How to Use the TrueFoundry MCP in LlamaIndex

Q: How does LlamaIndex use TrueFoundry to ground answers?

LlamaIndex ingests the output from tools like truefoundrylistgatewaymodels. It then calculates embeddings on that structured API data, allowing it to retrieve specific facts—like a model's name or version—instead of just guessing.

Q: Can TrueFoundry help LlamaIndex manage multiple tool schemas?

Yes. You can pull the metadata for individual tools using truefoundrygetmcpserverinfo. This allows you to combine data from several discrete sources into one unified, queryable index.

Q: If I use TrueFoundry with LlamaIndex, how do I know what's available?

You run truefoundrylistmcpservers to get a master registry. This list defines the boundaries of all logical MCP Tools that your RAG application can reference and index.

Q: What is the scope of TrueFoundry when running LlamaIndex?

You use truefoundrylistgatewaymodels to define the available LLM sources. This keeps your index focused only on models you want users to search against.

Q: What data type does TrueFoundry expose for tool metadata?

The truefoundrygetmcpserverinfo tool extracts the exact JSON metadata of a registered TrueFoundry tool schema, defining its inputs and expected outputs.

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Setup TrueFoundry with LlamaIndex

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TrueFoundry for LlamaIndex: Indexing API Output.

LlamaIndex doesn't just read data; it makes it searchable. When you call `truefoundry_generate_embeddings`, the resulting semantic vectors become part of your knowledge base. You can query past configurations or live service data and get answers grounded in actual TrueFoundry API responses, not guesses.

TrueFoundry for LlamaIndex: Discovering MCP Tools.

Before building the index, you need to know what's there. Use `truefoundry_list_mcp_servers` and `truefoundry_list_deployments` to map out all available logical tools and running backends. This lets your RAG application know exactly which data sources it can reference.

TrueFoundry for LlamaIndex: Model Schema Extraction.

To index the capabilities of a service, you need its blueprint. The tool `truefoundry_get_mcp_server_info` extracts the exact JSON metadata for any registered TrueFoundry tool schema. This structured data becomes part of your searchable knowledge base.

Setup guide

Set up TrueFoundry 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 TrueFoundry 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 TrueFoundry tools.",
)
response = await agent.run("List recent TrueFoundry data")

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Common questions about TrueFoundry MCP in LlamaIndex

LlamaIndex ingests the output from tools like `truefoundry_list_gateway_models`. It then calculates embeddings on that structured API data, allowing it to retrieve specific facts—like a model's name or version—instead of just guessing.

Yes. You can pull the metadata for individual tools using `truefoundry_get_mcp_server_info`. This allows you to combine data from several discrete sources into one unified, queryable index.

You run `truefoundry_list_mcp_servers` to get a master registry. This list defines the boundaries of all logical MCP Tools that your RAG application can reference and index.

You use `truefoundry_list_gateway_models` to define the available LLM sources. This keeps your index focused only on models you want users to search against.

The `truefoundry_get_mcp_server_info` tool extracts the exact JSON metadata of a registered TrueFoundry tool schema, defining its inputs and expected outputs.

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