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Designing APIs That AI Agents Can Use Reliably

•
10 min read
•By Sanjay Krishnamurthy
astrologyAI AgentsMCPAPI DesignDeveloper Guide

Learn how to design APIs that AI agents consume effectively. Tool schemas, predictable responses, MCP integration, and patterns for LLM-friendly API design.

Designing APIs That AI Agents Can Use Reliably

In 2026, your API has two consumers: human developers and AI agents. Gartner predicts 40% of enterprise applications will embed AI agents by the end of this year. Multi-agent system inquiries surged 1,445% between Q1 2024 and Q2 2025. The agentic AI market is projected to reach $52 billion by 2030.

If your API was designed for human developers reading documentation and writing integration code, it probably works poorly for AI agents that discover, interpret, and call APIs programmatically. The requirements are different. The expectations are different. And the consequences of poor design are different.

This guide covers the design patterns that make APIs reliable for AI agent consumption, drawn from real-world experience building APIs that serve both human developers and AI systems.

Why AI Agents Struggle with Most APIs

The Discovery Problem

Human developers find APIs through Google searches, documentation links, and word of mouth. AI agents find APIs through:

  • MCP (Model Context Protocol) servers that expose tool definitions
  • llms.txt files that describe API capabilities in machine-readable format
  • OpenAPI specifications that define endpoints, parameters, and responses
  • Tool registries and agent marketplaces

If your API has none of these, AI agents cannot find it. It effectively does not exist in the agentic ecosystem.

The Interpretation Problem

A human developer reads "yoga_id": 15 and looks up what yoga 15 means in a reference table. An AI agent receiving "yoga_id": 15 has no idea what to do with it. The number is meaningless without context.

Most APIs return data optimized for machine parsing (compact, minimal) rather than machine understanding (descriptive, contextual). These are different things. JSON is parseable by any language. But the semantic meaning of the values requires context that most APIs do not provide.

The Reliability Problem

AI agents need deterministic, predictable responses. If an API returns different field names depending on the input, uses inconsistent error formats, or changes behavior between versions without warning, the agent breaks. Human developers can adapt to inconsistency. AI agents cannot.

Design Principle 1: Rich, Descriptive Responses

Bad: Minimal Response

{
  "p": "Moon",
  "s": "Cn",
  "d": 15.42,
  "r": false
}

An AI agent cannot generate a meaningful reading from abbreviated field names and unlabeled values.

Good: Semantically Rich Response

{
  "planet": {
    "name": "Moon",
    "description": "Emotions, instincts, inner world"
  },
  "sign": {
    "name": "Cancer",
    "element": "Water",
    "modality": "Cardinal",
    "description": "Nurturing, protective, emotionally sensitive"
  },
  "degree": 15.42,
  "retrograde": false
}

An AI agent can now generate a meaningful interpretation: "Your Moon is in Cancer at 15 degrees. Cancer is a Water sign, suggesting deep emotional sensitivity and nurturing instincts."

The Pattern

Every response field should answer three questions:

  1. What is it? (clear, unabbreviated field name)
  2. What does it mean? (description or label for enumerated values)
  3. How is it used? (context for application)

This adds response size but dramatically improves AI usability. In practice, the additional bytes are negligible compared to the LLM context window they save (the agent does not need to look up meanings separately).

Design Principle 2: Consistent, Predictable Schemas

Consistent Field Names

Use the same field names across all endpoints. If birth chart data uses "planet_name", transit data should use "planet_name", not "body" or "celestial_object" or "name". Consistency lets agents reuse parsing logic across endpoints.

Consistent Error Format

Every error response should follow the same structure:

{
  "error": "Invalid date format. Expected YYYY-MM-DD."
}

Not sometimes { "error": "..." }, sometimes { "message": "..." }, and sometimes { "errors": ["..."] }. Pick one format. Use it everywhere. AI agents parse error responses too, and inconsistency causes cascading failures.

Consistent Data Types

If a field is a number, it is always a number. Not sometimes a number and sometimes a string representation of a number. Not sometimes null and sometimes missing entirely. Predictable types let agents write reliable parsing code.

Consistent Envelope

Either always wrap responses in an envelope or never wrap them. Not sometimes { "data": { ... } } and sometimes just { ... }. Consistency beats convention. The best pattern: return data directly (like Stripe and GitHub APIs do).

Design Principle 3: Self-Documenting via OpenAPI

Every Field Gets a Description

In your OpenAPI specification, every request parameter and response field should have a description:

planet_name:
  type: string
  description: "Planet name. Used for matching transits to natal positions."
  example: "Moon"

nakshatra:
  type: string
  description: "Nakshatra (lunar mansion) the Moon occupies. One of 27 Vedic nakshatras spanning 13d20m each."
  example: "Ashwini"

AI agents that consume OpenAPI specs use these descriptions to understand what fields mean and how to use them. Without descriptions, the agent guesses.

Meaningful Examples

Include example values that are realistic and informative. "example": "string" is useless. "example": "Ashwini" tells the agent what kind of value to expect.

Enum Documentation

For enumerated values, explain what each option means:

dosha_severity:
  type: string
  enum: ["None", "Mild", "Moderate", "Severe"]
  description: "Severity of the identified dosha. None means the dosha is not present. Mild means minor influence. Moderate requires attention. Severe suggests significant astrological concern."

An AI agent receiving "Moderate" now knows how to communicate this to a user.

Design Principle 4: MCP Server Integration

What MCP Is

MCP (Model Context Protocol) is the standard for AI agent-to-API communication. It defines how AI agents discover, authenticate with, and call external tools. Think of it as the USB standard for AI agent tooling.

Why MCP Matters

Without MCP, connecting an AI agent to an API requires:

  1. Reading the API documentation
  2. Writing custom integration code
  3. Defining tool schemas manually
  4. Handling authentication, errors, and retries
  5. Maintaining the integration as the API changes

With MCP, the agent discovers the API's capabilities, understands the parameters, and calls endpoints directly. The integration is automatic.

What an MCP Server Provides

An MCP server for your API exposes:

  • Tool definitions: What the API can do, with parameter descriptions
  • Authentication handling: How to authenticate requests
  • Response formatting: Structured responses the agent can process
  • Error handling: Consistent error patterns the agent can recover from

The Adoption Reality

Most APIs in 2026 do not have MCP servers. This means most APIs are invisible to the growing ecosystem of AI agents. For API providers, shipping an MCP server is a competitive advantage today and a survival requirement tomorrow.

Design Principle 5: llms.txt for Discoverability

What llms.txt Is

llms.txt is a standard (similar to robots.txt) that tells AI systems what an API or website does, in a format optimized for LLM comprehension. It sits at the root of your domain and provides a structured description of your capabilities.

Why It Matters

When an AI system (Claude, GPT, Perplexity) is asked "What API can I use for tarot readings?", it searches for and references llms.txt files to identify relevant providers. Without llms.txt, your API is less likely to be recommended.

What to Include

  • What domains your API covers
  • Key endpoint categories
  • Authentication method
  • Pricing model
  • Link to OpenAPI specification
  • Link to documentation

Design Principle 6: Deterministic Outputs

AI agents need predictable responses to build reliable workflows:

Same Input = Same Output Structure

Given the same parameters, your API should return the same response structure every time. Field names should not change. Field types should not change. Optional fields should be consistently present (as null) or consistently absent.

No Implicit Defaults That Change Behavior

If a parameter defaults to a specific value, document it explicitly. AI agents should not have to discover defaults through experimentation.

Pagination and Limits

Use consistent pagination patterns. If one endpoint uses offset/limit, all endpoints should use offset/limit, not some using page/per_page and others using cursor.

Applying These Principles: A Real Example

Consider an astrology API endpoint that returns a birth chart.

Before (Human-Developer Optimized)

{
  "planets": [
    { "id": 0, "lon": 187.53, "lat": -0.02, "spd": 0.98, "ret": false },
    { "id": 1, "lon": 92.17, "lat": 4.13, "spd": 13.21, "ret": false }
  ],
  "houses": [1, 32, 61, 92, 120, 150, 181, 212, 241, 272, 300, 330]
}

A human developer can parse this with a planet ID lookup table and degree-to-sign conversion. An AI agent sees meaningless numbers.

After (AI-Agent Optimized)

{
  "planets": [
    {
      "name": "Sun",
      "sign": { "name": "Libra", "element": "Air" },
      "degree": 7.53,
      "full_degree": 187.53,
      "house": 7,
      "retrograde": false,
      "description": "Core identity expressed through partnership, balance, and diplomacy"
    },
    {
      "name": "Moon",
      "sign": { "name": "Cancer", "element": "Water" },
      "degree": 2.17,
      "full_degree": 92.17,
      "house": 4,
      "retrograde": false,
      "description": "Emotional nature deeply rooted in home, family, and nurturing"
    }
  ],
  "houses": [
    { "number": 1, "sign": "Aries", "degree": 1.0, "description": "Self, identity, physical body" },
    { "number": 2, "sign": "Taurus", "degree": 32.0, "description": "Values, money, possessions" }
  ]
}

The AI agent can now generate a complete, accurate birth chart reading without any external lookups. The response is self-contained and semantically meaningful.

The Business Case for AI-Ready APIs

API providers that ship AI-ready features today capture the growing agent market:

  • AI agents that discover your API through MCP and llms.txt recommend it to developers and users
  • LLMs that reference your documentation cite your API in responses to developer questions
  • Agent marketplaces (which are emerging rapidly) index APIs with MCP servers first
  • Developers building AI products choose APIs that integrate with their agent stack natively

The cost of adding these features is small relative to the market access they unlock.

RoxyAPI: Built for AI Agents from Day One

RoxyAPI implements every principle in this guide:

  • Rich, descriptive responses with field-level semantic descriptions
  • Consistent schemas across all six spiritual domains
  • Interactive OpenAPI documentation (Scalar-powered) with descriptions on every field
  • MCP server for direct AI agent integration
  • llms.txt for AI discoverability
  • Deterministic outputs with predictable response structures

Whether you are building an AI astrology chatbot, a multi-agent spiritual advisor, or an LLM-powered wellness app, the API is designed for your agent to consume directly.

Check the API documentation to see the response quality firsthand. View pricing to get started.

Frequently Asked Questions

Q: Do I need an MCP server if I already have REST endpoints? A: REST endpoints work for human developers writing integration code. MCP servers work for AI agents that discover and call APIs programmatically. In 2026, you need both. REST for traditional integrations, MCP for the growing agent ecosystem.

Q: How much work is it to make an existing API AI-friendly? A: The highest-impact changes are: adding field descriptions to your OpenAPI spec (hours), shipping an MCP server (days), and adding an llms.txt file (minutes). Restructuring responses for semantic richness requires more effort but delivers the most value for AI consumers.

Q: Which AI agents use MCP? A: Claude (via Claude Code and the desktop app), various open-source agent frameworks, and a growing number of enterprise agent platforms support MCP. The standard is gaining rapid adoption as the default for agent-to-tool communication.

Q: Does RoxyAPI have an MCP server? A: Yes. RoxyAPI ships with an MCP server, llms.txt, and OpenAPI documentation with field-level descriptions. All six spiritual domains (astrology, tarot, numerology, I-Ching, dreams) are available through MCP tool calls. View the documentation or check pricing.

Q: Will AI agents replace human developers as API consumers? A: Not replace, but augment. AI agents will increasingly handle routine API integration, data retrieval, and basic feature building. Human developers will focus on architecture, user experience, and complex logic. APIs that serve both audiences will capture the full market.

Build for the agentic future. Explore RoxyAPI products, check the API documentation, or view pricing.