Building an LLM-agnostic SDK that eliminates 98% of boilerplate code for crypto social data integration
LLM integration shouldn't require hundreds of lines of repetitive boilerplate code. Yet that's exactly what we found when building applications that connect AI assistants to real-time crypto social data.
The solution? A lightweight SDK that reduces integration complexity from 260 lines to just 5 lines - a 98% reduction in code while maintaining full functionality and future-proofing.
The Problem: Integration Hell
When building LLM applications that need access to crypto social intelligence, developers face a common challenge. Here's what connecting an AI assistant to LunarCrush's social data typically looked like:
// The old way: 260+ lines of boilerplate
async function integrateWithLunarCrush() {
// 20 lines: MCP client setup
const transport = new SSEClientTransport(new URL(`https://lunarcrush.ai/sse?key=${apiKey}`));
const client = new Client(
{ name: 'crypto-assistant', version: '1.0.0' },
{ capabilities: { tools: {} } }
);
await client.connect(transport);
// 15 lines: Tool discovery & schema handling
const { tools } = await client.listTools();
const validatedTools = tools.filter(/* validation logic */);
// 50 lines: Tool selection orchestration
const orchestrationPrompt = createPromptWithTools(tools);
const llmResponse = await llm.generateContent(orchestrationPrompt);
const parsedChoices = parseToolChoices(llmResponse);
// 75 lines: Tool execution management
const results = [];
for (const choice of parsedChoices) {
try {
const result = await client.callTool({
name: choice.name,
arguments: choice.args
});
results.push(processResponse(result));
} catch (error) {
results.push(handleError(error));
}
}
// 100 lines: Response parsing, cleanup, error handling
const finalData = aggregateResults(results);
await client.close();
return finalData;
}
This pattern repeated across every project. Worse, any changes to the MCP server required updates across all implementations. The maintenance burden was becoming unsustainable.
The Solution: LLM-Agnostic SDK Design
We built @jamaalbuilds/lunarcrush-mcp with three core principles:
1. Zero Hardcoding
The SDK dynamically discovers all available tools and their schemas. No hardcoded tool definitions or parameter specifications. When LunarCrush adds new data sources or modifies existing ones, the SDK automatically adapts.
2. LLM Agnostic
Instead of building OpenAI-specific or Anthropic-specific helper methods, we provide raw schemas that developers format for their chosen LLM. This eliminates maintenance burden and supports any current or future LLM provider.
3. Thin Wrapper Philosophy
The SDK is a minimal layer over the Model Context Protocol. Error handling, validation, and business logic remain with the MCP server where they belong.
The Implementation
Here's the complete integration with our SDK:
// The new way: 5 lines total
import LunarCrushMCP from '@jamaalbuilds/lunarcrush-mcp';
const mcp = new LunarCrushMCP('your-api-key');
await mcp.connect();
const tools = mcp.getToolsWithDetails();
const result = await mcp.callTool('Topic', { topic: 'bitcoin' });
await mcp.disconnect();
That's it. 260 lines reduced to 5 lines - a 98% reduction in code complexity.
Enhanced Schema Exposure for LLMs
The key innovation is getToolsWithDetails(), which provides LLM-friendly tool information:
const toolsData = mcp.getToolsWithDetails();
console.log(toolsData[0]);
/*
{
name: "Topic_Time_Series",
description: "Get historical time series metrics...",
schema: {
type: "object",
properties: { ... },
required: ["topic"]
},
parameterInfo: {
required: ["topic"],
optional: ["metrics", "interval"],
types: {
topic: "string",
metrics: "array",
interval: "enum"
},
enums: {
interval: ["1d", "1w", "1m", "3m", "6m", "1y", "all"]
}
}
}
*/
This enhanced information helps LLMs understand parameter formatting requirements, leading to higher success rates in function calling.
Real-World LLM Integration Examples
OpenAI Function Calling
import OpenAI from 'openai';
import LunarCrushMCP from '@jamaalbuilds/lunarcrush-mcp';
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const mcp = new LunarCrushMCP(process.env.LUNARCRUSH_API_KEY);
await mcp.connect();
// Format tools for OpenAI (developer controls formatting)
const tools = mcp.getToolsWithDetails();
const functions = tools.map(tool => ({
name: tool.name,
description: tool.description,
parameters: tool.schema
}));
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: 'What is Bitcoin trending at?' }],
functions,
function_call: 'auto'
});
// Execute function call through SDK
if (response.choices[0].message.function_call) {
const { name, arguments: args } = response.choices[0].message.function_call;
const result = await mcp.executeFunction(name, args);
console.log('Bitcoin data:', result);
}
Google Gemini Integration
import { GoogleGenerativeAI } from '@google/generative-ai';
import LunarCrushMCP from '@jamaalbuilds/lunarcrush-mcp';
const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const mcp = new LunarCrushMCP(process.env.LUNARCRUSH_API_KEY);
await mcp.connect();
// Enhanced prompt with detailed parameter information
const toolsData = mcp.getToolsWithDetails();
const prompt = `You have access to these LunarCrush tools:
${toolsData.map(tool => {
const { name, description, parameterInfo } = tool;
return `${name}: ${description}
- Required: ${parameterInfo.required.join(', ') || 'none'}
- Optional: ${parameterInfo.optional.join(', ') || 'none'}
- Types: ${JSON.stringify(parameterInfo.types)}
- Valid enums: ${JSON.stringify(parameterInfo.enums)}`;
}).join('\n\n')}
Analyze Bitcoin's market performance using appropriate tools.`;
const model = genAI.getGenerativeModel({ model: 'gemini-pro' });
const result = await model.generateContent(prompt);
// Parse LLM response and execute chosen tools
const response = JSON.parse(result.response.text());
for (const choice of response.selected_tools) {
const toolResult = await mcp.callTool(choice.name, choice.arguments);
console.log(`${choice.name} result:`, toolResult);
}
Why This Matters for Crypto Development
Access to real-time social intelligence is becoming crucial for crypto applications:
Social Sentiment Analysis: Track community mood and sentiment shifts
Trend Detection: Identify emerging narratives before they go viral
Risk Assessment: Monitor social indicators alongside technical analysis
Creator Insights: Analyze influence patterns and engagement metrics
LunarCrush processes over 100 million social interactions daily across Twitter, Reddit, YouTube, and other platforms. This SDK makes that data easily accessible to any LLM-powered application.
Get your LunarCrush API key at lunarcrush.com/pricing
Use my discount referral code JAMAALBUILDS to receive 15% off your plan.
Complete documentation and examples available at the npm package page.
The Bigger Picture
This SDK represents a broader trend in AI development: the need for lightweight, maintainable integrations that don't assume specific LLM providers or lock developers into particular frameworks.
As the Model Context Protocol gains adoption, we expect to see more tools designed with similar principles:
- Dynamic discovery over hardcoded definitions
- LLM agnostic design over provider-specific implementations
- Thin wrappers over monolithic libraries
The goal is enabling developers to build AI applications without getting bogged down in integration complexity.
Try It Today
The SDK is production-ready and available now. Whether you're building trading bots, social sentiment analyzers, or crypto research tools, LunarCrush's social intelligence can enhance your application with just a few lines of code.
Links:
Package: @jamaalbuilds/lunarcrush-mcp
Documentation: LunarCrush API Docs
GitHub: lunarcrush-mcp-sdk
What will you build with real-time crypto social intelligence?
Built by Danilo Jamaal for the LunarCrush community. Contributing to the future of LLM-powered crypto applications.
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