MCP tool governance defines which connected tools an AI application may use, what data they can reach, and which actions require approval. The protocol helps connect applications and tools; business authorization remains an implementation responsibility. Start with a tool inventory and enforce permissions outside the model’s conversational instructions.
Inventory tools before granting access
Record each server’s publisher, deployment location, authentication method, exposed operations, and data access. Review the implementation rather than assuming every server with a familiar product name is official. Separate tools that read public information from those that retrieve private records or modify systems. The MCP documentation explains the protocol’s role. Confirm the actual behavior and scope of the server you deploy, including what happens when an upstream application changes its API.
Classify consequences by operation
Assess individual actions rather than giving an entire server one broad approval. Searching approved documentation, drafting a customer message, editing a contact, and issuing a payment carry different consequences. Define allowed records, destinations, and parameters for each operation. Use the smallest permissions practical for the workflow. A task that prepares an estimate summary does not need authority to alter prices or send invoices. Reject unsupported requests explicitly instead of asking the model to improvise a substitute action.
Bind human approval to a specific request
Show the reviewer the proposed tool, target record, arguments, and expected effect. Keep supporting evidence available for inspection. Approval should authorize that exact operation under the stated conditions, not grant open-ended permission for future actions. Revalidate when important details change. Enforce the decision in the application or gateway so a retrieved document cannot bypass it. External content may inform the task, but instructions embedded in that content should not determine authorization.
Record execution and design recovery
Log request identifiers, permission decisions, approved action references, and confirmed results while minimizing sensitive content. Distinguish an attempted call from a successful external change. For timeouts, verify whether the operation completed before retrying. Add duplicate protection for writes and a visible queue for failures. Keep a rapid way to revoke credentials or disable a tool. Assign an operator who can inspect an incident and continue necessary business work through a documented manual process.
Use release gates that test enforcement
Test unauthorized records, changed arguments, expired approvals, malicious retrieved instructions, duplicate events, and unavailable services. Inspect actual tool outcomes as well as the assistant’s final answer. Use NIST’s AI Risk Management Framework to organize ownership and risk review. Measure blocked unauthorized actions, legitimate requests incorrectly denied, and recovery time. Expand access only when the team has evidence that permitted work succeeds and prohibited operations remain blocked across realistic failure conditions.
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