about any type or method

You can ask the assistant about any platform type, method, property, or constructor — it instantly shows you the full details, parameters, and return types in a clean format.

5
You validate your code
✅
Quick check

At the basic level, it catches obvious mistakes like wrong enumeration values or incorrect argument counts in function calls.

🧠
Deep check

At the deeper level, it also traces how variables get their types through your code and follows chains of method calls to catch more subtle issues.

🎉 Your code is validated and ready

You get a clear list of any problems found, with confidence levels and helpful suggestions — so you can fix issues with confidence and ship cleaner code.

Sign up to see the full architecture

4 more

Sign Up Free

Star Growth

See how this repo grew from 13 to 12 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is bsl-context?

bsl-context is an MCP server that gives AI coding assistants real knowledge about the 1C:Enterprise platform API. It parses the platform's built-in help database (shcntx_ru.hbk) to index every type, method, property, constructor, and system enumeration value. Beyond just lookup, it validates BSL code snippets against this index, catching mistakes like nonexistent enum values, wrong argument counts on global functions, or unknown type constructors. Built in Rust with tree-sitter for parsing, it exposes nine MCP tools over HTTP: fuzzy search, type inspection, member lookup, and expression validation with configurable confidence levels.

Why is it gaining traction?

The core value is solving a blind spot in AI-assisted 1C development. Language models handle BSL syntax fine but hallucinate platform APIs constantly. This tool bridges that gap by checking generated code against the actual platform version installed on the machine. The validation system is thoughtfully designed with three depth levels and two profiles (full/strict), letting you tune noise versus coverage depending on your model. The HTTP transport makes it drop-in compatible with GitHub Copilot, VS Code, and other MCP clients without extra configuration.

Who should use this?

1C developers using AI assistants (Copilot, Cursor, or any MCP-compatible tool) who want reliable platform-aware code generation. It's especially valuable for teams validating AI-written migration scripts or refactoring code where method signatures and enum values matter. If you write 1C professionally and have been burned by AI suggestions that look syntactically correct but call nonexistent methods, this tool directly addresses that pain.

Verdict

This is a niche but genuinely useful tool for a specific workflow. The 1.0% credibility score and 12 stars reflect early-stage development with limited community validation. Documentation exists in both Russian and English, and the architecture is well-structured, but test coverage and real-world battle-testing remain unknown. Worth evaluating if you fit the use case; treat it as promising rather than production-proven.

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.