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DimensionNetwork Sketcher (Local MCP)Local-first, AI-native MCP server for network design automationMartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS59 / 100 · C79 / 100 · B
Reliability10 / 2012 / 20
Security and permissions9 / 2018 / 20
Maintenance14 / 2016 / 20
Documentation15 / 2018 / 20
Setup experience11 / 2015 / 20
Best for
  • Teams using Cursor or Claude Code who want to drive network design directly through tool calls
  • Local single-user workflows that keep master files on the machine with no external communication
  • Network engineers who need Cisco-style L1/L2/L3 topology diagrams (SVG / PowerPoint), device tables, and AI Context files
  • Users willing to use a model with strong multi-step tool-calling ability (Claude Opus 4.7 or later recommended)
  • Engineering teams scaling AI coding agents while controlling spend
  • Platform teams that need auditable, rollback-aware agent execution records
  • Organizations using multiple agents (Claude, Codex, Gemini) under one governed flow
Not for
  • Deployments needing HTTP/SSE transport or multi-user concurrency (stdio, single-user only)
  • Scenarios where the LLM client must directly view PPTX / SVG binary output (the user should open the generated SVG)
  • Environments that cannot run Python or install local dependencies
  • Network designs requiring IPv6 (IPv6 is not supported)
  • Needs for master file creation from PowerPoint sketches, GUI add/delete of VPNs, or Flow management (not offered in this edition)
  • Developers who just want a simple code-completion helper without run governance
  • Teams unwilling to accept local JSONL run records with HMAC signing
  • Environments without a supported adapter (Claude/Codex/Gemini CLIs, direct provider, or verifier-only)
Required permissions
  • Read and write workspace directories under the user's home directory (set_workspace validates the path is under the home directory and confirms it is writable)
  • Create and read .nsm master files in the active workspace
  • Read a .xlsx file at a given path for import_master
  • Write generated SVG / PPTX diagrams, .xlsx files, and HTML device tables into the active workspace
  • Launch a local Python subprocess to execute Network Sketcher CLI commands
  • Read/write access to the configured repo root (writable scope restrictable via --allow-path/--deny-path)
  • Ability to invoke the chosen agent CLI (Claude/Codex/Gemini, etc.) and its underlying model account
  • Local disk writes for run records, receipts, and share outputs (share/run-receipt., etc.)
  • Execution of user-configured verifier commands (e.g. npm test)
Risks and side effects
  • run_commands batches LLM-generated CLI commands into a local subprocess, so wrong or unintended commands can modify the .nsm master (back up first or experiment with an empty master)
  • import_master and export_master_xlsx read and write files in the workspace; path mistakes or overwrites can affect existing files
  • Imported converter output is only a starting draft and may have data-quality issues requiring review
  • Diagram generation for large networks may take a long time
  • Weaker or older models may struggle to follow the layout / workflow rules embedded in the server instructions and AI Context (e.g. RULE 0 / 0.5 layout, RULE 3.5 multi-transport WAN waypoint design, the mandatory get_workspace_info to get_ai_context bootstrap)
  • Verifier commands are executed; poorly chosen verifier commands carry security risk despite preflight policy checks
  • Cost and token figures may be estimates (labeled with provenance) and should not be treated as settled accounting
  • Receipt integrity must be verified before use as trustworthy evidence; missing rollback evidence is flagged as EVIDENCE_BOUNDARY
  • Policy defaults come from martin.config.yaml and can be overridden by CLI flags; misconfiguration can loosen safety boundaries
Supported clientsCursor, Claude CodeClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools110