Glama Connector for AI agents.
8 live capabilities
Route prompts through a unified gateway and discover new capabilities on the fly.
Waiting for input…
Why people use Glama
Glama for AI Agents: Stop Manually Managing LLM API Keys
Glama fixes this by putting everything behind a single gateway. You can list models, check their specs, and run prompts through one unified pipe. It turns a messy infrastructure problem into a clean, programmable flow.
What Vinkius changes
You get a single point of entry for your entire AI infrastructure.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Finding CRM capabilities for a new workflow
An analyst wants to find a CRM integration.
- Real-world use case 02
Checking model limits before a long task
A developer wants to see if a model can handle a large file.
- Real-world use case 03
Routing prompts to the best model
An app needs to run a complex reasoning task.
Complete set · 8capabilities
The complete Glama capability set.
These are the exact actions your AI can choose when you ask it to work with Glama.
01—04
4 capabilities in this set.
Part of 8 available through Glama.
- 01 Capability
Glama get hosted instances
Access the private hosted MCP instances tied to your specific account. This keeps your internal capabilities secure and reachable.
- 02 Capability
Glama get mcp attributes
See the categories and filtering tags used in the Glama registry. Use this to narrow down your search for specific types of capabilities.
- 03 Capability
Glama get mcp server info
Pull the exact parameters and setup steps for a specific MCP. This makes it easy to get a new capability running quickly.
- 04 Capability
Glama send telemetry
Send usage data and execution metrics back to the Glama backend. Use this to monitor your agent's performance and costs.
05—08
4 capabilities in this set.
Part of 8 available through Glama.
- 05 Capability
Glama get gateway model details
Get specific details like pricing and context windows for models in the Glama gateway. This helps you plan your costs and limits before running a task.
- 06 Capability
Glama get gateway models
See every AI model currently supported by the Glama gateway. Use this to audit your available model options in one list.
- 07 Capability
Glama list mcp servers
Search the global directory to find new Connectors for your agent. This helps you discover new capabilities without manual searching.
- 08 Capability
Glama run gateway chat
Send a prompt to a specific model through the Glama proxy network. This lets you swap models instantly without changing your code.
Set up in minutes
One URL. Then ask Glama to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Glama from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Glama, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Glama for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Glama URL.
- Step 03
Save and start
Save the connection and enable Glama in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"glama": {
"url": "https://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Glama
Open Agent mode in chat and ask: "Using Glama, help me...". 8 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"glama": {
"url": "https://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Glama
Ask Copilot: "Using Glama, help me...". 8 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"glama": {
"url": "https://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Glama
Open Cascade and ask: "Using Glama, help me...". 8 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"glama": {
"url": "https://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Glama
Ask Cline: "Using Glama, help me...". 8 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add glama --transport http "https://edge.vinkius.com/vk_preview_3I3xkcQ0Lir2EbUp0HqzccT0Pcn6Iy0qjp9A5LCu/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Glama
Ask Claude: "Using Glama, show me...". 8 tools are ready
Where the request belongs
Work Glama can move forward.
For the AI infrastructure engineer who's tired of manual API management and the DevOps lead who needs to manage a fleet of models without the headache of multiple keys.
AI Infrastructure Engineer
Manages model routing and capability discovery for production agents.
DevOps Engineer
Automates the deployment of private MCP instances across different environments.
LLMOps Specialist
Monitors model performance and telemetry to optimize costs and latency.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsSmithery
AI MCP registry: discover, search, and connect MCP servers to your agents via Smithery.
Alpic
AI MCP infrastructure: deploy, manage, and monitor MCP servers programmatically via agents.
Vinkius AI Catalog
Give your AI agent full access to the Vinkius MCP catalog. Search, browse, and discover MCPs. their capabilities, debugger grades, FAQs, and documentation. all programmatically.
TrueFoundry
Universal LLM Gateway & ML deployment hub: invoke 1000+ proxy models and manage MCP service instances natively.
Prisma Access
Connect Prisma Access to any AI agent via MCP.
Prisma Cloud
Connect Prisma Cloud to any AI agent via MCP.
Bring your own AI
Change the model, client or framework. Keep Glama connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Glama.
The practical details behind the request, access and result.
What is the Glama MCP?
The Glama MCP is a bridge that connects your AI agent to a large directory of capabilities and a unified gateway for various AI models.
Can I use multiple models with Glama?
Yes, you can route prompts to different models through the Glama gateway without having to manage separate API keys for each one.
How do I find new capabilities for my AI agent?
You can search the global registry directly using the Glama MCP to see what capabilities are available and what they do.
Can I keep my private Connectors secure?
Yes, the Glama MCP allows you to fetch and use private hosted instances that are tied specifically to your account.
How do I see model pricing with Glama?
You can query the gateway to see specific details like pricing and context windows for any model before you run your tasks.
Can I track my agent's usage?
Yes, the Glama MCP includes capabilities to send telemetry and execution metrics back to the backend so you can monitor performance.
Can I test alternative AI models entirely within the terminal using the Glama integration?
Yes. Capabilities like glama_get_gateway_models list available OpenAI-compatible proxies, and glama_run_gateway_chat allows your Vinkius agent to run text completions outside itself natively.
Does the Glama server provide telemetry data back to the registry?
Yes. Active MCP usage events can be logged seamlessly applying the glama_send_telemetry capability in specific sequences to inform publishers about proxy executions.
Are private hosted instances queryable?
Yes. By executing glama_get_hosted_instances, your agent limits queries exclusively to private proxies explicitly belonging to your linked environment.
One connection away
Give your agent a direct line to Glama.
Connect Glama once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available