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3rdbrain

ArchitectGBT MCP Server

by 3rdbrain

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

npm version License: MIT


Quick Start

No signup, no API key, no configuration needed:

npx -y architectgbt-mcp

Get 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:

  1. Cost planning - Get realistic estimates before you build, not surprise bills after

  2. Model discovery - Find models you didn't know existed (Mistral, Cohere, specialized models)

  3. Requirements matching - Match your specific use case (context window needs, latency requirements, budget constraints)

  4. 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)

  1. Create .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "architectgbt": {
      "command": "npx",
      "args": ["-y", "architectgbt-mcp@latest"]
    }
  }
}
  1. Restart Cursor IDE

  2. 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:

  1. Get your API key from ArchitectGBT Dashboard

  2. Edit .cursor/mcp.json:

{
  "mcpServers": {
    "architectgbt": {
      "command": "npx",
      "args": ["-y", "architectgbt-mcp@latest"],
      "env": {
        "ARCHITECTGBT_API_KEY": "agbt_your_key_here"
      }
    }
  }
}
  1. Replace agbt_your_key_here with your actual API key

  2. Restart Cursor IDE

Unlimited recommendations + code templates unlocked.


Claude Desktop

Option 1: Free (No API Key)

  1. Open config file:

    • Windows: %APPDATA%\Claude\claude_desktop_config.json

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

    • Linux: ~/.config/Claude/claude_desktop_config.json

  2. Add this configuration:

{
  "mcpServers": {
    "architectgbt": {
      "command": "npx",
      "args": ["-y", "architectgbt-mcp@latest"]
    }
  }
}
  1. Restart Claude Desktop

  2. 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:

  1. Get your API key from ArchitectGBT Dashboard

  2. 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"
      }
    }
  }
}
  1. Replace agbt_your_key_here with your actual API key

  2. Restart Claude Desktop

Unlimited access activated.


Other Editors

VS Code (with Continue extension)

  1. Install Continue extension

  2. 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?

  1. Wait for daily reset (midnight UTC)

  2. 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 list_models to explore

Waste recommendations on exploratory questions

Use get_ai_recommendation for decisions

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_models tool)

  • 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.

Get Pro Access →


Enterprise (Custom Pricing)

Need more? We offer:

  • Team collaboration (shared recommendations, templates)

  • SSO and advanced security

  • Priority support with SLA

  • Onboarding and training

Contact Sales →


Rate Limit Details

Endpoint

Free

Pro

list_models

Unlimited

Unlimited

get_ai_recommendation (MCP)

3/day

Unlimited

Web app recommendations

10/month

Unlimited

get_code_template

No

Unlimited

Rate limit resets: Midnight UTC daily

Hit your limit?

  1. Wait for reset (resets every 24 hours)

  2. Use the web app (10 free/month)

  3. 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:

  1. Wait for reset — Limits reset at midnight UTC (check countdown in response)

  2. Use the web app — Get 10 more recommendations/month at architectgbt.com

  3. Upgrade to Pro — Get unlimited access for $15/month → Upgrade here


"API Key Invalid"

Problem: Your API key isn't being accepted.

Solutions:

  1. Check format — Keys start with agbt_ (32 characters total)

  2. Verify Pro status — API keys only work for Pro subscribers → Check subscription

  3. Regenerate key — Create a new key at Settings

  4. Check config — Ensure key is in env section of your MCP config

  5. Restart 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:

  1. Check Node.js — Requires Node.js >= 18.0.0 (node --version)

  2. Verify config — Check JSON syntax in your MCP config file

  3. Restart IDE — Full restart required after config changes

  4. Test manually — Run npx -y architectgbt-mcp@latest in terminal

  5. Check 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:

  1. Check internet — Test connection to https://architectgbt.com

  2. Verify API — Visit https://architectgbt.com/api/models in browser

  3. Update package — Run with @latest tag: npx -y architectgbt-mcp@latest

  4. Clear cache — Delete node_modules/.cache and retry


Free Tier Not Working

Problem: Getting errors even without API key.

Solutions:

  1. Remove API key — Free tier doesn't need env section at all

  2. Use basic config:

    {
      "mcpServers": {
        "architectgbt": {
          "command": "npx",
          "args": ["-y", "architectgbt-mcp@latest"]
        }
      }
    }
  3. Check IP limit — Each IP gets 3/day (shared across users on same network)

  4. Create free account — Get 10 more/month on architectgbt.com


Still Having Issues?


Environment Variables

Variable

Required

Description

ARCHITECTGBT_API_KEY

No (Pro only)

Your API key for unlimited access (starts with agbt_)

ARCHITECTGBT_API_URL

No

Custom API endpoint (default: https://architectgbt.com)

Example:

export ARCHITECTGBT_API_KEY="agbt_your_key_here"

ArchitectGBT Platform

NPM Package

Support Channels


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.

Start exploring models now →


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 dev

Before submitting PRs:

  • Test with both Cursor and Claude Desktop

  • Update documentation if adding features

  • Follow existing code style


License

MIT © ArchitectGBT


Acknowledgments

Built with:


Built for developers who ship fast

Try Free · Get Pro · Read Docs

Available Tools

3 tools
get_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.

ParametersJSON Schema
NameRequiredDescriptionDefault
budgetNoBudget constraint for API costs
promptYesDescription of the SOFTWARE project you want to build (e.g., 'customer support chatbot for e-commerce', 'code review AI assistant', 'document analysis API')
priorityNoWhat matters most for this project

TDQS

A4.5/5.0
Behavior5/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
modelYesThe AI model name (e.g., 'Claude', 'GPT-4', 'Gemini')
languageNoProgramming language for the template

TDQS

A3.9/5.0
Behavior3/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of models to return (default: 50)
providerNoFilter by provider

TDQS

B3.4/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness2/5

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.

Parameters2/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

  1. 3 tool updatesv0.4.3
    • First observedget_ai_recommendation
    • First observedget_code_template
    • First observedlist_models

TDQS

A4.1/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness5/5

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

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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If you are the server author, to access and configure the admin panel.

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