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Compare MCP servers

Compare scores, permissions, risks, and fit in one decision-focused table.

DimensionRLM Tools BSL MCP ServerToken-efficient analysis MCP server for 1C (BSL) codebasesNPM Sentinel MCP ServerAI-powered NPM package analysis MCP server
FMRS60 / 100 · C79 / 100 · B
Reliability10 / 2012 / 20
Security and permissions9 / 2016 / 20
Maintenance12 / 2018 / 20
Documentation16 / 2018 / 20
Setup experience13 / 2015 / 20
Best for
  • 1C:Enterprise projects with CF, EDT, or MDO source formats
  • Large configurations with 20K+ files that cannot fit into a single session context
  • Deterministic codebase questions, e.g. finding an HTTP service and its methods or how a document posting routine works
  • Teams that want to save context and tokens and avoid maintaining RAG infrastructure
  • Developers auditing NPM dependencies within AI workflows
  • Teams performing supply chain security assessments
  • Users of Claude Desktop, Cursor, or VS Code
Not for
  • Scenarios needing a complete dependency graph of all configuration objects (requires a pre-indexed RAG/graph MCP)
  • Semantic/embedding search over object descriptions
  • Vague, non-deterministic questions like 'how does budgeting work in ERP' or 'how do cellular warehouses work in UT', which need full RAG or a strong model to turn them into strict queries
  • Treating it as an official 1C product: it is not maintained by 1C
  • Projects outside the NPM ecosystem (e.g., pure Python/Go)
  • Environments without network access to deps.dev, OSV.dev, and the npm registry
  • Scenarios requiring maintenance by an official upstream vendor
Required permissions
  • Read 1C source directories (BSL files, XML/MDO descriptions, metadata)
  • Write and manage SQLite index files on the host
  • Read and modify the projects.json registry next to the server directory (mutating operations require a password)
  • Execute agent-submitted Python scripts inside a sandbox
  • Optional access to an LLM endpoint (OpenAI-compatible OpenRouter, Ollama, vLLM, or the Anthropic API), only when llm_query is configured
  • Optional use of Git on the host to read the source repository (enables git_search)
  • Network access to the NPM registry and external services (deps.dev, OSV.dev, OpenSSF, npms.io, GitHub)
  • Read access to workspace lockfiles (pnpm-lock.yaml, package-lock., yarn.lock) for cache invalidation
Risks and side effects
  • Agent-submitted Python scripts are executed in a sandbox whose real strength determines the host's exposure surface
  • A .git directory in the sources automatically enables git grep full-text search over all files, which may surface 'raw' XML/MDO and text content developers did not expect to expose
  • When llm_query is configured, script content or code fragments may be sent to an external LLM endpoint
  • The registry password protects mutating operations to stop the AI acting without confirmation, but it is not strong encryption or isolation
  • Indexing is slow on HDDs or network shares, and indexes persist code content read from disk
  • Third-party READMEs and changelogs are untrusted external content; they are wrapped in tags with _meta flags but should still be handled cautiously
  • Depends on availability and accuracy of external services
  • Batch requests are capped at 25 packages to prevent registry enumeration; larger sets require batching
  • Third-party open source project, not officially maintained by NPM or Anthropic
Supported clientsClaude Code, Kilo Code, Roo Code, CursorClaude Desktop, VS Code, Cursor
Tools619