How to Use the Bored MCP in LlamaIndex

Q: Can I query Bored activity data using LlamaIndex RAG pipelines?

Yes, you can index the output of findactivity directly into a vector store. This lets your LlamaIndex agent perform semantic searches over all retrieved activities.

Q: How do I configure the Bored MCP Server within LlamaIndex?

Initialize our server with BasicMCPClient and convert it using McpToolSpec. You then pass the generated tool list directly to your LlamaIndex FunctionAgent.

Index Bored activities directly into your LlamaIndex vector stores to ground your agent in real-world recreation data.

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Connect Bored MCP to LlamaIndex

Create your Vinkius account to connect Bored 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 Bored with LlamaIndex

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Index Bored MCP Server Data into LlamaIndex

The `find_activity` tool retrieves structured recreation ideas that LlamaIndex instantly vectorizes and stores. Your agent queries this local index instead of hitting external APIs repeatedly. This setup prevents hallucinations by grounding your agent's suggestions in verified Bored data. LlamaIndex uses semantic search to match user mood queries to actual indexed activities.

Retrieving Exact Activities by Unique Keys

The `get_activity_by_key` tool pulls specific recreation nodes into your LlamaIndex document store. You can map these unique keys to metadata tags inside your vector index. When a user asks for a specific past recommendation, LlamaIndex locates the key in its index. The agent then serves the exact activity details without searching the entire database again.

Dynamic Knowledge Base Enrichment

The `get_random_activity` tool provides spontaneous data points to refresh your LlamaIndex knowledge base. Our MCP server lets you schedule periodic runs to inject fresh, random activities into your index. LlamaIndex's ingestion pipeline processes these random suggestions, updating the vector space on the fly. This keeps your agent's recommendation pool diverse and unexpected.

Setup guide

Set up Bored 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 Bored 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 Bored tools.",
)
response = await agent.run("List recent Bored 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 Bored API. 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 Bored MCP in LlamaIndex

Yes, you can index the output of `find_activity` directly into a vector store. This lets your LlamaIndex agent perform semantic searches over all retrieved activities.

Initialize our server with `BasicMCPClient` and convert it using `McpToolSpec`. You then pass the generated tool list directly to your LlamaIndex `FunctionAgent`.

LlamaIndex allows you to store tool outputs in its document store for persistent access. This reduces the number of live calls your agent makes to the `get_random_activity` tool.

Your agent inspects the price metadata returned by our tool. You can set filter thresholds in your query engine to discard any results that exceed your user's budget.

Your budget, participant limits, and activity keys are processed in a zero-trust, ephemeral sandbox. We never write your search criteria or indexed recreation outputs to persistent storage.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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