How to Use the Serviceform MCP in Mastra AI

Q: How do I implement Serviceform and mastra-ai in a production environment?

You use the mastra-ai client setup, which supports OAuth for authentication. This makes building robust, high-uptime applications straightforward.

Q: What kind of tool approval does mastra-ai support with Serviceform?

It supports requireToolApproval. This means you can build a human-in-the-loop process where the workflow pauses and waits for confirmation before taking action, which is critical when dealing with lead data.

Build reliable, failure-proof automation workflows using our MCP Server for mastra-ai.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Serviceform MCP to Mastra AI

Create your Vinkius account to connect Serviceform to Mastra AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Serviceform with Mastra AI

Ask AI about this MCP

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Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Manage Complex Lead Data Flows

Set up a workflow that needs to check lead status across multiple steps. Your agent can call `get_lead` to verify details, and if the initial attempt fails, it automatically retries using exponential backoff. The whole process is contained within one command flow, ensuring that even tricky data states don't crash your production system.

Automated Form Validation Chains

Design a workflow to validate multiple forms. You can call `list_forms` first to get all available schemas, and then use those definitions to conditionally process user input. If validation fails on the second form, the entire workflow pauses for human review. This multi-step approach is exactly what you need when data quality is non-negotiable.

Structured Space Item Retrieval

Build a reliable process that pulls specific content from defined areas. The `get_space_items` tool retrieves structured data for a given space ID, which can then feed into multiple subsequent actions within your workflow. It handles the complexity of fetching related items and ensures all necessary context is available before proceeding.

Setup guide

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

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

const result = await agent.generate(
  "List recent Serviceform 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 Serviceform. 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.

Why Choose Vinkius

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Common questions about Serviceform MCP in Mastra AI

Yes. Because the MCP Server supports automatic retries, if fetching leads hits a temporary error, your workflow will automatically retry with exponential backoff until it succeeds or you hit a defined limit.

You use the `mastra-ai` client setup, which supports OAuth for authentication. This makes building robust, high-uptime applications straightforward.

It's built for that. You can define conditional branching logic: if a lead is found via `get_lead`, proceed to step B; otherwise, notify the admin.

It supports `requireToolApproval`. This means you can build a human-in-the-loop process where the workflow pauses and waits for confirmation before taking action, which is critical when dealing with lead data.

The server works heavily with captured lead details. Since you're building workflows that process and store this PII, monitoring access logs is mandatory.

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