# Agent Reach

> Use when universal internet scraper for AI agents. Read and search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu, LinkedIn, V2EX, RSS, web pages. Zero API fees. Use when agents need real-time social media data, content research, or trend monitoring.

- Skill: `oyi77/agent-reach` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/agent-reach`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/agent-reach/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/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

| Rationalization | Reality |
|---|---|
| "I will just use curl" | MCP handles auth, retries, streaming, and type safety. Use the SDK. |
| "One mega-server is simpler" | Single-responsibility servers are easier to debug and maintain. |
| "MCP is just a wrapper" | MCP enables cross-platform tool sharing. It is infrastructure, not overhead. |

```typescript
// 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
1. **Execute** — Run agent reach workflow with configured parameters
1. **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