← Engineering Log
General

MCP Guide for Authors: Build Safe, Consent-Ready Servers

Building an MCP server puts you in a powerful position: your tools become the hands of AI agents operating inside real user environments. That power comes with responsibility most MCP authors haven't fully addressed yet — specifically around user consent. This guide walks through what MCP authors need to know, what they often skip, and how to ship production-ready servers that don't create legal or trust liabilities.

What Is an MCP Server and Who Are Its Authors?

MCP (Model Context Protocol) is an open standard that lets AI agents — powered by LLMs like Claude or GPT — call external tools and services through a structured interface. An MCP server exposes those tools: functions the agent can invoke to read files, send emails, query databases, post to APIs, and more.

MCP authors are the developers who build and maintain these servers. You might be:

  • An indie developer publishing an open-source MCP package
  • An enterprise AI team embedding MCP into internal workflow automation
  • A SaaS company giving your product an AI agent integration surface
  • An LLM application builder wiring multiple MCP servers together

Regardless of context, you are defining what actions an AI agent is allowed to take on behalf of a user. That makes you directly responsible for how those actions are authorized.

Core Responsibilities of an MCP Author

Most MCP documentation focuses on the technical side: defining tool schemas, handling JSON-RPC calls, returning structured responses. That's necessary but incomplete. Your actual responsibilities span three areas:

  1. Tool design — defining what actions are exposed and how they're scoped
  2. Authorization — ensuring the agent only acts when the user has approved it
  3. Observability — logging what happened, when, and with what permission

The last two are where most MCP servers fall short today.

Why Consent Is a Critical Gap in MCP Servers

MCP servers are often built quickly and iteratively. Consent gets deferred — "we'll add it later" — or assumed to be handled by the host application. In practice, neither is true. The result is agents that take real-world actions without any verifiable record that the user approved them.

How Agents Act Without Explicit User Permission

When an LLM decides to call an MCP tool, it doesn't inherently check whether the user consented to that specific action. It checks whether the tool is available. Authorization is typically:

  • Implicit — the user logged in, so the agent assumes broad permission
  • Session-scoped — the user approved the app once, not each sensitive action
  • Absent — no approval mechanism exists at all

This matters because agents can take high-impact, irreversible actions: sending messages, modifying records, making purchases, deleting data. An agent invoking send_email or delete_record without explicit per-action consent is a product liability waiting to materialize.

Compliance and Liability Risks for MCP Authors

Publishing an MCP server that performs sensitive actions without a consent layer exposes you to real risk:

  • GDPR / CCPA violations — processing user data without lawful basis or records of consent
  • Enterprise procurement blockers — security-conscious buyers require audit trails before approving AI tools
  • Reputational damage — a single incident of an agent acting without user permission can destroy trust
  • Terms of service violations — many downstream APIs require verifiable user authorization before your integration can act on their behalf

If you're targeting enterprise AI teams or building for regulated industries, consent infrastructure isn't optional — it's a hard requirement.

Adding a Consent Layer to Your MCP Server

A consent layer sits between the agent's decision to act and the actual tool execution. It answers one question: did the user explicitly approve this action?

The implementation pattern looks like this:

  1. Agent decides to invoke a tool (e.g., transfer_funds)
  2. MCP server intercepts the call and checks for a valid consent token
  3. If no token exists, the server requests user approval via a hosted consent screen
  4. User approves or declines
  5. Server receives a signed token and proceeds (or halts) accordingly
  6. Every step is logged with an immutable audit record

Using Permitly to Gate Agent Actions with Signed Approvals

Permitly (permitly.dev) is purpose-built consent infrastructure for exactly this pattern. It provides a hosted SDK that MCP authors can drop into their server with minimal code. The flow is straightforward:

// 1. Request consent before executing a sensitive tool call
const consentRequest = await permitly.requestConsent({
  userId: context.userId,
  action: "send_email",
  scope: { recipient: params.to, subject: params.subject }
});

// 2. Redirect user to Permitly's hosted consent screen
// consentRequest.redirectUrl → your app handles the redirect

// 3. Verify the signed JWT before executing the tool
const verified = await permitly.verifyConsent(token);
if (!verified.approved) throw new Error("User did not approve this action.");

Three lines of meaningful logic. Permitly handles the hosted consent UI, the signing, and the verification — so you don't build any of that yourself. The signed JWT your agent verifies at runtime is cryptographically tied to the specific user, action, and scope — not a generic session token.

This is directly relevant whether you're building an MCP server for internal enterprise AI teams, publishing an open-source MCP package, or wiring up a multi-agent automation workflow.

Logging and Audit Trails for MCP Tool Calls

Every tool invocation should generate an immutable log entry. Permitly automatically records each approval, decline, and revocation with timestamps and scope details. For MCP authors, this means:

  • Compliance evidence — you can demonstrate that user consent existed before each action
  • Debugging — trace exactly what the agent did and why
  • Revocation support — if a user withdraws consent, subsequent tool calls are blocked

Don't rely on application-level logs alone. An immutable, third-party audit trail is what enterprise buyers and compliance teams actually require.

Best Practices for Permission Scoping in MCP Tools

Broad permissions create broad risk. Apply these scoping principles when designing your MCP tools:

  • Principle of least privilege — each tool should request only the access it needs for that specific action, nothing more
  • Action-level granularity — separate read_calendar from write_calendar and delete_event; don't bundle them
  • Time-bound consent — approve actions for a session or a defined window, not indefinitely
  • Scope binding — tie consent to specific parameters (e.g., consent to email this recipient, not all emails)
  • Explicit revocation — users should be able to withdraw consent at any time, with immediate effect

These practices reduce your blast radius if something goes wrong and make your MCP server far easier to trust.

MCP Author Checklist Before Going to Production

Before shipping your MCP server, verify each of the following:

  • Every sensitive tool call is gated by explicit user consent, not just session auth
  • Consent tokens are signed and verified at runtime (not just stored client-side)
  • All tool invocations are logged with user ID, action, scope, and timestamp
  • Users can revoke consent and revocation is enforced immediately
  • Your consent records are immutable and exportable for compliance review
  • You've scoped tool permissions to the minimum necessary for each action
  • Your consent flow has been tested for the decline and revocation paths, not just happy-path approval

Frequently Asked Questions

Do I need consent infrastructure if my MCP server only reads data? Read-only access still involves processing user data. Under GDPR and similar frameworks, you need a lawful basis for that processing. Consent is one of the strongest bases you can demonstrate, and logging it protects you.

Can I build a consent layer myself instead of using Permitly? You can, but you're building hosted UI, signing infrastructure, audit logging, and revocation logic from scratch. Permitly gives you all of that in three lines of code — most MCP authors shouldn't spend engineering cycles on consent plumbing.

What's the difference between MCP authorization and user consent? Authorization checks whether the agent can call a tool — typically via API keys or OAuth scopes granted at integration setup. User consent checks whether the user approved this specific action at this moment. Authorization is a technical gate; consent is a legal and trust gate. You need both.