How to Use the Glama MCP in Mastra AI

Q: How do I add Glama search capabilities to a Mastra AI agent?

Install @mastra/mcp and instantiate the client with your Vinkius HTTP endpoint. Spread the tools like glamalistmcpservers into your agent configuration to let it find integrations.

Q: How do I query model options using a Glama MCP Server in Mastra AI?

Use glamagetgatewaymodels inside a workflow step to fetch the active model list. Your agent can then parse this list to select the cheapest or fastest option for the next step.

Q: Does Mastra AI support private instances hosted on Glama?

Yes. The glamagethostedinstances tool fetches the private servers assigned to your account. Mastra AI connects to these endpoints over secure transport layers automatically.

Q: How is my telemetry data secured when using the Glama MCP Server with Mastra AI?

The glamasendtelemetry tool only transmits technical execution metrics like latency and success rates. No proprietary workflow context or prompt text ever leaves your secure Vinkius sandbox environment.

Build reliable agentic workflows that discover and execute tools using Glama and Mastra AI.

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Connect Glama MCP to Mastra AI

Create your Vinkius account to connect Glama to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Glama with Mastra AI

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Autonomous Tool Discovery in Mastra AI

`glama_list_mcp_servers` searches the global directory to find tools that match your workflow's current failure state. If a step fails, your agent queries the directory to locate a recovery utility. Mastra AI registers these tools dynamically. The framework handles the transport negotiation automatically, feeding the newly discovered endpoints straight into your active agent's toolset.

Automated Failover for Model Routing

`glama_run_gateway_chat` runs isolated prompts through a single gateway proxy that handles multiple model backends. You don't have to write custom failover code for each provider. This MCP Server helps you manage model failures inside Mastra AI workflows. When a model rate-limits your workflow, Mastra AI catches the error, uses `glama_get_gateway_models` to find an alternative model, and retries the prompt instantly.

Dynamic Metadata Inspection

`glama_get_mcp_attributes` extracts the semantic categorization and filter tags of registered directory tools. Your workflow uses these attributes to validate that a tool matches your security policies before running it. This programmatic check prevents your agents from executing unverified code. By integrating this step into your Mastra AI pipeline, you build a strict gatekeeping process for external tools.

Setup guide

Set up Glama MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Glama tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "glama-mcp-client",
  servers: {
    "glama-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Glama Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Glama tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Glama transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Glama. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Glama MCP in Mastra AI

Install `@mastra/mcp` and instantiate the client with your Vinkius HTTP endpoint. Spread the tools like `glama_list_mcp_servers` into your agent configuration to let it find integrations.

Yes. You can configure Mastra AI to pause workflows and require manual approval before executing tools like `glama_run_gateway_chat` or installing new servers.

Use `glama_get_gateway_models` inside a workflow step to fetch the active model list. Your agent can then parse this list to select the cheapest or fastest option for the next step.

Yes. The `glama_get_hosted_instances` tool fetches the private servers assigned to your account. Mastra AI connects to these endpoints over secure transport layers automatically.

The `glama_send_telemetry` tool only transmits technical execution metrics like latency and success rates. No proprietary workflow context or prompt text ever leaves your secure Vinkius sandbox environment.

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