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

DimensionAI Vision MCP ServerAI-powered image and video analysis MCP server using Gemini and Vertex AICompartmentEncrypted, fully offline long-term memory for AI agents
FMRS64 / 100 · C75 / 100 · B
Reliability10 / 2012 / 20
Security and permissions14 / 2016 / 20
Maintenance12 / 2014 / 20
Documentation15 / 2018 / 20
Setup experience13 / 2015 / 20
Best for
  • Developers looking to quickly integrate AI vision capabilities, especially within Google's AI ecosystem.
  • Design teams needing UI/UX compliance and accessibility audits.
  • Workflows requiring automated image and video analysis.
  • Individuals or teams who prioritise privacy and offline operation and do not want data leaving the machine
  • Users who share memory across multiple MCP clients such as Claude Code, Claude Desktop, Hermes Agent, OpenClaw, Cursor, Codex and Gemini CLI
  • Scenarios that need encrypted storage, a hash-chained audit log and verifiable vault migration
  • Users who want low-latency recall, a bundled embedding model and no extra LLM calls inside the memory layer
Not for
  • Not suitable for scenarios requiring offline or on-premise AI models.
  • Not for integrations with non-Google cloud resources (e.g., AWS).
  • May not be ideal for complex video editing or real-time streaming needs.
  • Users who need cloud multi-device sync or a hosted team memory service; moving between machines means locking the vault and copying the file
  • Users who expect the server itself to run an LLM that extracts facts and decides what to remember; Compartment explicitly keeps no LLM inside and leaves that to the host model
  • Users who cannot keep a passphrase safe or who need a recovery path if it is lost, since Compartment never generates a password, seed or recovery phrase
  • Deployments that require network transports such as SSE or streamable-http; this server is stdio only and opens no ports
Required permissions
  • Requires access to Google AI Studio API or Vertex AI API credentials.
  • For local file analysis, needs read access to the local filesystem.
  • If using Vertex AI, needs access to a Google Cloud Storage bucket.
  • For video analysis, needs access to YouTube or local video files.
  • Read and write the vault file under the user's home directory, by default ~/.compartment/memory.vault, and its sibling settings file
  • Access the session directory holding the unlock credential (configurable with COMPARTMENT_SESSION_DIR), which also carries the shared embedding process's Unix socket
  • Optionally use the macOS keychain (compartment unlock --keychain is an explicit opt-in, and it survives reboots)
  • Write or merge an mcpServers entry into each MCP client's own configuration file, taking a byte-exact backup first
  • Install a PostToolUse hook for Claude Code that captures memory files the agent writes
  • Serve a read-only dashboard on 127.0.0.1 behind a one-time random URL token
Risks and side effects
  • API key leakage: environment variables contain sensitive credentials; store them securely.
  • Data privacy: images/videos sent to Google Cloud may contain sensitive information; ensure compliance.
  • Cost: API usage may incur costs based on volume.
  • Potential timeout or transport errors if not configured properly.
  • A leaked passphrase means the encrypted content can be decrypted; Compartment generates no recovery phrase and cannot recover a lost passphrase
  • Enabling the memory_unlock tool places the passphrase in the model's context, which is why it is off by default
  • If a 2FA keyfile is lost, for example a USB stick, the vault cannot be opened even with the passphrase
  • `export --plaintext` writes the vault as unencrypted JSONL, so that file must be handled carefully
  • Memory content can come from untrusted sources; recall wraps memories with a notice that they are stored data and content can be marked quarantined, but the host agent must still treat memory as data to limit prompt-injection risk
  • The capture hook and client integration write into the user's own settings files, although the implementation backs up and merges rather than replacing
Supported clientsClaude Desktop, Claude Code, Cursor, ClineClaude Desktop, Claude Code, Hermes Agent, OpenClaw, Cursor, VS Code, Codex, Gemini CLI, Cline, Roo Code, Zed, OpenCode, LM Studio, AnythingLLM, BoltAI, Goose, Kiro
Tools514