| FMRS | 40 / 100 · D | 75 / 100 · B |
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| Reliability | 6 / 20 | 12 / 20 |
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| Security and permissions | 6 / 20 | 16 / 20 |
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| Maintenance | 10 / 20 | 14 / 20 |
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| Documentation | 8 / 20 | 18 / 20 |
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| Setup experience | 10 / 20 | 15 / 20 |
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| Best for | - Students who want to interact with Canvas through an AI assistant
- Instructors looking to automate course management and grading
- Administrators needing account-level oversight and reporting
| - 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
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| Not for | - Users seeking official support from Instructure
- Use cases requiring instant mobile access or offline functionality (not yet implemented)
- Environments with extremely high token security requirements and where administrative privileges cannot be granted
| - 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
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| Required permissions | - Requires CANVAS_API_TOKEN with appropriate permissions: student, instructor, or admin
- Account-level operations require administrative privileges
- The server may read and modify courses, assignments, grades, users, and account data
| - 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
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| Risks and side effects | - API token leakage could lead to unauthorized access
- Large number of tools may overload context windows, reducing efficiency
- Prompt injection risk: tool outputs may contain malicious instructions; enforce least privilege
- Unofficial server, not supported, may not meet institutional security policies
| - 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
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| Supported clients | Claude Desktop, Codex, Cursor | Claude Desktop, Claude Code, Hermes Agent, OpenClaw, Cursor, VS Code, Codex, Gemini CLI, Cline, Roo Code, Zed, OpenCode, LM Studio, AnythingLLM, BoltAI, Goose, Kiro |
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| Tools | 53 | 14 |
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