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Compare MCP servers

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

DimensionGoogle Search Console MCP ServerAnalyze Google Search Console SEO data through natural-language chats with your AI assistantCompartmentEncrypted, fully offline long-term memory for AI agents
FMRS63 / 100 · C75 / 100 · B
Reliability10 / 2012 / 20
Security and permissions10 / 2016 / 20
Maintenance13 / 2014 / 20
Documentation16 / 2018 / 20
Setup experience14 / 2015 / 20
Best for
  • SEO practitioners who regularly analyze GSC search performance data
  • Users of MCP-compatible clients such as Claude Desktop, Cursor, Codex CLI, Gemini CLI, or Antigravity
  • Individuals and small teams who prefer natural-language conversation over manually exporting reports
  • Users willing to configure their own Google Cloud credentials and run the server locally
  • 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
  • Users who only want claude.ai in the browser and won't install the desktop app (the MCP server runs locally)
  • Users who don't want to create a Google Cloud project and API credentials
  • Users who need a hosted one-click sign-in with GA4 tools (the source points to a separate hosted version limited to 100 seats)
  • Users running a cloned install on Python 3.10 or older
  • 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
  • Google Search Console API access (read search analytics and site data)
  • OAuth path requires Google account authorization, with the token cached locally
  • Service account path requires adding the service account email as a Full access user on your GSC property
  • Credential JSON file paths are provided by the user via the client config env
  • Destructive operations (add_site, delete_site, delete_sitemap) are disabled by default and require explicitly setting GSC_ALLOW_DESTRUCTIVE=true
  • 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
  • Credential JSON files contain sensitive keys and must be protected and kept out of version control
  • If GSC_ALLOW_DESTRUCTIVE is enabled, the AI assistant can perform destructive actions such as adding/removing sites and deleting sitemaps
  • Remote deployment with sse transport bound to 0.0.0.0 exposes the service on the network, so you must assess access-control risk yourself
  • OAuth tokens are cached in the local platform user config directory, which may have access boundaries on multi-user machines
  • Service account keys are long-lived, so a leak has a broad impact
  • 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, Cursor, Codex, Gemini CLI, AntigravityClaude Desktop, Claude Code, Hermes Agent, OpenClaw, Cursor, VS Code, Codex, Gemini CLI, Cline, Roo Code, Zed, OpenCode, LM Studio, AnythingLLM, BoltAI, Goose, Kiro
Tools1514