x-agent MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@x-agent MCP Serversearch tweets about open source AI"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
@talocode/x-agent
Open-source X/Twitter growth agent — CLI, SDK, MCP, REST API, and AI agent skill packs.
npm install -g @talocode/x-agent
x-agent auth login --bearer YOUR_X_BEARER_TOKENFeatures
Search — tweets, users, trends, conversations
Post — tweets, replies, threads with AI drafting
Analyze — Phoenix algorithm scoring (19 engagement signals)
Monitor — watch keywords, accounts, trends
CLI — full terminal interface
SDK —
XAgentClientfor programmatic useMCP — 19+ tools for any MCP-compatible agent
REST API — HTTP server for remote integrations
Skills — agent skill packs for Claude Code, OpenCode, Cursor
Related MCP server: Twitter/X MCP Server
Quick Start
# Install
npm install -g @talocode/x-agent
# Configure X API credentials
x-agent auth login --bearer YOUR_BEARER_TOKEN
# Search tweets
x-agent search tweets "open source AI"
# Draft content
x-agent post draft --topic "building with MCP" --tone educational
# Score against Phoenix algorithm
x-agent analyze score "Your tweet text here"
# Start MCP server
x-agent mcp
# Start REST API
x-agent serveCLI Reference
Command | Description |
| Configure X API credentials |
| Check auth status |
| Search tweets by keyword |
| Search X users |
| Get tweet by ID |
| Get user profile |
| Get user's recent tweets |
| Get user's followers |
| Get user's mentions |
| View trending topics |
| Post a tweet |
| Generate AI drafts |
| Reply to a tweet |
| Improve tweet engagement |
| Phoenix algorithm scoring |
| Full AI analysis |
| Add a watch target |
| List watch targets |
| Check watch targets |
| Start MCP server |
| Start REST API server |
SDK Usage
import { XAgentClient } from '@talocode/x-agent'
const client = new XAgentClient({
tokens: { bearerToken: 'YOUR_TOKEN' }
})
const user = await client.getUser('talocode')
const score = client.scoreTweet('Your tweet text')
const drafts = await client.generateDrafts({ topic: 'AI', tone: 'educational' })MCP Tools
19 tools for AI agents: get_user, search_tweets, post_tweet, score_tweet, generate_drafts, improve_tweet, analyze_post, and more.
REST API
x-agent serve --port 4173
curl http://localhost:4173/v1/health
curl http://localhost:4173/v1/trends
curl -X POST http://localhost:4173/v1/analyze/score -H 'Content-Type: application/json' -d '{"text":"your tweet"}'Phoenix Algorithm
Scores posts against X's 19 engagement signals (favorite, reply, retweet, dwell, click, etc.) with weighted predictions and actionable recommendations.
Talocode ecosystem
Part of Talocode — open-source workflow layers for builders. Explore sibling projects:
Project | What it is |
Screen-aware voice command layer | |
AI chat & assistant | |
Local coding agent | |
MCP gateway & agent tool control plane | |
Context ingestion for persistent agents | |
Persistent agent memory | |
X growth intelligence | |
X reply opportunity intelligence | |
Crawler / SEO intelligence | |
Web extraction to structured data | |
Search layer for agents | |
Invoicing tools | |
Geo intelligence | |
UGC workflows | |
Open-source distribution tools | |
Builder stack platform | |
Trading intelligence | |
Browser automation for agents | |
Org home & control plane | |
Shared agent skills | |
X automation agent (this repo) | |
Launch tooling | |
CAD workflows | |
Work automation | |
Clip / video loops |
MCP-compatible agents integrate via each product's MCP server where available (Model Context Protocol).
More: github.com/talocode · talocode.site · docs.talocode.site
License
MIT
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Maintenance
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