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This guide covers best practices for using AI and Large Language Models (LLMs) to accelerate your development workflow with Paragraph’s API.

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Quick Context for LLMs

When working with AI assistants like Claude, ChatGPT, or Cursor, you can provide comprehensive API context by sharing our full documentation: 
https://paragraph.com/docs/llms-full.txt
Simply paste this URL or its contents into your LLM conversation to give it complete knowledge of Paragraph’s API endpoints, data models, and capabilities.

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MCP Server

The Paragraph MCP server (@paragraph-com/mcp) connects AI agents directly to the Paragraph API. Manage posts, search content, work with coins, and more from any MCP-compatible client. 
npx @paragraph-com/mcp
Works with Claude Code, Claude Desktop, Cursor, VS Code, and any MCP client. See the full MCP setup guide for client-specific instructions and configuration.

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CLI for Agents

The Paragraph CLI (@paragraph-com/cli) is designed for agent and programmatic usage. All commands support --json for structured output: 
npm install -g @paragraph-com/cli
PARAGRAPH_API_KEY=<your-api-key> paragraph --json post list
See the full command reference for details.

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Agent Skill

Install the Paragraph Agent Skill to teach AI agents (Claude Code, Cursor, etc.) how to use the CLI: 
npx skills add paragraph-xyz/skill
The skill includes working agreements, all commands with examples, JSON response shapes, and common patterns for chaining commands.

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Best Practices for AI-Assisted Development

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1. Provide Clear Context

When prompting AI tools, include:
  • Specific API endpoints you’re working with
  • Example responses or data structures
  • Your programming language and framework
  • Any authentication requirements

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2. Validate Generated Code

Always review AI-generated code for:
  • Error Handling: Add proper error handling for API responses
  • Rate Limiting: Implement appropriate rate limiting and retry logic
  • Type Safety: Verify types match the API’s expected request/response formats

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3. Iterative Development

  • Start with simple API calls and gradually add complexity
  • Test each integration point before moving to the next
  • Use the AI to explain error messages and suggest fixes

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4. Common Prompting Patterns

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For API Integration

"Help me integrate Paragraph's [endpoint name] endpoint in [language/framework].
I need to [specific use case]. Include error handling and type definitions."

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For Debugging

"I'm getting [error message] when calling Paragraph's [endpoint].
Here's my code: [code snippet]. What's wrong?"

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For Data Modeling

"Based on Paragraph's API, help me create [language] models/interfaces
for [specific data types] with proper type annotations."

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Example Workflow

  1. Initial Setup: Share the llms-full.txt context with your AI assistant
  2. Describe Your Goal: Explain what you want to build with Paragraph’s API
  3. Generate Boilerplate: Have the AI create initial client setup and authentication
  4. Implement Features: Work through each API endpoint you need
  5. Add Error Handling: Ask the AI to add comprehensive error handling
  6. Create Tests: Generate test cases for your integration
  7. Optimize: Request performance improvements and best practices

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Troubleshooting

If AI-generated code isn’t working:
  • Verify you’re using the latest API version
  • Check that all required headers are included
  • Ensure proper JSON formatting in request bodies
  • Review rate limits and implement appropriate delays

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Additional Resources