Smaturus /

1c-mcp-rag-search

Search for 1C (BSL code + configuration metadata) exposed to LLM agents via MCP: local e5/Chroma retrieval, global BM25 + RRF hybrid…

A new way for AI to search for data
View on GitHub
0GitHub stars

Python

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit 99469ffc18.

Selected work

Sergey Matyukhin
@Smaturus

6 files, 548 lines in data, tests, src/mcp_1c_homework.

Licence

The repository

MIT

Checked by a person at the pinned commit.

The XERJ corpus

MIT, with credit

The author agreed by email on 2026-10-22. Same terms as the repository. Answers name the repo, the commit and the file:line.

in licence reviewnot in the hub yetsuggested use: adapt with attribution

1c1c-enterprisebm25chromadbevaluationmcpmcp-serverpython

Example questions

The prompt

Paste it into your AI agent. It clones Smaturus/1c-mcp-rag-search at the pinned commit and indexes it on your machine.

Search the GitHub repository Smaturus/1c-mcp-rag-search with XERJ (https://hub.xerj.org/corpus/smaturus--1c-mcp-rag-search).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/Smaturus/1c-mcp-rag-search 1c-mcp-rag-search && git -C 1c-mcp-rag-search checkout 99469ffc18eb5a43e5b7c18755c7464b97f705e2
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./1c-mcp-rag-search --url http://127.0.0.1:9200 --prefix 1c-mcp-rag-search --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

Related

Sergey MatyukhinAI, prompts and agents corporadeepevalfastmcpgriptapeMaz2580/geosparkpandich93/youtube-niche-finderOpenMachineLabs/openmachine

Selected work and consent: the XERJ registry.