# 1C Element Docs

> Use this tool when you need to efficiently search and access documentation for the 1С:Элемент Russian enterprise software platform. It solves problems of information discovery and retrieval by indexing and exposing documentation through a searchable interface, accepting queries and returning relevant results from HTML, Markdown, and PDF files. Ideal for users seeking specific information within large document collections, particularly those working with 1С:Элемент software.

Canonical page: https://skillsregistry.net/skills/korolevpavel-1c-element-docs  
JSON: https://api.skillsregistry.net/v1/skills/korolevpavel-1c-element-docs

## Description

1C Element Docs indexes documentation for the 1С:Элемент Russian enterprise software platform and exposes it through an MCP interface. It supports full-text SQLite FTS5 search across HTML, Markdown, and PDF files covering the main documentation, object model/standard library reference, and user-uploaded documents, with incremental reindexing.

## Trust

- **Trust score (0–1):** 0.84
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/korolevpavel-1c-element-docs)
- **Repository:** <https://github.com/korolevpavel/xbsl-mcp-docs>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "korolevpavel-1c-element-docs"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/korolevpavel-1c-element-docs` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/korolevpavel-1c-element-docs/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt