How to Use the Tray.io MCP in LlamaIndex

Q: Can LlamaIndex search past operational states using Tray.io?

Yes. By indexing the outputs of listworkflowexecutions, you can query specific historical data points from any workflow run, effectively turning execution history into a searchable knowledge base.

Q: If I use LlamaIndex with Tray.io, can it find what connectors are available?

The listavailableconnectors tool provides the raw list, but LlamaIndex indexes this data. You can then ask semantic questions about connectivity rather than just running a programmatic function.

Q: What happens to user details when I use Tray.io and LlamaIndex?

When getauthenticateduser is called, the resulting user data is indexed. This allows your agent to later answer questions like 'Who was the last person who ran this automation?' using natural language search.

Build knowledge-augmented systems with LlamaIndex: Indexing Tray.io's operational data for semantic search.

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Connect Tray.io MCP to LlamaIndex

Create your Vinkius account to connect Tray.io to LlamaIndex 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 Tray.io with LlamaIndex

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Making workflow execution history searchable.

The `list_workflow_executions` tool lets your agent pull recent run history, which LlamaIndex indexes into a vector store. Instead of just listing runs, you can query past data to get answers grounded in actual API results. This means if you ask about 'why did the marketing workflow fail last Tuesday,' the index returns the specific execution details.

Indexing integration setup options.

You don't just list solutions with `list_integration_solutions`; LlamaIndex indexes those templates. You can then ask natural language questions like, 'What solutions exist for syncing data from Salesforce to Slack?' and get precise answers. This capability turns static lists into a dynamic knowledge base.

Querying account structure via available services.

The `list_available_connectors` tool shows all connected services. By indexing this data, your agent can answer complex questions like, 'What services besides Slack are connected?' This is much better than just seeing a list. It lets you build RAG applications where live API connection status and documentation combine into one queryable index.

Setup guide

Set up Tray.io MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Tray.io MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Tray.io tools.",
)
response = await agent.run("List recent Tray.io data")

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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 Tray.io. 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 Tray.io MCP in LlamaIndex

LlamaIndex takes the raw data from MCP tools—like workflow lists or user details—and turns it into searchable embeddings. This means your agent doesn't just read a list; it understands and answers questions about that list.

Yes. By indexing the outputs of `list_workflow_executions`, you can query specific historical data points from any workflow run, effectively turning execution history into a searchable knowledge base.

The `list_available_connectors` tool provides the raw list, but LlamaIndex indexes this data. You can then ask semantic questions about connectivity rather than just running a programmatic function.

When `get_authenticated_user` is called, the resulting user data is indexed. This allows your agent to later answer questions like 'Who was the last person who ran this automation?' using natural language search.

This server touches user identity and account configuration data, specifically retrieving details for the currently authenticated user via `get_authenticated_user`.

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