NewsroomPlatform release · · 5 min read

Meetric MCP: the governed conversation layer for ChatGPT, Claude and enterprise AI.

One OAuth connection gives MCP-compatible AI clients on-demand, permission-aware access to conversation context, without first copying the entire archive into another silo.

MCPopen connection standardOne interface for compatible AI clients and agents
OAuth + PKCEuser authorizationExplicit user approval and short-lived access
6 scopesseparate permission familiesConversation, transcript, insight and directory access

AI is only as useful as the context it can safely reach

Business conversations hold commitments, objections, decisions and emerging risks. Yet most general-purpose AI starts without that context, or receives it through one-off exports that create another unmanaged copy.

Meetric MCP uses the open Model Context Protocol to make permitted conversation context available when an AI client needs it. Meetric remains the governed system of context; the connected AI becomes an interface for asking questions and drafting next steps.

One layer across supported, authorized conversations

Phone and dialler calls, video and in-person meetings, and approved email are the current source scope. Selective chat-platform ingestion remains under validation.

Through MCP, a connected client can request permitted conversation records, summaries, transcripts and source-linked insights instead of relying on detached notes or a second bulk archive.

One endpoint should not mean one open pipe. Every request stays bound to a user, a scope and a permitted view.

How governed MCP access works

A user connects Meetric from an MCP-compatible client and sees an authorization request before access is granted. OAuth authorization with PKCE binds the connection to that person, account and approved set of scopes.

Every data request is evaluated again inside Meetric. Account isolation and the same conversation-visibility rules used in the workspace determine which records can be returned; directory access also requires an appropriate account role.

  • Separate scopes for conversations, transcripts, insights, users, teams and departments
  • User, account and role carried with every authorized request
  • Visibility filtering applied before a conversation is returned
  • Responses marked with authorization mode, user, account and timestamp

One standards-based path into major AI ecosystems

Remote MCP is supported by ChatGPT, Claude and Microsoft Copilot Studio, and by supported Google or Gemini agent tooling. Meetric exposes a standards-based remote endpoint for these clients and for internal agents.

That reduces the need for a separate proprietary data model for every AI product. It is not a claim of universal plug-and-play compatibility: support still depends on the client, plan, transport, administrator settings, OAuth configuration and client-specific testing.

Questions teams can bring to their preferred AI

A revenue leader can investigate recurring objections across permitted calls. A product team can compare themes from meetings and approved email. An internal agent can retrieve the source conversation behind an insight before a separately authorized workflow is updated.

The AI client performs the reasoning and presentation; Meetric decides which underlying context the signed-in user is allowed to retrieve.

GDPR and the EU AI Act: governance at the handoff

OAuth, scoped access, data minimization, account isolation and source provenance support governed use of conversation data. They help organizations avoid exposing an entire archive when a smaller permitted result is sufficient.

They do not create blanket legal compliance. The customer remains the data controller and is responsible for legal basis, participant information, purpose and retention. Meetric acts as processor or sub-processor under the agreed DPA.

Under the EU AI Act, transparency duties apply in specified cases and high-risk uses carry additional obligations. An MCP connection does not determine that classification. The organization must assess the downstream use case, define any required transparency and human oversight, and document how the receiving AI client is configured.

Live context without pretending no data moves

MCP reduces the need to pre-copy a full conversation archive into every AI product. When a user approves a request, selected results are still transmitted to the chosen client for processing.

Organizations should therefore review that client’s hosting, retention, model-training, access and audit settings before enabling it. Governance is strongest when Meetric’s permission boundary and the client’s enterprise controls are configured together.

More from MeetricNewsroom

See Meetric on your own conversations.

Start with the conversations you are already allowed to use. We show the governed record, the sourced answers and the permitted actions on your data, not a script.