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The context entry points covered so far all encode knowledge ahead of time — your schema, your rules, your documents, your examples. But some context lives in other systems and changes constantly: a CRM’s account state, a ticketing queue, a status page, an internal service. The Model Context Protocol (MCP) is an open standard for connecting exactly those systems to an AI agent, and your Upsolve agent can call external MCP servers at chat time to pull that live context into its answers.
Project Settings' MCP Connectors tab showing connected external sources

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Where MCP fits among the entry points

Each context entry point answers a different question: MCP is the entry point for anything the agent should fetch live rather than be told in advance. A question like “are there any open incidents affecting this customer?” can’t be answered from your data model — but an MCP connector to your incident tracker can.

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Managing connectors

MCP connectors are configured at the project level (Project Settings → MCP Connectors) and are enabled for every workspace’s agent by default — there is no per-workspace connector list. Keep a connector’s tools relevant for every agent in the project, or connect it in a project of its own if one agent would find them noise.

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The other direction: exposing Upsolve over MCP

This page is about MCP as a way to bring external context into your agent. Upsolve also uses MCP in the opposite direction — exposing your agent to MCP clients. Those are deployment topics, covered in their own guides:

Claude MCP Server

Explore your own data in Claude, with charts rendered in the conversation.

Agent MCP for Your Users

Expose your agent to end users or your own agent, scoped per user by token.

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Next steps

Knowledge Base

Store static reference documents the agent searches on demand.

Skills

Package procedures the agent follows once live context arrives.