Cursor Enterprise: Model & Integration Management (Allowlists, BYO Keys, MCP)

From the Enterprise admin dashboard, admins set allow or blocklists at two levels: the provider (block all Anthropic models, for example) or an individual model configuration. A May 2026 update added the ability to filter by specific model attributes — speed tier and context window size — so admins can restrict to cheaper fast models or require high-context models for certain workloads.

• Block an entire provider to prevent all of its models from appearing in the model picker.
• Block specific model versions while leaving the rest of that provider available.
• Filter by speed or context window to enforce a cost or capability floor.
• Enable block new providers by default so newly released models don't appear until explicitly approved.

Cursor provides frontier models from four providers on Enterprise plans: OpenAI, Anthropic, Google Gemini and xAI. The exact model list changes as providers release new versions; admins see the current set in the model management section and can approve or block each one.

Enterprise customers can supply their own API keys for supported providers. Requests using a BYO key route through the customer's provider account at that provider's published rates, plus Cursor's Token Rate for infrastructure. This is the standard pattern for:

• Organizations with existing enterprise agreements with a model provider.
• Teams that need specific rate limits or data residency guarantees from the provider.
• Finance teams that want AI costs to flow through a single provider invoice.

Enterprise admins can allowlist or blocklist MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. servers — the external integrations that give agents access to databases, APIs and internal tools. Admins configure this centrally so members get a consistent, reviewed set of integrations rather than each individual managing their own MCP list.

1. 1Open the admin dashboard model and integration management section.
2. 2Review the MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. servers team members have connected (or propose to connect).
3. 3Add servers to the allowlist to make them available; add to the blocklist to prevent use.
4. 4Publish the approved list; team member MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. pickers reflect the admin configuration.

Beyond model and MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. controls, admins can set global agent run settings that apply across the team: default model for new agent chats, permission levels for agent actions (read-only vs write), and the repo allow/blocklist for Cloud AgentsAgents that run in a Cursor-managed virtual machine, check out the repo, do the work and open a pull request, then shut down, with no load on your laptop.. These settings establish a team-wide baseline that users can override within any bounds admins permit.