An AI agent needs an API whose operations it can identify, call with valid arguments, interpret from structured results, and use safely when an action has consequences. That means clear machine-readable descriptions, focused tools, recoverable errors, and server-enforced authorization—not every endpoint exposed as a tool, a large default response, or necessarily an MCP server.
What does an AI agent need from an API?
An agent selects operations from the names, descriptions, and schemas presented to it, then uses their results to decide what to do next. Those descriptions are part of the runtime interface, not just documentation. As the authors of the IETF’s June 2026 informational Internet-Draft, Design Considerations and Profile for HTTP APIs Consumed by AI Agents, put it: “The description is input.” If a tool is vague or its schema does not match what the server accepts, the agent may choose the wrong operation or construct invalid arguments.
The draft offers a useful design profile, not a finalized protocol or mandatory standard. Its focus is API behavior and descriptions; it does not define agent identity, authentication, authorization, tool-calling protocol internals, planning, or evaluation. Treat its recommendations accordingly, and keep the security boundary on the server side.
How do I make an API agent-friendly?
Give each operation a precise, machine-readable contract
Use a stable, meaningful operation identifier and a concise description of what the operation does and when it is appropriate. Define parameter types, required fields, allowed values, and return fields explicitly. Document the meaning of returned values and keep the description synchronized with the implementation: a generated tool layer can expose only what its source description contains.
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Prefer structured affordances over instructions buried in prose. Examples include explicit enums, valid next-action links, a retryable flag, dry-run support, and metadata indicating that an operation requires confirmation. This gives an agent something it can act on without having to infer policy or intent from a warning sentence.
Curate the tool surface around tasks
Do not expose every low-level endpoint by default. Group or compose operations when doing so makes a bounded, common task simpler and safer, while keeping authorization, resource meaning, auditability, and partial failures visible. For batch operations, report an outcome for each item. If an agent cannot reasonably know an opaque identifier, accept a human-meaningful name or provide a lookup operation. Avoid exposing deprecated operations as current choices.
There is no universal ideal tool count. The IETF draft notes that empirical measurements suggest operation selection can degrade as a tool set grows into the hundreds, while acknowledging that effects vary. Google Cloud’s architecture guidance also recommends concise definitions, focused toolsets, and progressive disclosure. Evaluate the size and organization of the toolset with the target model and workflow.
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Return compact results that support the next step
Include fields needed for the current task and likely follow-up calls, with readable labels alongside opaque identifiers where possible. Represent values such as money with their currency. For collections, use bounded page sizes, stable ordering, cursor-based pagination, and a ready-to-use cursor or next-page link. Link to valid next operations when the resource supports them.
If clients need different levels of detail, offer field selection or concise and detailed response modes. Make clear which fields a concise response omits and how to request more detail; compactness should not conceal information needed for a correct decision.
Make errors recoverable
Use a consistent machine-readable error shape, such as HTTP Problem Details, and provide a stable application error code distinct from the HTTP status. State whether a retry is appropriate, include a retry delay when useful, and identify validation failures by field. A rate-limit error should distinguish a delayed retry from a request that must change; a validation error should say what needs correction.
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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.
Design writes for retries and consequences
Support client-supplied idempotency keys for side effects, and document their scope and retention. That lets a client safely repeat a submission when it cannot tell whether the first attempt succeeded, without unintentionally duplicating the effect.
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For expensive, high-impact, or hard-to-reverse actions, provide a preview or dry run and explicit risk metadata. Use a separate confirmation step where appropriate; offer cancellation or reversal when feasible. Make these behaviors machine-readable where possible rather than relying on the agent to infer permission requirements from prose.
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Give long-running work a status path
For work that may take more than a few seconds, return promptly with an operation identifier and status URL—often through HTTP 202—rather than holding the request open without guidance. The status resource should expose clear states, polling guidance and retry delay, completion links, and cancellation where supported. Authenticated callbacks or streaming can fit workflows that can use them.
Make API evolution visible to tools
Changes to an API description are changes to the agent’s available tools. Favor backward-compatible changes, version breaking ones, and do not silently change an operation’s meaning while keeping the same identifier. Mark deprecations in machine-readable metadata and link to replacements. Use a coherent versioning approach and compare successive API descriptions to detect breaking changes.
Publish a complete, low-noise API description, such as OpenAPI, and generate any model-facing documentation index from the same source as the human documentation. The IETF draft mentions llms.txt as a community convention, not a standard. Accept and propagate a correlation identifier so it can be logged alongside the acting identity.
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Does every API need an MCP server?
No. MCP, custom function tools, and API management address different needs, and can be combined. Google Cloud’s architecture guidance describes the distinction this way: “An API management platform and a communication protocol like MCP solve different architectural problems.” Use interoperability, integration specificity, existing platform investment, governance and audit needs, observability, and the amount of context exposed to the model to choose a pattern.
| Pattern | Consider it when | What it provides—and what it does not |
|---|---|---|
| Custom function tool | You are integrating one specific internal or third-party API that has no suitable MCP server. | A focused adapter with a natural-language description of its purpose, parameters, and returns. It is specific to the integration rather than a standard reusable interface. |
| MCP | You want reusable tools across models or modular agent components. | A standardized interaction interface and tool discovery. It does not replace API-side access control or enterprise API lifecycle management. |
| API management platform | You operate many APIs and need centralized cataloging, security, usage monitoring, or lifecycle controls. | Governance around API endpoints. It can sit behind an MCP interface; it is not itself the agent-to-tool communication protocol. |
Google Cloud’s guidance is vendor architecture advice, not a universal requirement. A particular API can be wrapped in a custom tool, made available through MCP, and governed by an API management platform when those layers solve distinct problems.
How should I secure APIs used by AI agents?
Enforce authorization at invocation time
Model instructions are not an authorization boundary. The server and downstream service must verify each call, including the token and its scopes, and enforce access to the requested resource and action. Use credentials scoped to the task and principal rather than a broad static credential for an entire API; prefer narrow, short-lived, revocable delegated credentials and record delegation.
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, 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, but the server still has to verify tokens and scopes on every invocation. These details are specific to the guide’s product behavior and can change; verify them against the target client and specification when implementing.
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AWS Prescriptive Guidance recommends purpose-generated, explicitly scoped downstream tokens, logging and auditing access, and avoiding propagation of user credentials through the agent system. Make clear which principal an action represents, and retain an audit trail that connects the action to that identity. Propagate a correlation identifier as well so related calls can be investigated together.
Do not rely on prompts as the only policy checkpoint
Microsoft’s April 22, 2026 developer post argues that MCP’s standardized execution surface does not by itself provide a policy checkpoint for every call. Microsoft reported 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. Those are results from Microsoft’s described evaluation, not a general rate for all agents or systems. The post described its Agent Governance Toolkit as Public Preview at publication.
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