Model ID Cheatsheet

Stop your AI coding agent from hallucinating outdated model names. This MCP server gives any AI assistant instant access to accurate, up-to-date API model IDs, pricing, and specs for 107 models across 19 providers.

Built in Go. Single 10MB binary. Zero external calls. Sub-millisecond responses. Auto-updated daily.

- model = "gpt-4-turbo"           # Hallucinated - doesn't exist anymore
+ model = "gpt-5.3-codex"         # Correct - verified against official docs

- model = "claude-3-opus-20240229" # Deprecated
+ model = "claude-opus-4-6"        # Current - latest Anthropic flagship

Quick Start

Pick one option below. You'll be up and running in under a minute.

Option A: Claude Code (one command)

claude mcp add --transport sse --scope user model-id-cheatsheet \
  https://universal-model-registry-production.up.railway.app/sse

Verify it works:

claude mcp list
# Should show: model-id-cheatsheet ... Connected

Then start a new Claude Code session and ask: "What's the latest OpenAI model?" - it will use the tools automatically.

Option B: Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "model-id-cheatsheet": {
      "url": "https://universal-model-registry-production.up.railway.app/sse"
    }
  }
}

Restart Cursor to pick up the change.

Option C: Windsurf

Add to Settings > MCP Servers (or edit ~/.codeium/windsurf/mcp_config.json):

{
  "mcpServers": {
    "model-id-cheatsheet": {
      "serverUrl": "https://universal-model-registry-production.up.railway.app/sse"
    }
  }
}

Option D: Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.model-id-cheatsheet]
command = "uvx"
args = ["mcp-proxy", "--transport", "sse", "https://universal-model-registry-production.up.railway.app/sse"]

Option E: OpenCode

Add to ~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "model-id-cheatsheet": {
      "type": "remote",
      "url": "https://universal-model-registry-production.up.railway.app/sse"
    }
  }
}

Option F: Any MCP Client

Connect to the SSE endpoint directly (no API key, no auth):

https://universal-model-registry-production.up.railway.app/sse

Or use the Streamable HTTP transport:

https://universal-model-registry-production.up.railway.app/mcp

Verify Your Setup

Once connected, try asking your AI assistant any of these:

• "What's the correct model ID for Claude Opus 4.6?"
• "Is gpt-4o still available?"
• "Compare gpt-5.2 vs claude-opus-4-6"
• "What's the cheapest model with vision?"

If the agent calls a tool like get_model_info or check_model_status before answering, it's working.

How It Works

Your AI agent gains 6 tools that it calls automatically before writing any model ID:

Resources

What Happens Under the Hood

1. You ask your agent to write code or answer a model question
2. The agent automatically calls the appropriate tool (e.g., get_model_info)
3. The server responds in sub-milliseconds with verified data (no external API calls)
4. The agent writes code with the correct, current model ID

The server instructions tell the agent: "NEVER use a model ID from your training data without verifying it first." This means the agent will always check before writing.

Real-World Examples

Writing an API call:

# You: "Call the OpenAI API with their best coding model"
# Agent calls: get_model_info("gpt-5.4")
response = client.chat.completions.create(
    model="gpt-5.4",  # Verified via model registry
    messages=[...]
)

Catching deprecated models:

# You: "Use gpt-4o for this task"
# Agent calls: check_model_status("gpt-4o")
# Agent: "gpt-4o is deprecated. I'll use gpt-5 instead."
response = client.chat.completions.create(
    model="gpt-5",  # Updated automatically
    messages=[...]
)

Finding the cheapest option:

# You: "Use the cheapest model that supports vision"
# Agent calls: list_models(capability="vision", status="current")
response = client.chat.completions.create(
    model="gpt-5-nano",  # $0.05/$0.40 per 1M tokens
    messages=[...]
)

Comparing options:

# You: "Should I use Claude or GPT for this?"
# Agent calls: compare_models(["claude-opus-4-6", "gpt-5.2"])
# Agent gets a side-by-side table and makes a recommendation

Resource Footprint

A common concern: "Will this slow down my agent or eat tokens?"

For comparison, a single web search costs more tokens than all 6 tool schemas combined.

Covered Models (107 total)

Current Models (79)

Legacy & Deprecated Models (30)