An AI agent needs an API it can choose from, understand, call safely and recover from when something goes wrong. That means clear machine-readable operation descriptions, bounded inputs and useful outputs, structured errors, and server-enforced access controls. It does not mean turning every endpoint into a tool or adopting MCP for every integration.
What does an AI agent need from an API?
An agent selects operations using the names, descriptions and schemas presented to it, then uses the results to decide what to do next. Those descriptions are part of the runtime interface, not merely documentation for people. The IETF’s June 2026 “Design Considerations and Profile for HTTP APIs Consumed by AI Agents” calls this out succinctly: “The description is input.” The document is an informational Internet-Draft, not a finalized protocol or mandatory standard; its profile focuses on API behavior and descriptions, not agent identity, authentication, authorization, planning or evaluation.
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A clear, machine-readable contract
Give each operation a stable, meaningful name, a concise explanation of its purpose, a schema with clear types and allowed values, and documented return values. Use explicit enums instead of asking the model to guess valid options. Keep descriptions in sync with the implementation: a generated tool layer may expose only what the machine-readable contract says.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteDescriptions should distinguish operations that might otherwise look alike. If two tools do similar things, explain when to use each and what it changes. Include structured signals for important behavior—such as whether an operation is deprecated, retryable or high risk—instead of relying only on warning text that a client must interpret.
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A deliberate tool surface
Choose tools around useful tasks rather than exposing every low-level endpoint. A bounded operation that safely completes a common task may be easier to use than a chain of small calls, but preserve the meaning of affected resources, authorization, audit records and partial failures. For batch work, return an outcome for each item. If an agent cannot reasonably know an opaque identifier, accept a human-meaningful name or provide a lookup operation. Do not expose deprecated operations as if they were current choices.
More tools are not automatically better. The IETF draft says empirical measurements suggest operation selection can degrade as a tool set grows into the hundreds, while noting that the effect varies. Google Cloud likewise recommends focused toolsets, concise definitions and progressive disclosure. Treat the right tool count as something to evaluate for the target model and workflow, not a universal threshold.
Compact, actionable responses
Return what the agent needs for the current task and likely next call, without making the default response needlessly large. Use bounded page sizes, stable ordering and cursor-based pagination; provide a ready-to-use cursor or next-page link. Include readable labels beside opaque IDs where possible, represent money with its currency, and expose links to valid next operations when appropriate.
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If clients need different levels of detail, offer field selection or concise and detailed response modes. Say which fields are omitted and how to request them; compactness must not hide information needed for a correct decision.
Errors an agent can act on
Use a consistent machine-readable error shape, such as HTTP Problem Details, with a stable application error code in addition to the HTTP status. State whether a retry is appropriate, include a delay when useful, and identify validation failures by field. A rate-limit response should indicate that a delayed retry may succeed; a validation error should identify what needs to change.
The IETF draft illustrates a 429 response with retryable: true and retry_after, and a 422 response with retryable: false and field-specific errors. Those field names are examples in the draft, not registered standard fields.
Safe writes and long-running work
For operations with side effects, support client-supplied idempotency keys and document their scope and retention so a repeated submission does not accidentally repeat an action. For consequential or irreversible work, consider a preview or dry run, structured risk metadata, and a separate confirmation step. Provide cancellation or reversal where feasible.
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When work takes more than a few seconds, return promptly with an operation identifier and status URL—often with HTTP 202—rather than leaving the caller waiting. Make the operation’s state, polling guidance, retry delay, completion links and cancellation path explicit. Authenticated callbacks or streaming may suit workflows that can use them.
Stable evolution and discoverability
Treat changes to an API description as changes to the agent’s available tools. Favor backward-compatible changes; version breaking changes; and do not silently change an operation’s meaning under the same identifier. Mark deprecations in machine-readable metadata and link to replacements. Keep a coherent versioning approach and compare successive API descriptions to catch breaking changes.
Publish a complete, low-noise API description—OpenAPI is one option—and generate model-facing documentation indexes from the same source as human documentation. The IETF draft mentions llms.txt as a community convention, not a standard. Accept and propagate a correlation identifier so calls can be traced alongside the acting identity.
Does every API need an MCP server?
No. MCP, custom function tools and API management address different needs, and teams can combine them. Google Cloud’s architecture guidance puts it plainly: “An API management platform and a communication protocol like MCP solve different architectural problems.” That is vendor guidance, not a universal requirement.
| Situation | Candidate pattern | What it provides |
|---|---|---|
| A specific internal or third-party API has no suitable MCP server | Custom function tool | A focused adapter with a natural-language description of its purpose, parameters and returns. |
| Tools need to be reused across models or modular agent components | MCP | A standardized interaction interface and tool discovery; it does not replace API-side access control or enterprise API lifecycle management. |
| Many APIs need centralized cataloging, security, usage monitoring or lifecycle controls | API management platform | Governance around API endpoints; it can sit behind an MCP interface. |
Choose based on interoperability, how specific the integration is, existing platform investment, governance and audit needs, observability, and how much tool context the model must handle. MCP can standardize the agent-to-tool interface; API management can govern the APIs behind it. Neither choice removes the need for a well-designed API contract.
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How should you secure APIs used by AI agents?
Authorization must be enforced by the server and downstream service on every invocation. A model instruction such as “do not access this account” is not an authorization boundary. Use credentials scoped to the task and principal, record who or what the action represents, and maintain an audit trail.
Authenticate the user and authorize each call
OpenAI’s current MCP plugin guide says read-only anonymous operation can be possible, but customer-specific data and write actions should authenticate users. For the authenticated MCP integration described in that guide, the requirements include OAuth 2.1 conforming to the MCP authorization specification, resource metadata, authorization-server discovery, propagation of the OAuth resource parameter, and a client registration approach. Per-tool security declarations distinguish anonymous from OAuth-protected tools. The server must still verify token and scope information at each invocation. These are product-specific details and can change; check the target client and current specification when implementing.
Use delegated, scoped credentials and audit actions
The IETF draft characterizes a credential spanning an entire API as a poor fit for agent use, pointing instead toward narrow, short-lived, revocable credentials with delegation recorded. AWS Prescriptive Guidance similarly recommends purpose-generated, explicitly scoped downstream tokens, logging and auditing access, and avoiding propagation of user credentials through the agent system. Keep the acting identity clear in the audit record.
Do not rely on prompts as the policy checkpoint
MCP provides a standardized execution surface, but standardization alone does not ensure that each call is checked against organizational policy. In an April 22, 2026 developer post, Microsoft described an internal red-team evaluation of 60 prompts—45 adversarial and 15 valid—in which prompt-only safety instructions produced a 26.67% policy-violation rate. That is Microsoft’s result from its evaluation, not a general rate for agents or systems. The post described Microsoft’s Agent Governance Toolkit as Public Preview at publication.
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