Valsight MCP integration

The Valsight MCP integration exposes your Valsight models to external AI agents over the Model Context Protocol (MCP), an open standard for connecting AI clients to tools and data. It is the external-client counterpart to the in-app Assistant: instead of chatting inside Valsight, you connect your own AI client — such as Claude — through the MCP server, and it both explores and changes your models on your behalf, always inside a session whose scope and permissions you control.

• Free through 31 December 2026 — included for all customers at no additional cost. Connect your own AI client, work with your real models, and see what it adds.
• A paid add-on from January 2027.
• Off by default — it is opted into per instance and needs some setup.

To switch it on, or to talk through whether it fits your team, write to support@valsight.com or contact your Customer Success representative.

What it is

The MCP server is a connection layer, not a screen inside Valsight. An external AI client connects to it, authenticates, and receives a set of tools it can call to work in a project on your behalf — reading model structure and metadata, and, when the session allows it, making changes.

Every connection runs as a session that is scoped and permissioned up front (see Session scope and permissions). Nothing the agent does escapes that session’s boundaries.

What you can do with it

A connected agent can, within the session’s scope and permissions:

• Explore models — search across models, workspaces, data sources, and dimensions; inspect nodes, levels, and their usage; read formulas, functions, and entity definitions.
• Read data — fetch calculation results, line-item value tables, and dimension values (subject to the session’s data-access setting).
• Build and edit models — create, update, duplicate, and delete entities; manage line-item levels; add dimension rows.
• Work with charts — create and update charts and their display configuration, including chart and perspective images.
• Manage scenarios and workspaces — select scenarios/workspaces, share and unshare scenarios, and export scenario versions and template nodes.
• Handle data sources and ETL — upload data, map data-source columns, import tables, and build and run ETL pipelines.
• Run workflows — list, configure, and manage multi-step workflows.
• Analyze — validate workspaces, models, and data sources, and inspect calculation performance.
• Manage access — explain access-rights breakdowns and grant or revoke access (only when the session is permitted to modify access rights).
• Use the knowledge base — search and read Valsight product documentation, and list and read guided skills.
• Move data in and out — download entities as Excel, and upload files into the session.

Session scope and permissions

Each session is bounded on five independent axes, chosen when the client connects. This is the core safeguard: a session can only ever do what its axes allow.

Broadening access is not automatic. An agent cannot silently raise its own permissions: expanding any axis requires your explicit confirmation (a form in the client, or a link to the session settings). Narrowing access takes effect immediately.

What it cannot access or do

• It cannot act outside the session’s project scope — entities in other projects are refused.
• In read-only sessions it cannot create, update, or delete anything.
• In data-restricted sessions it cannot read or write numeric values, import table data, or inspect/run ETL for data.
• It cannot change access rights or delete project spaces unless those axes are explicitly enabled.
• It cannot reach external systems or use your personal credentials — it authenticates through its own token exchange.
• It cannot connect to an unrecognized Valsight instance — the target instance is validated before any secret is sent.

Connecting an AI client

Prerequisite: the MCP server must be enabled for your Valsight instance. It is a premium feature, opted into per customer (see the availability note at the top of this page), so confirm your instance has it before you start.

There is no single, public MCP endpoint. The server address is specific to your Valsight instance and contains your instance’s URL — you connect your AI client to that per-instance address.

Connecting is a two-part handshake: the client authenticates with OAuth 2.0, and the server exchanges that identity for access to your instance.

1. Point your AI client at your instance’s MCP endpoint. Any AI client that supports MCP can connect. There is no single, standard way to add an MCP server — how you configure it depends on the client, so follow that client’s own MCP setup instructions.
2. Sign in when prompted. Authentication uses your normal Valsight sign-in — the same flow you already use.
3. Set the session scope. You can steer what the session may do by asking the agent in plain language, or through the session-management page. Scope covers the target instance, project(s), access level, and data access — see Session scope and permissions for the axes and their defaults.

Sessions and sign-in: a session ends after a period of inactivity, when access is revoked, or when an administrator ends it. Your Valsight sign-in stays valid for 12 hours, after which you sign in again.

Good to know

• Write is the default: an MCP session can change your models unless it is connected as read_only. Scope it deliberately.
• Numeric data is a separate switch: data access is independent of access level — a read-only session can still be restricted from seeing numbers, and a write session can still be blocked from them.
• Every call is audited: tool calls are logged per session for traceability.

Related

• Valsight AI: overview of both ways to use Valsight AI
• Using the AI Assistant: the in-app Assistant — how to phrase questions and what to expect
• FAQ: Valsight MCP integration: connecting, security, and prerequisites