Carlisia Campos

MCP Provenance

2 min read

In the context of MCP, provenance refers explicitly to the ability to clearly trace, verify, and audit the origin and history of data, resources, decisions, and actions within MCP-enabled AI workflows.

More concretely, provenance within MCP includes:

  • Where resources originated from:
    Explicit documentation of sources such as databases, files, APIs, or other MCP servers providing context.
  • Which exact resources were used in generating prompts:
    Clear auditability of every structured prompt and associated context used in interactions with language models.
  • When, why, and how actions were taken:
    Auditable records showing explicit consent, policies, and validation logic behind every decision and tool invocation.
  • Who authorized or consented to an action:
    Clear records showing explicit authorization and consent provided by users or policy-based logic.
  • Transparency of decision-making:
    Ability to explicitly inspect and verify each step in MCP interactions (Client-to-Server, Server-to-External system, LLM-to-Client interactions).

Why is Provenance critical in MCP?

  • Security and trust:
    Ensuring every action and decision can be audited for compliance, regulatory purposes, and trustworthiness.
  • Compliance and governance:
    Adhering to strict enterprise, regulatory, or ethical requirements around data usage, consent, privacy, and accountability.
  • Debugging and traceability:
    Quickly identifying and resolving problems by clearly tracing back through structured interactions and decisions.
  • Transparency for users:
    Providing explicit transparency, allowing users to fully understand and trust the interactions happening on their behalf.

Community discussions and proposals

As of the current MCP specification (Revision: 2025-06-18), provenance is not fully detailed within the spec itself. However, it seems to be an issue recognized as essential by the community:

Tools for provenance

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