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Connect AI agents to 1C:Enterprise data through MCP and REST APIs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "1C MCP Toolkit" yet — see the docs or source repo.
Connect to the 1C MCP Toolkit, read sales orders from the last 30 days from the 1C:Enterprise database, summarize revenue by region, and output a table with key findings.
A revenue-by-region table with brief trend analysis and anomaly highlights.
Use the 1C MCP Toolkit to create a new customer record in 1C:Enterprise with company name, contact person, phone, and email; check for duplicates before creating it.
A duplicate-check result and either the created customer record ID or the failure reason.
Recommend a 1C MCP Toolkit deployment approach for my environment: the 1C server can expose its built-in HTTP service, but there is no Python runtime. Compare the built-in HTTP mode and Python proxy mode, including use cases, pros and cons, and setup steps.
A deployment recommendation for the environment, plus a comparison and implementation checklist for both modes.
Turn existing APIs and databases into MCP tools for direct AI use.
Use natural language to query 1C.ai, analyze code, and search docs.
Run SQL, APIs, and sandboxed Python for multi-step research and data tasks.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Connect to the mcp API via MCP to extend AI tool capabilities.
Connect to and operate MCP servers from the command line.