SkillFoxx

1C Code Search MCP

A local MCP server for semantic search over an exported 1C configuration, with a choice of embedding model and vector database

  • 11 ★ on the repository
  • Last commit
  • MIT
MCP server

Extended access

Why this level

  • Runs a Python server with a web UI and downloads an embedding model from HuggingFace on first start
  • The SSE endpoint on port 8000 has no auth if exposed to the network
All reasons and checks
Russian stack

rsyuzyov/bsl-mcpAuthor:rsyuzyov

Install

Manual install

python install.py

Install dependencies after cloning the repository.

Access and checks

How we review

Extended accessThe tool runs code or makes network calls and only reads external data.

  • Runs a Python server with a web UI and downloads an embedding model from HuggingFace on first start
  • The SSE endpoint on port 8000 has no auth if exposed to the network

This is third-party code. Review the repository files before installing.

What it does

The server indexes an exported XML 1C configuration and lets you search code by meaning rather than exact text. It supports several infobases at once, each with its own embedding model and vector index. The default model is rubert-tiny2, but any HuggingFace or ONNX-compatible model can be used, and Qdrant, LanceDB or ChromaDB are available as the search backend. A web interface handles configuring the bases and indexing parameters, and the MCP itself is served over SSE, so it can be reached over the network, not just locally.

Who it is for. For 1C developers with large configurations who need an agent to find relevant code by meaning, not just exact text matches.

Good fit when

  • The configuration is large and plain grep struggles to find the right code for an agent
  • You need to keep several infobases with separate indexes
  • You want a choice of local vector databases without an external cloud service

Not a fit when

  • The configuration is small and plain file search is enough
  • There is no file export of the configuration, only a file-based infobase
  • You want a ready cloud service without local setup and indexing

Example request

Find the code in the Trade Management base that handles reserving stock in a warehouse

MCP

Transport
sse
Authentication
not required

Limitations

The first run downloads and converts an embedding model to ONNX, which needs access to HuggingFace. Indexing is tuned through in-code constants (BATCH_SIZE, EMBED_BATCH_SIZE, CHUNK_SIZE), and changing the chunk size requires a full reindex. The project is small, and the README does not describe behavior under heavy code volumes or load limits. MIT license.

How to disable. Stop the code-search.py process and remove the server from your MCP client configuration.

FAQ

Can several configurations be connected at once?

Yes, config.yaml supports a list of infobases, each with its own source folder, index and embedding model.

Is Qdrant required?

No, lancedb or chromadb can be chosen instead through the vector_db field.

Discussion

No questions or reviews yet.

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README in short

The README describes a local Python MCP server for semantic search over 1C code, with a web UI on port 8000. It lists supported embedding models and vector engines, shows a config.yaml example with several bases, explains internal indexing performance constants, and gives ready connection snippets for Claude Desktop, Cursor and Kiro over SSE. MIT license.

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