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Side-by-side MCP evidence comparison
Evidence fieldDBHubWatchlist candidateGoogle MCP Toolbox for DatabasesMCP100 Selected
Task fit
Best for
  • Developers/DBAs who need a compact MCP interface to multiple SQL databases with configurable read-only guardrails.
  • Schema exploration and database-aware coding in development environments
  • Connecting one MCP layer to supported Google Cloud and third-party databases
  • Defining structured, parameterized database tools with integrated authentication and observability
Not ideal for
  • Users who need a broadly proven, low-risk, fully mature Selected-grade integration today.
  • Unrestricted natural-language SQL against production data
  • Teams unwilling to operate and secure a local or hosted Toolbox process
Avoid when
  • Avoid high-impact or sensitive production use unless the documented authentication, permissions, rollback, and data handling match your environment.
  • The configured database user can modify production data or schema beyond the approved task
  • Sensitive query results cannot be sent to the AI client
  • Credentials would be committed in tools.yaml or MCP configuration
Provenance
Provenance details

First-party MCP

github:bytebase · Publisher source ↗

First-party MCP

Google · Publisher source ↗

Maintenance
Maintenance details

Stale

Repo: Not documented

Package: Not documented

Repo: Aug 14, 2026

Package: Aug 14, 2026

Popularity evidence
GitHub starsNot documented

16,173

GitHub stars · checked 2026-08-14T18:46:26.000Z

Package downloads / 30 daysNo reliable download data

61,660

package downloads / 30 days · checked 2026-08-14T18:46:26.000Z

External adoption evidenceNot documented
  • npm: package downloads: 61,660Exact attributable signal.npm: package downloads · Rolling 30 days · checked 2026-08-14T18:46:26.000ZEvidence source ↗
Client coverage
Client coverage detailsNot documented
Claude CodeLocal stdio
Claude DesktopLocal stdio
CodexLocal stdio
VS CodeLocal stdio
CursorLocal stdio
OpenCodeLocal stdio
Client coverage noteLocal stdio runs on your computer. Streamable HTTP connects to a remotely hosted MCP server.
Access and data
AuthenticationUses the configured database credentials; SSH/TLS/custom connection settings depend on the target database.Authentication depends on the database source and can include database username/password, Google Cloud IAM, or other source-specific mechanisms. Use a dedicated read-only identity and secret manager or protected environment variables.
CostOpen-source server; database/infrastructure/model costs remain separate.The Apache-licensed Toolbox and npm server have no documented MCP usage fee. Database services, Google Cloud resources, network transfer, observability backends, hosting, and the AI client may incur normal charges.
PermissionsCan execute SQL against configured databases. Read-only mode, row limits and query timeouts are key guardrails; custom tools can intentionally expose additional operations.Effective permissions come from each configured database identity and the enabled prebuilt or custom tools. Generic prebuilt sets can include schema discovery and execute_sql; custom tools can constrain statements, parameters, and accessible sources.
Data handlingDatabase schema/query results are exposed to the MCP client; self-hosting keeps the MCP process local but the AI client/provider may receive returned data.The Toolbox process connects directly to configured databases and returns selected schemas, rows, query results, and errors to the MCP client. Credentials may be supplied through environment variables or configuration; integrated cloud authentication and OpenTelemetry are available.
Limitations
Tradeoffs
  • Database write authority is possible, so safe defaults and credential isolation need strong production evidence.
  • Prebuilt tools are quick to adopt but may expose generic execute_sql capability; custom tools require more design but provide a narrower contract.
  • The npx path is convenient but the publisher recommends binary or container installation for standard reliable deployments.
  • Supporting many database engines increases configuration flexibility and operational complexity.
Risk contextDatabase write authority is possible, so safe defaults and credential isolation need strong production evidence.Generic SQL tools can expose or mutate large amounts of data and retrieved database text can carry prompt-injection content. Prefer a non-production replica, read-only credentials, allow-listed custom parameterized tools, query limits, audit logs, and no secrets in config. This review does not claim MCP100 execution testing.
Evidence date
Editorial review2026-09-13T00:00:00Z2026-07-24
Candidate evidence2026-09-13T00:00:00Z2026-08-14T18:46:26.000Z

Popularity, maintenance, fit, permissions, and client support are independent evidence fields.

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