ArchitectGBT 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., "@ArchitectGBT MCP Serverrecommend a model for a chatbot with low latency"
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.
ArchitectGBT MCP Server
AI model recommendations directly in your IDE. Compare 50+ models, get code templates, and optimize costs without leaving Cursor or Claude Desktop.
Try it Free → · Get API Key → · View All Models →
Resources: NPM Package · GitHub Repository
Quick Start
No signup, no API key, no configuration needed:
npx -y architectgbt-mcpGet 3 free AI recommendations per day to try it out.
Want unlimited access + code templates + advanced features? Upgrade to Pro → (starts at $15/mo)
Related MCP server: ai-compass
What is ArchitectGBT?
Choosing the right AI model shouldn't take hours of research. ArchitectGBT analyzes your project requirements and recommends the best model with actual cost estimates, not marketing fluff.
Instead of manually comparing pricing tables across OpenAI, Anthropic, Google, Meta, and Mistral, you get:
AI-powered recommendations based on your project requirements
Cost optimization with realistic usage estimates
Model comparison across 50+ models with real-time pricing
Production-ready code in TypeScript and Python
Direct IDE integration via Model Context Protocol (MCP)
This MCP server brings ArchitectGBT's intelligence directly into your IDE, so you never have to leave your coding flow.
New to ArchitectGBT? Explore the full platform →
Why This vs Cursor's Auto?
Cursor's Auto feature is great for quick model switching, but it doesn't solve the research problem:
ArchitectGBT solves:
Cost planning - Get realistic estimates before you build, not surprise bills after
Model discovery - Find models you didn't know existed (Mistral, Cohere, specialized models)
Requirements matching - Match your specific use case (context window needs, latency requirements, budget constraints)
Production templates - Get working integration code, not just model names
When to use Cursor Auto: Switching between models you already know When to use ArchitectGBT: Figuring out which model to use in the first place
Think of it as research before execution. ArchitectGBT helps you make the decision, Cursor Auto helps you execute it.
Features
Feature | Free | Pro |
Browse 50+ AI Models | Unlimited | Unlimited |
AI Recommendations | 3/day | Unlimited |
Code Templates | No | Unlimited |
Cost Calculator | No | Yes |
API Keys | No | Up to 5 |
Priority Support | No | Yes |
AI Model Recommendations
Get intelligent, personalized suggestions for your specific use case:
Smart analysis with detailed reasoning, pros/cons, and alternatives
Budget optimization (low/medium/high/unlimited budgets)
Priority matching (optimize for cost, speed, quality, or balanced)
Real cost estimates based on realistic token usage
Alternative suggestions if your top pick doesn't fit
Model Database (Always Free)
50+ AI models from OpenAI, Anthropic, Google, Meta, Mistral
Real-time pricing per 1M tokens (input + output)
Detailed specs (context windows, speed rankings, capabilities)
Provider filtering to compare similar models
Always updated with the latest model releases
Code Templates (Pro Only)
Production-ready integration code to ship faster:
Copy-paste ready for Anthropic, OpenAI, Google Gemini
TypeScript & Python support
Complete examples with installation, env setup, and error handling
Best practices including streaming, retries, and rate limiting
Saves hours of reading API docs
View all features on ArchitectGBT →
Installation
Choose your editor below. No API key needed to start — upgrade to Pro later for unlimited access.
Cursor IDE
Cursor has native MCP support built-in.
Option 1: Free (No API Key)
Create
.cursor/mcp.jsonin your project root:
{
"mcpServers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"]
}
}
}Restart Cursor IDE
Test it — Look for the connection icon in the bottom right, then ask:
"Show me all available AI models"
"Recommend an AI model for my chatbot project"
You're done. You get 3 free recommendations/day.
Option 2: Pro (Unlimited)
Upgrade to Pro → then:
Get your API key from ArchitectGBT Dashboard
Edit
.cursor/mcp.json:
{
"mcpServers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"],
"env": {
"ARCHITECTGBT_API_KEY": "agbt_your_key_here"
}
}
}
}Replace
agbt_your_key_herewith your actual API keyRestart Cursor IDE
Unlimited recommendations + code templates unlocked.
Claude Desktop
Option 1: Free (No API Key)
Open config file:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.json
Add this configuration:
{
"mcpServers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"]
}
}
}Restart Claude Desktop
Test it — Ask Claude:
"List all AI models from Anthropic"
"Recommend a model for document analysis"
You're set. You get 3 free recommendations/day.
Option 2: Pro (Unlimited)
Upgrade to Pro → then:
Get your API key from ArchitectGBT Dashboard
Edit the config file (same locations as above):
{
"mcpServers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"],
"env": {
"ARCHITECTGBT_API_KEY": "agbt_your_key_here"
}
}
}
}Replace
agbt_your_key_herewith your actual API keyRestart Claude Desktop
Unlimited access activated.
Other Editors
VS Code (with Continue extension)
Install Continue extension
Add to settings:
{
"mcp.servers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"]
}
}
}For Pro: Add "env": { "ARCHITECTGBT_API_KEY": "agbt_your_key" } after upgrading.
Zed Editor
Add to ~/.config/zed/settings.json:
{
"context_servers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"]
}
}
}For Pro: Add "env": { "ARCHITECTGBT_API_KEY": "agbt_your_key" } after upgrading.
Other MCP-Compatible Editors
Check ArchitectGBT Documentation for setup instructions.
Usage Guide
ArchitectGBT provides 3 MCP tools that work directly in your IDE:
1. list_models — Browse Models (Always Free)
Shows all 50+ AI models with pricing, specs, and capabilities.
Rate limit: Unlimited (no restrictions)
Example prompts:
"Show me all AI models"
"List models from Anthropic with pricing"
"What OpenAI models are available?"
"Compare Claude models"Response includes:
Model name and provider
Pricing per 1M tokens (input/output)
Context window size
Speed ranking
Key capabilities
Tip: Start here to explore options before asking for a recommendation.
2. get_ai_recommendation — AI-Powered Suggestions
Analyzes your project and recommends the best model with detailed reasoning.
Rate limit:
Free: 3 per day (resets at midnight UTC)
Pro: Unlimited
Example prompts:
"Recommend an AI model for a customer support chatbot handling 10k requests/day"
"What's the best model for analyzing legal documents with 50k tokens?"
"I need a fast, cheap model for sentiment analysis — recommend one"
"Find me a model with low latency and medium budget for a translation app"Response includes:
Top recommended model with match score
Detailed reasoning (why it's perfect for your use case)
Pros and cons
Estimated daily cost
Alternative suggestions
Upgrade prompt if you hit the free limit
Important: This is the only rate-limited tool. Browse models (list_models) unlimited.
Hit your limit?
Wait for daily reset (midnight UTC)
Upgrade to Pro → for unlimited access
3. get_code_template — Production Code (Pro Only)
Returns production-ready integration code for specific AI models.
Rate limit:
Free: Not available
Pro: Unlimited
Example prompts:
"Give me TypeScript code to integrate Claude"
"Show me how to use GPT-4 in Python"
"Get me a Gemini integration template"
"I need example code for calling Anthropic's API with streaming"Response includes:
Installation commands
Environment variable setup
Complete working code
Usage examples with error handling
Not a Pro user? Free users see an upgrade prompt with pricing.
Get Pro access → to unlock code templates.
Prompting Tips
Do | Don't |
Be specific about your use case | Ask generic "which is best?" questions |
Mention budget, volume, latency needs | Only ask for model names without context |
Start with | Waste recommendations on exploratory questions |
Use | Ask for code without mentioning language |
Pro Tip: You can say "use the list_models tool" or "call get_ai_recommendation" to explicitly trigger a specific tool.
Pricing & Limits
Tier | Recommendations | Code Templates | Cost Calculator | API Keys | Price |
Free | 3/day (MCP) + 10/month (web) | No | No | No | $0 |
Pro | Unlimited | Unlimited | Yes | Up to 5 | $15/mo |
Free Tier
Perfect for trying ArchitectGBT:
No signup required — works instantly
3 AI recommendations/day via MCP (IP-based rate limit)
Unlimited model browsing (
list_modelstool)10 recommendations/month on the web app
Full model database access on architectgbt.com
Limitations:
No code templates
No cost calculator
No API key access
No priority support
Best for: Exploring options, comparing models, trying ArchitectGBT for personal projects.
Pro Tier ($15/month)
For developers shipping to production:
Unlimited AI recommendations (MCP + web app)
Production-ready code templates (TypeScript + Python)
Cost calculator with realistic usage estimates
API key access (create up to 5 keys)
Priority email support
Advanced filters and comparison tools
Early access to new features
Best for: Professional developers, startups, teams building AI products.
Enterprise (Custom Pricing)
Need more? We offer:
Team collaboration (shared recommendations, templates)
SSO and advanced security
Priority support with SLA
Onboarding and training
Rate Limit Details
Endpoint | Free | Pro |
| Unlimited | Unlimited |
| 3/day | Unlimited |
Web app recommendations | 10/month | Unlimited |
| No | Unlimited |
Rate limit resets: Midnight UTC daily
Hit your limit?
Wait for reset (resets every 24 hours)
Use the web app (10 free/month)
Upgrade to Pro for unlimited access
Available Tools
This MCP server exposes 3 tools that your IDE's AI assistant can use:
1. list_models
Description: Browse all available AI models with pricing and specifications.
Parameters:
provider(optional): Filter by provider —"OpenAI"|"Anthropic"|"Google"|"Meta"|"Mistral"limit(optional): Max models to return (default: 50)
Returns:
List of models with name, provider, pricing (input/output per 1M tokens), context window, speed ranking
Rate limit: Unlimited (always free)
2. get_ai_recommendation
Description: Get AI-powered model recommendations based on your project requirements.
Parameters:
projectDescription(required): Your use case (e.g., "customer support chatbot")budget(optional):"low"|"medium"|"high"|"unlimited"(default: medium)priority(optional):"cost"|"speed"|"quality"|"balanced"(default: balanced)
Returns:
Top recommended model with match score
Detailed reasoning and analysis
Pros and cons
Estimated costs
Alternative suggestions
Rate limit:
Free: 3 per day (resets midnight UTC)
Pro: Unlimited
3. get_code_template
Description: Get production-ready integration code for specific AI models.
Parameters:
model(required): Model name (e.g.,"Claude","GPT-4","Gemini")language(optional):"typescript"|"python"(default: typescript)
Returns:
Installation commands
Environment variable setup
Complete working code
Usage examples with error handling
Rate limit:
Free: Not available (shows upgrade prompt)
Pro: Unlimited
Troubleshooting
"Daily Limit Reached"
Problem: You've used your 3 free recommendations for today.
Solutions:
Wait for reset — Limits reset at midnight UTC (check countdown in response)
Use the web app — Get 10 more recommendations/month at architectgbt.com
Upgrade to Pro — Get unlimited access for $15/month → Upgrade here
"API Key Invalid"
Problem: Your API key isn't being accepted.
Solutions:
Check format — Keys start with
agbt_(32 characters total)Verify Pro status — API keys only work for Pro subscribers → Check subscription
Regenerate key — Create a new key at Settings
Check config — Ensure key is in
envsection of your MCP configRestart IDE — Changes require full restart
Example config:
{
"mcpServers": {
"architectgbt": {
"command": "npx",
"args": ["-y", "architectgbt-mcp@latest"],
"env": {
"ARCHITECTGBT_API_KEY": "agbt_abc123xyz..."
}
}
}
}MCP Server Not Loading
Problem: Tools aren't showing up in your IDE.
Solutions:
Check Node.js — Requires Node.js >= 18.0.0 (
node --version)Verify config — Check JSON syntax in your MCP config file
Restart IDE — Full restart required after config changes
Test manually — Run
npx -y architectgbt-mcp@latestin terminalCheck network — Verify you can access https://architectgbt.com/api/models
For Cursor users: Look for the connection icon in the bottom right corner
Models Not Showing
Problem: list_models returns empty or errors.
Solutions:
Check internet — Test connection to https://architectgbt.com
Verify API — Visit https://architectgbt.com/api/models in browser
Update package — Run with
@latesttag:npx -y architectgbt-mcp@latestClear cache — Delete
node_modules/.cacheand retry
Free Tier Not Working
Problem: Getting errors even without API key.
Solutions:
Remove API key — Free tier doesn't need
envsection at allUse basic config:
{ "mcpServers": { "architectgbt": { "command": "npx", "args": ["-y", "architectgbt-mcp@latest"] } } }Check IP limit — Each IP gets 3/day (shared across users on same network)
Create free account — Get 10 more/month on architectgbt.com
Still Having Issues?
Email support: hello@architectgbt.com
Documentation: architectgbt.com/docs/mcp-integration
Report bugs: GitHub Issues
Environment Variables
Variable | Required | Description |
| No (Pro only) | Your API key for unlimited access (starts with |
| No | Custom API endpoint (default: |
Example:
export ARCHITECTGBT_API_KEY="agbt_your_key_here"Links & Support
ArchitectGBT Platform
Homepage: architectgbt.com — Explore 50+ models, compare pricing, get recommendations
Browse Models: architectgbt.com/models — Full database with filters and search
Pricing: architectgbt.com/pricing — Upgrade to Pro for unlimited access
Get API Key: architectgbt.com/dashboard/settings — Manage keys and subscription
Documentation: architectgbt.com/docs/mcp-integration — Full MCP setup guide
Blog: architectgbt.com/blog — Model comparisons, benchmarks, tutorials
NPM Package
Package: npmjs.com/package/architectgbt-mcp
Version: 0.4.0
License: MIT
Support Channels
Email: hello@architectgbt.com
Bug Reports: GitHub Issues
Feature Requests: GitHub Discussions
Twitter/X: @architectgbt — Updates and announcements
Why Use ArchitectGBT?
Save hours of research — We track 50+ models from 5 providers so you don't have to.
Make better decisions — AI-powered recommendations based on your actual requirements, not marketing hype.
Ship faster — Production-ready code templates eliminate boilerplate and integration headaches.
Optimize costs — Realistic cost estimates prevent budget surprises in production.
Stay updated — We monitor pricing changes, new releases, and deprecations across all providers.
Contributing
This MCP server is part of the ArchitectGBT platform. We welcome contributions.
Development Setup:
git clone https://github.com/3rdbrain/architectgbt-mcp.git
cd architectgbt-mcp
npm install
npm run build
npm run devBefore submitting PRs:
Test with both Cursor and Claude Desktop
Update documentation if adding features
Follow existing code style
License
MIT © ArchitectGBT
Acknowledgments
Built with:
Model Context Protocol (MCP) by Anthropic
TypeScript, Node.js, and the amazing open-source community
Built for developers who ship fast
Available Tools
3 toolsget_ai_recommendationA
Get AI model recommendations for SOFTWARE PROJECTS ONLY (web apps, APIs, chatbots, AI features, etc.). DO NOT use for physical projects, crafts, or non-software tasks. Analyzes requirements and recommends the best AI model with pricing, reasoning, and alternatives. Free tier: 3 recommendations/day. Add ARCHITECTGBT_API_KEY for unlimited access.
| Name | Required | Description | Default |
|---|---|---|---|
| budget | No | Budget constraint for API costs | |
| prompt | Yes | Description of the SOFTWARE project you want to build (e.g., 'customer support chatbot for e-commerce', 'code review AI assistant', 'document analysis API') | |
| priority | No | What matters most for this project |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full disclosure burden. It discloses a rate limit ('3 recommendations/day'), an authentication requirement ('Add ARCHITECTGBT_API_KEY'), and the nature of the output (pricing, reasoning, alternatives). This gives agents clear behavioral expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is front-loaded with purpose and scope, then adds necessary details in a compact, efficient manner. Every sentence provides meaningful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking annotations and output schema, the description covers the tool's purpose, scope, rate limits, auth, and output content. It is sufficiently complete for an AI agent to use the tool correctly, with schema covering parameter details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so description does not need to explain parameters. It adds no extra meaning beyond the schema's existing descriptions of prompt, budget, and priority. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool provides AI model recommendations specifically for software projects, using a specific verb ('Get') and resource ('AI model recommendations'). The 'SOFTWARE PROJECTS ONLY' scope and exclusion of non-software tasks distinguishes it from sibling tools like get_code_template and list_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use (software projects) and when-not-to-use (physical projects, crafts, non-software) instructions. However, it does not name alternative tools or explain when to prefer siblings like list_models, so it lacks full alternative differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_code_templateA
Get ArchitectGBT's production-tested code templates for AI model integration (TypeScript & Python). These are battle-tested templates with proper error handling, type safety, and best practices - NOT generic code. Pro feature - requires API key. Free users: browse models unlimited, get 3 AI recommendations/day.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | The AI model name (e.g., 'Claude', 'GPT-4', 'Gemini') | |
| language | No | Programming language for the template |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses that the tool requires an API key (pro feature) and emphasizes template quality ('battle-tested', 'proper error handling'), but it does not specify what happens without an API key, response format, error behavior, or rate limits beyond the free-user note about recommendations, leaving gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loads the core purpose. The second sentence adds quality and access context, though the free-user note about recommendations is somewhat tangential for a template tool. Still, each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple getter tool with two well-documented parameters and no output schema, the description covers the essential use case, language options, and access restriction. It does not describe return format or error handling, but the tool's simplicity and the presence of a sibling for listing models reduce the need for more detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters ('model' and 'language'), so the schema already explains their meaning. The description mentions 'TypeScript & Python', matching the language enum, but adds no additional semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get ArchitectGBT's production-tested code templates for AI model integration' with specific languages (TypeScript & Python). It uses a specific verb ('Get') and resource ('code templates'), and distinguishes itself from siblings like 'get_ai_recommendation' and 'list_models' by focusing on templates rather than recommendations or model lists.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context by noting it's a 'Pro feature - requires API key' and mentions free-user limits, implying this tool is intended for paying users. It does not explicitly name alternatives or exclusion criteria, but the context makes it clear that this tool is for obtaining code templates, while siblings serve different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_modelsB
List available AI models with optional filtering by provider or capability.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of models to return (default: 50) | |
| provider | No | Filter by provider |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'list available AI models' without describing side effects (likely none), auth needs, rate limits, or the structure of returned data. It adds little beyond the tool's name, leaving the agent to guess about defaults and response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, one line long, with no filler. However, it includes the inaccurate 'capability' term, which slightly detracts from its precision, but overall it is appropriately brief.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should explain what the tool returns (e.g., model IDs, names, provider metadata). It doesn't. It also doesn't disclose default limit behavior or note the discrepancy around 'capability.' For a simple tool, it's under-specified regarding response structure and edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the schema has full descriptions for both parameters (100% coverage), the description introduces 'filtering by provider or capability,' but there is no 'capability' parameter in the schema. This is misleading and adds confusion. It omits the limit parameter and doesn't clarify the filtering semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'list' with the resource 'available AI models' and mentions optional filtering. This clearly differentiates it from sibling tools get_ai_recommendation and get_code_template, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context that this tool is for listing models and implies filtering options. It doesn't explicitly state when to avoid it or mention alternatives, but the sibling names make the use case obvious. There are no exclusions or prerequisites mentioned, which is acceptable for such a simple listing tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
v0.4.3- First observed
get_ai_recommendation - First observed
get_code_template - First observed
list_models
TDQS
Each tool has a clearly distinct responsibility: listing models, getting a recommendation, and fetching a code template. Descriptions are explicit about boundaries, especially the warning on get_ai_recommendation to not use it for non-software tasks.
All tool names follow a consistent verb_noun pattern in lowercase snake_case: get_ai_recommendation, get_code_template, list_models. No mixed conventions or vague verbs.
Three tools is within the typical well-scoped range and perfectly sized for a server that offers two core actions (recommend and template) plus a discovery/list function. Each tool earns its place.
The surface covers the full workflow for AI model integration: discover models (list_models), get a tailored recommendation (get_ai_recommendation), and obtain integration code (get_code_template). There are no obvious dead ends for the domain.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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