What each is for, where Cloudflare MCP Server Portals is strong, and how the two differ, capability by capability.
Cloudflare, Inc. · Based on the vendor's public documentation, last reviewed .
Cloudflare MCP Server Portals
Cloudflare One gathers remote MCP servers behind portals protected by Cloudflare Access, with curated tools, managed OAuth and per-request logs. Gateway adds DLP and detects MCP traffic; AI Gateway handles model traffic.
A cloud service on Cloudflare's network, configured in Cloudflare One (Zero Trust). Portals reach remote MCP servers over HTTP; a server that only speaks stdio needs a self-hosted HTTP wrapper.
Vekri
Runtime authorization for AI agents. Vekri Gateway checks every tool call against a verified identity, a task scope and Cedar policy; privileged writes wait for a person's approval of the exact operation; a broker executes with credentials the agent never sees.
In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri.
Side by side
| Capability | What it means | Vekri | Cloudflare MCP Server Portals |
|---|---|---|---|
| Decides each tool call inline | Every agent tool call is allowed or denied before it reaches the protected system. | Yes Vekri Gateway decides every MCP tool call sent through it before the broker executes it; a denied call never reaches the system. It is a boundary only when it is the only path to that system. | Partly The portal proxies every tool call and applies Access policies to the portal and each server. Cloudflare describes its current MCP policy as connection-level, with control over specific tool use to follow. [1] [3] |
| Verified workload identity | An agent running as a workload proves who it is with a credential the platform issued, not a shared key. | Yes Kubernetes projected service-account tokens, verified with TokenReview before Vekri issues a short-lived token for the run; every call is then authenticated. SPIFFE X.509-SVIDs from a configured trust bundle (no Workload API yet). | Not in public docs Machine clients can authenticate to a portal with Access service tokens, a client ID and secret. Attesting a workload's identity, for example with Kubernetes or SPIFFE, is not described. [1] |
| Employee agents use company SSO | Agents that employees run sign in as that employee, through the company identity provider. | Yes Vekri is the MCP authorization server for Claude Code and federates to your OpenID Connect provider. SAML sign-in is not available yet. | Documented Users sign in through Cloudflare Access with the organization's identity provider, and MCP clients are directed through an OAuth flow backed by Access. [1] [2] |
| Task-scoped permissions from the server | What an agent may do is set per task by the server, not by the agent or its prompt. | Yes A run scope for workloads, or a protected access assignment for employees. The effective permission is always an intersection, never a widening. | Not in public docs Access policies apply to users, groups, portals and servers. A scope issued by the server for each task is not described in the public documentation. [1] |
| Policy as code, deny by default | Decisions come from versioned policy, and anything not allowed is refused. | Yes Cedar over typed operations. Unknown operations and evaluation errors are denied, and a revision can be simulated before it is published. | Partly Access policies decide who reaches each portal and server, and administrators can turn off or allowlist individual tools. Cloudflare describes rules on specific tool use as coming soon. [1] [3] |
| Approval of the exact operation | A person approves one specific operation, and the approval cannot be reused for another. | Yes Privileged writes wait for another person to approve that operation, bound to its digest, short-lived and single use, with separation of duties. | Not in public docs Approval of an individual tool call by a person is not described in the public documentation. |
| Agent never holds backend credentials | Secrets for the protected system stay out of the agent's reach. | Yes A broker executes with narrowly scoped Kubernetes or GitHub App credentials. Incoming tokens are never forwarded. | Documented The portal stores upstream OAuth credentials, encrypted, and attaches either an administrator's credential or each user's own authorization when it proxies a request. [1] |
| Audit evidence of decision and outcome | Each call leaves a durable record of what was decided and what happened. | Yes Intent is recorded durably before execution and the outcome after; an uncertain outcome is never retried blindly. | Documented Access logs each tool request with its server, capability, status and duration, and Enterprise plans can export the logs with Logpush. [1] |
| AI agents on workstations | Finds AI coding agents and other AI applications on employee devices, and watches or controls what they do there. | Partly Vekri Endpoint discovers coding agents, desktop and IDE AI apps, local models and their MCP servers on macOS and Linux, and its local MCP gateway decides each MCP tool call sent through it (Claude Code verified connecting; tool calls verified with an MCP client library). On macOS, a network gateway mediates MCP requests to approved remote servers, and agent control stops AI agents the organization has not allowed. Hooks observe built-in tools without blocking them; Windows is not supported. | Partly Gateway detects MCP traffic on TLS-inspected network paths, and a dashboard lists the servers and users it sees; the detection selector is in beta. Local stdio servers and traffic off the managed network are not covered. [3] [4] |
| Prompt and output inspection | Detects prompt injection or sensitive data in what models read and write. | Partly Tool results on Vekri Gateway and the local MCP gateway are inspected for prompt injection before the agent reads them; sensitive-data categories are detected on the device, off by default. Browsers and file uploads are not inspected. | Documented Portal traffic can be routed through Gateway, where DLP profiles block matching requests and responses. AI Gateway guardrails check model prompts and responses for harmful content. [1] [5] |
| Systems covered | Which systems, platforms or agents the product works with. | Kubernetes and GitHub, with GitLab, PostgreSQL, MySQL, MongoDB, Slack, Jira, Linear, Asana, ClickUp, monday.com, HubSpot, Pipedrive, Zendesk, Freshdesk and custom OpenAPI connectors in preview. | Remote MCP servers, up to 80 per portal, including servers secured with their own OAuth. AI Gateway proxies model providers such as OpenAI, Anthropic, Google Gemini and Workers AI. |
| Deployment | How the product is delivered. | In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri. | A cloud service on Cloudflare's network, configured in Cloudflare One (Zero Trust). Portals reach remote MCP servers over HTTP; a server that only speaks stdio needs a self-hosted HTTP wrapper. |
Every agent tool call is allowed or denied before it reaches the protected system.
An agent running as a workload proves who it is with a credential the platform issued, not a shared key.
Agents that employees run sign in as that employee, through the company identity provider.
What an agent may do is set per task by the server, not by the agent or its prompt.
Decisions come from versioned policy, and anything not allowed is refused.
A person approves one specific operation, and the approval cannot be reused for another.
Secrets for the protected system stay out of the agent's reach.
Each call leaves a durable record of what was decided and what happened.
Finds AI coding agents and other AI applications on employee devices, and watches or controls what they do there.
Detects prompt injection or sensitive data in what models read and write.
Which systems, platforms or agents the product works with.
How the product is delivered.
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