How to Use the Type.fit MCP in LlamaIndex

Q: How does Type.fit help with LlamaIndex?

The system indexes the quotes retrieved by getquotes into a searchable knowledge base. You query that index later, getting answers directly from the fit database data.

Q: Can I use Type.fit and LlamaIndex for past session queries?

Yep. Since the output of getquotes is indexed, you can build a knowledge base that lets you query past sessions or configurations based on the quote content.

Q: Does Type.fit support filtering data access in LlamaIndex?

Yes, you can use the allowedtools filter when setting up your client. This keeps your application focused and only exposes necessary tools like getquotes.

Grounding search results with Type.fit and LlamaIndex.

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Connect Type.fit MCP to LlamaIndex

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Setup Type.fit with LlamaIndex

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Indexing Quotes for Search

The `get_quotes` tool retrieves quotes from the fit database, but LlamaIndex does more than just run it. It indexes the resulting quote text into a vector store. Later, you can query past quotes or motivational content and get answers grounded in actual API data, not something hallucinated.

RAG with Type.fit Data

You build RAG applications by combining live API data—like the quotes from `get_quotes`—with your documents. The MCP tool output becomes part of a unified, queryable index. This means if you run a quote today and run another search next week, you can ask about both pieces of content.

Retrieving Quotes in Code

When defining your agent tools, you pass the `get_quotes` specification to FunctionAgent. This allows LlamaIndex to treat the quote retrieval like any other piece of knowledge source code. It's clean setup: `await mcp_tool_spec.to_tool_list_async()` gets it ready for your typed code.

Setup guide

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

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Common questions about Type.fit MCP in LlamaIndex

The system indexes the quotes retrieved by `get_quotes` into a searchable knowledge base. You query that index later, getting answers directly from the fit database data.

Yep. Since the output of `get_quotes` is indexed, you can build a knowledge base that lets you query past sessions or configurations based on the quote content.

This server touches inspirational quote text from the fit database. This raw text is what gets ingested and indexed into your vector store for semantic search later on.

Yes, you can use the `allowed_tools` filter when setting up your client. This keeps your application focused and only exposes necessary tools like `get_quotes`.

It moves beyond simple tool calling. You use quotes not just for immediate output, but as structured knowledge that your AI client can search through months later.

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