How to Use the Upstream Lens MCP in Mastra AI

Q: How does Mastra AI handle failure when fetching project details from Upstream Lens?

Mastra AI's workflow engine guarantees execution. If getpropertydetails fails, the system automatically retries the request using exponential backoff until it succeeds or exhausts attempts.

Q: Can I chain multiple data lookups from Upstream Lens and Mastra AI?

Absolutely. You can create a sequence where fetching listprojects is followed by calling listpropertyimagery, making sure both datasets are available before the workflow completes.

Q: If I need to list environmental projects using Upstream Lens and Mastra AI, what are the steps?

You simply define a step in your workflow that calls listprojects. The system handles passing this tool's result into subsequent steps for further processing.

Build reliable, multi-step workflows with Upstream Lens and Mastra AI to guarantee mission-critical data execution.

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Connect Upstream Lens MCP to Mastra AI

Create your Vinkius account to connect Upstream Lens 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 Upstream Lens with Mastra AI

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Perplexity

Guaranteed Data Collection Workflows

Mastra AI handles complex sequences. You can build a workflow that first calls `list_projects` to get all environmental projects, and if successful, automatically moves on to calling `get_property_details` for each one. The system includes automatic retries with exponential backoff, so network hiccups won't stop your critical data gathering process.

Structured Reporting Pipelines

You can use the built-in workflow engine to manage structured reports. For example, a flow might run `list_project_observations`, and if no observations are found, it triggers an admin notification step instead of failing. The ability to conditionally branch means your agent can check for specific metadata using `get_organization_info` before deciding the next set of required actions.

Time-Sensitive Data Management

Tracking changes is key. Mastra AI's workflow handles calls to `list_project_notes`, allowing you to filter by update date and ensure only recent records are processed. This reliable execution framework works great for pulling historical data like listing all portfolios via `list_portfolios`.

Setup guide

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

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

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

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Common questions about Upstream Lens MCP in Mastra AI

Mastra AI's workflow engine guarantees execution. If `get_property_details` fails, the system automatically retries the request using exponential backoff until it succeeds or exhausts attempts.

Absolutely. You can create a sequence where fetching `list_projects` is followed by calling `list_property_imagery`, making sure both datasets are available before the workflow completes.

This MCP Server touches project observation data, specifically via `list_project_observations`. The workflow needs to process structured text records which can include dates and detailed descriptions.

You simply define a step in your workflow that calls `list_projects`. The system handles passing this tool's result into subsequent steps for further processing.

Yes. To use the tools, you must configure your Mastra AI workflow with valid endpoint tokens that grant permission to read organizational metadata via `get_organization_info`.

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