oyi77/1ai-skills/agent-reach

Agent Reach

When to Use

Trigger phrases:

• "agent reach"
• "Scrape Twitter tweets, threads, search results without API keys"
• "Read Reddit posts, comments, subreddit feeds"
• "Get YouTube video transcripts and metadata"

• Scrape Twitter tweets, threads, search results without API keys
• Read Reddit posts, comments, subreddit feeds
• Get YouTube video transcripts and metadata
• Scrape XiaoHongShu (Little Red Book) posts
• Monitor Bilibili video content
• Search across platforms for competitive intelligence
• Gather social proof and sentiment data
• Research trending content for viral creation
• When NOT to use: When you already have API access (use native tools), when the platform blocks scraping ethically, when data doesn't need real-time freshness

When NOT to Use

• When a simpler HTTP client would suffice
• For internal tools that do not need cross-platform compatibility
• When the tool is used by a single agent in a single context

Overview

Agent Reach implements a Model Context Protocol server for Model Context Protocol.

Architecture

• Server — MCP-compliant server exposing tools and resources
• Transport — stdio or HTTP transport layer
• Tools — Callable functions with JSON Schema definitions
• Resources — Readable data sources with URI-based access

Setup

1. Install the MCP server package
2. Configure environment variables and credentials
3. Register the server in MCP client configuration
4. Test tool invocations and resource access

Configuration

• Server name and version
• Transport type (stdio, SSE, HTTP)
• Tool definitions with input/output schemas
• Resource URI patterns
• Authentication and rate limiting

Integration

• Compatible with Claude, Cursor, and other MCP clients
• Supports streaming responses for large payloads
• Handles errors with standard MCP error codes

Anti-Rationalization Table

// Example: MCP server tool definition
import { McpServer } from "@modelcontextprotocol/sdk";
const server = new McpServer({ name: "my-tools", version: "1.0.0" });
server.tool("search", { query: z.string() }, async ({ query }) => {
  const results = await search(query);
  return { content: [{ type: "text", text: JSON.stringify(results) }] };
});

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run agent reach workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• [ ] All steps executed successfully
• [ ] Results validated against acceptance criteria
• [ ] Error handling tested with edge cases
• [ ] Documentation updated with findings