YAPS MCP Server

An MCP (Model Context Protocol) server that wraps Kaito's YAPS API to provide tokenized attention scores for X/Twitter accounts. This server enables LLMs to query credibility and influence metrics for any X account, with built-in caching and rate limiting.

Features

🔍 Influencer Trust Score: Fetch YAPS scores for any X/Twitter account with caching
🔄 Comparison Tool: Compare the credibility of two influencers with natural language summaries
📊 Daily Leaderboard: Top-10 crypto influencers automatically refreshed daily
🚦 Rate Limiting: Graceful handling of YAPS API's 100 calls / 5 min limit
💾 Redis Caching: 10-minute TTL for scores, 24-hour TTL for leaderboards

Getting Started

Prerequisites

Node.js 18 or higher
Redis server

Installation

Clone the repository

git clone https://github.com/yourusername/yap-mcp.git cd yap-mcp

Install dependencies

npm install

Create a .env file based on the following template:

# YAPS API (no API key needed - using public API with rate limits) YAPS_API_ENDPOINT=https://api.kaito.ai/api/v1/yaps # Redis cache REDIS_URI=redis://localhost:6379 # Server PORT=3000 NODE_ENV=development # YAPS cache TTL in seconds YAPS_CACHE_TTL=600 # 10 minutes # Leaderboard settings LEADERBOARD_CACHE_TTL=86400 # 24 hours

Build the project

npm run build

Start the server

npm start

For integration with LLM providers, use the stdio transport:

npm start -- --stdio

MCP Resources and Tools

Resources

yaps-score - YAPS score for a Twitter user
- Schema: yaps-score://{username}

Tools

get_yaps_score - Get YAPS score and summary for a Twitter user
- Input: { username: string }
- Output: JSON object with score and natural language summary
compare_scores - Compare YAPS scores between two Twitter users
- Input: { username_a: string, username_b: string }
- Output: JSON comparison with deltas and natural language verdict
leaderboard_today - Get today's top-10 YAPS leaderboard
- Input: {}
- Output: Array of top 10 accounts by 24h YAPS score

Using with OpenAI GPT

Example of using the YAPS MCP server with OpenAI:

import OpenAI from 'openai'; import { spawn } from 'child_process'; const openai = new OpenAI({ apiKey: 'your-api-key' }); // Start the MCP server as a child process const mcpProcess = spawn('node', ['dist/index.js', '--stdio'], { stdio: ['pipe', 'pipe', process.stderr] }); // Example function to call GPT with tools async function askGptWithTools(question) { try { const response = await openai.chat.completions.create({ model: 'gpt-4-turbo', messages: [{ role: 'user', content: question }], tools: [ { type: 'function', function: { name: 'get_yaps_score', description: 'Get YAPS tokenized attention score for an X/Twitter account', parameters: { type: 'object', properties: { username: { type: 'string', description: 'Twitter username (with or without @)' } }, required: ['username'] } } }, // Additional tools would be defined here ], tool_choice: 'auto' }); console.log(response.choices[0].message); // Process tool calls if any if (response.choices[0].message.tool_calls) { // Implementation for handling tool calls would go here } return response; } catch (error) { console.error('Error calling GPT:', error); throw error; } } // Example usage askGptWithTools('What is the YAPS score for @VitalikButerin?');

Performance and Availability

P95 tool latency < 300 ms (cache hit) / < 2 s (cache miss)
99% monthly availability with stateless containers
Prometheus metrics for monitoring calls and errors

API Rate Limits

The YAPS API is available as an open source API with the following limitations:

Default rate limit: 100 calls every 5 minutes
This MCP server implements caching and rate limiting to respect these constraints

Technical Details

Built with TypeScript and Node.js
Uses MCP SDK version 1.11.3
Express.js for HTTP handling
Redis for caching and rate limiting

License

MIT

YAPS MCP Server

YAPS MCP Server

Features

Getting Started

Prerequisites

Installation

MCP Resources and Tools

Resources

Tools

Using with OpenAI GPT

Performance and Availability

API Rate Limits

Technical Details

License

Related Resources

New MCP Servers

MCP directory API