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Search and analyze 1C metadata and BSL code with natural language.
This MCP tool is described as a local analysis server for 1C metadata and BSL code using a SQLite backend, with no declared secrets or remote endpoints. The main concerns are local code/process execution and access to project data, while supply-chain confidence is limited by low adoption and unclear maintenance despite being open source.
The materials and objective checks indicate no required secrets or environment variables. No API tokens, account passwords, or other sensitive credentials are declared, so credential exposure appears limited.
No remote endpoints are declared, and the description only mentions local search and analysis via a SQLite backend. Based on the provided materials, there is no factual indication of user data being sent to third-party services.
The objective checks mark this tool as having code-execution capability, which for an MCP tool implies it may start local processes or run server-side code on the host. This is a common inherent capability for such tools, and the materials do not show requests for privileges beyond its stated purpose, but it should still be run in a constrained environment.
The tool claims to search and analyze 1C metadata, configuration structure, and BSL code using a SQLite backend, so it likely needs to read local project code/metadata and may write a local index database. The materials do not show explicit overreach into unrelated resources, but its access to local project contents should be reviewed.
There is a public GitHub repository, making the source theoretically auditable, which is a positive risk-reducing factor. However, it comes from a third-party registry, has no declared license, shows 0 stars, and has unknown maintenance status, so supply-chain confidence is limited and the repo/dependencies should be verified before use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "onec-meta-mcp" yet — see the docs or source repo.
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A findings report with risk levels and recommended fixes.
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