The Tool Desk
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What to check before connecting
An MCP server can provide context to an AI application and expose tools that read data or take actions. A remote server’s responses can influence the model, and a malicious server may try to extract sensitive information that enters its context. OpenAI warns that this risk matters particularly when connected servers can access sensitive data or act on it (OpenAI’s remote MCP guide).
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Before authorizing a connection, identify who maintains the server, where it runs, what credentials it requests, and what each listed tool actually does. Prefer a server whose source and tool definitions you can inspect. There is no universal MCP safety certification or score established by the sources cited here, so protocol support or a reassuring tool name is not a substitute for review.
- Separate tools that only retrieve information from those that create, change, send, or delete it.
- Check whether any action could be difficult to reverse, expose private information, or affect other people.
- Confirm which user account and data the server could reach if its credentials were misused.
Choose a connection method for where the server runs
A publicly reachable remote server and a local, on-premises, or firewalled server have different network exposure. Use the connection method documented by the assistant you selected; support and setup options are product-specific, not universal across AI assistants.
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Remote server
For a remote service, confirm its identity and authentication requirements before adding it. A server URL alone does not establish trust. Consider that its responses become input to the assistant, and avoid connecting it to sensitive context unless the server and the access it receives are appropriate for that use.
Private or on-premises server
For supported OpenAI products, OpenAI documents Secure MCP Tunnel as an option for connecting to private, on-premises, or firewalled servers without making the server publicly reachable or opening inbound firewall ports (Secure MCP Tunnel documentation). This is an OpenAI-specific option; check the current documentation for your product and follow its exact setup instructions. Do not assume another assistant supports the same tunnel.
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Configure authentication and authorization at the server
For OAuth-protected MCP services, the MCP authorization specification describes an OAuth 2.1-based flow, protected-resource metadata, and authorization-server discovery. Its security guidance calls for clients to use the resource parameter and for servers to validate that a token was issued for the intended server (MCP authorization specification; MCP security best practices).
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- Use the assistant’s documented authorization flow. Confirm which OAuth and MCP authorization features the assistant supports, then follow its current setup guidance.
- Validate credentials on every request. The server should authenticate the caller and enforce authorization for each tool call, scoped to the authenticated user. The model should not decide whether access is allowed.
- Check token audience. Reject a token intended for another resource. Do not forward the token received from the MCP client to an upstream API; use credentials intended for that upstream service.
- Use PKCE for authorization-code flows. The MCP guidance describes PKCE as a protection against authorization-code interception and injection.
- Grant only the required scope. Limit the data and actions available to the server to what the task actually needs.
OAuth can help establish and constrain identity and access, but it does not stop prompt injection or make a tool’s behavior safe. Authentication and authorization are server responsibilities; safe handling of returned content and tool actions also matters.
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Review tools and require approval for sensitive actions
Inspect the tools the assistant discovers, not just the server’s name or summary. Tool annotations should reflect actual behavior: “read-only” should mean no state changes, while “destructive” should identify actions with difficult-to-reverse effects (MCP tools specification).
Use the assistant’s controls to restrict allowed tools and require approval for sensitive operations where those controls are available. OpenAI’s remote MCP guidance recommends configuring approval requirements and allowed tools. Avoid automatic execution for high-impact actions unless you have evaluated the consequences and the safeguards in the specific assistant and server.
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- Connect using the assistant’s documented method and inspect the tools it lists.
- Check how the assistant presents a tool call, its inputs, and any approval prompt.
- Start with a low-risk task, such as retrieving non-sensitive information, and verify that the result matches the tool’s stated purpose.
- Keep approval enabled for consequential actions and disable tools the task does not require.
A low-risk check can show what the assistant discovers and how its controls behave; it cannot prove a server is safe.
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Understand the main risks
Prompt injection in content or tool responses
Instructions can be embedded in retrieved content or returned by tools to try to steer the assistant. Treat server content and tool descriptions as untrusted input. A model’s apparent willingness to follow rules is not an access-control boundary. OpenAI calls prompt injection an important concern when connected tools can access sensitive data or take action (OpenAI’s remote MCP guide).
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Overbroad or misused credentials
A token accepted by the wrong resource, or passed through to an upstream API, can grant access beyond its intended purpose. Audience validation, per-request authorization, least-necessary scope, and separate upstream credentials help keep access bounded.
Excessive tool authority
A server may expose tools that write data or perform destructive actions. Names and descriptions can be misleading or incomplete, so inspect behavior, enforce authorization on the server, and retain approval controls for consequential operations.
Unnecessary network exposure
Making a private server directly reachable from the public internet increases the surface that can reach it. If your server must remain private, use a supported private connection method rather than exposing it solely to simplify setup.
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Check the chosen assistant’s live documentation for supported transports, OAuth requirements, available approval settings, and any private-server connection option. These capabilities vary by product and can change. Support for MCP establishes a way to connect; it does not independently validate a particular server, and no universal assistant ranking follows from the cited sources.
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