How to Use the Fountain MCP in Mastra AI

Q: How do I install the Fountain MCP Server in Mastra AI?

Run npm install @mastra/mcp@latest and configure a new MCPClient with your Vinkius endpoint URL. Mastra auto-detects the Streamable HTTP transport.

Q: Can Mastra AI require human approval before modifying a Fountain applicant?

Yes. You can enable requireToolApproval on your agent. It pauses the workflow before executing sensitive actions on any candidate.

Q: What happens if the Fountain API rate limits my Mastra AI workflow?

Mastra automatically applies exponential backoff. If listapplicants fails due to rate limits, the workflow waits and retries without dropping the execution state.

Q: How do I pass Fountain tools to my Mastra agent?

Call mcpClient.listTools() and spread the results directly into your agent's tools array. The agent then decides when to call listopenings based on your workflow logic.

Build resilient hiring workflows in Mastra AI that automatically retry failed Fountain API calls and handle complex routing.

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Connect Fountain MCP to Mastra AI

Create your Vinkius account to connect Fountain 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 Fountain with Mastra AI

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Automate Funnel Routing with Mastra AI

The `list_applicants` tool feeds raw candidate data into your Mastra AI workflow engine. Your agent evaluates the applicant's profile and checks the current pipeline via `list_funnel_stages` over the MCP connection. If a candidate stalls, the framework's conditional branching logic takes over. It can trigger a retry, re-route the candidate to a different stage, or notify an administrator if the pipeline breaks.

Schedule Interviews via MCP Server

The `list_interview_sessions` tool pulls the calendar state for your active `list_job_posts`. Your agent checks availability and attempts to match candidates based on their `get_applicant` profile. Network timeouts happen when querying massive HR databases. Mastra handles this by applying exponential backoff to your MCP client requests, ensuring you never drop an interview session due to a temporary API failure.

Monitor Hiring Goals and Worker Status

The `list_hiring_goals` tool provides the targets, while `list_workers` returns the actual hired headcount. Your agent compares these two datasets to determine if a specific branch needs more staff. You pass these tools into your agent using `mcpClient.listTools()`. If the numbers fall behind, the workflow automatically pulls `get_opening_details` to verify the job is still active before escalating the issue to a human manager.

Setup guide

Set up Fountain 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 Fountain 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: "fountain-mcp-client",
  servers: {
    "fountain-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Fountain 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 Fountain. 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 Fountain MCP in Mastra AI

Run `npm install @mastra/mcp@latest` and configure a new `MCPClient` with your Vinkius endpoint URL. Mastra auto-detects the Streamable HTTP transport.

Yes. You can enable `requireToolApproval` on your agent. It pauses the workflow before executing sensitive actions on any candidate.

Mastra automatically applies exponential backoff. If `list_applicants` fails due to rate limits, the workflow waits and retries without dropping the execution state.

Call `mcpClient.listTools()` and spread the results directly into your agent's tools array. The agent then decides when to call `list_openings` based on your workflow logic.

This server handles sensitive hiring pipelines and worker profiles via `list_workers`. Vinkius runs the connection through a zero-trust architecture, ensuring your workflow tokens never leak to unauthorized external networks.

Use it with your favorite AI tools

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