$ loading_
Govern DataHub MCP calls with policy checks, redaction, review, and audit writeback.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "DataHub Chaperone" yet — see the docs or source repo.
Use DataHub Chaperone to check whether this agent tool call complies with catalog metadata policy and return an allow, review, or deny outcome: the agent is trying to query the field list of a dataset.
A policy-based decision indicating whether the call is allowed, requires review, or is denied.
Have DataHub Chaperone process this agent call: read catalog metadata for a data asset. If policy applies, redact sensitive content and return a display-safe result.
A redacted result with the corresponding governance decision attached.
Use DataHub Chaperone to govern an agent tool call and write the agent action and outcome back into DataHub for audit tracking.
A governance decision plus a recorded agent action written back into DataHub.
Developers or platform teams can place Chaperone in front of the DataHub MCP Server to evaluate agent tool calls against catalog metadata policy. This lets them allow, redact, review, or deny requests before execution.
When teams need to track how agents interact with the data catalog, this tool can write agent actions back into DataHub. That makes governance and audit trails easier to maintain.
It is a governance proxy that sits in front of the DataHub MCP Server. It evaluates agent tool calls against catalog metadata policy, returns allow, redact, review, or deny outcomes, and writes agent actions back into DataHub.
Based on the description, it is meant to work with the DataHub MCP Server and write results back into DataHub. For exact installation steps and prerequisites, see the source repository.
Its main role is not simply serving data capabilities directly, but enforcing governance around DataHub MCP calls. The key difference is policy-based allow, redact, review, or deny decisions plus agent action logging.
Enforce permissions, approvals, sanitization, and audit for AI agent MCP calls.
Enforce policy checks on AI tool calls without exposing system credentials.
Connect to DataHub catalogs to find datasets, trace lineage, and read metadata.
Manage identity, permissions, auditing, and recall for healthcare AI agents.
Proxy and manage multiple MCP servers through one secure unified interface.
A drop-in proxy that secures MCP servers with policy and logging.