How to Use the Bitly MCP in LlamaIndex

Q: How do I filter Bitly tools in LlamaIndex?

You use the allowedtools filter during the agent setup. This ensures your LlamaIndex agent only touches the specific link functions you define.

Index your Bitly link performance data into LlamaIndex for searchable knowledge bases.

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Connect Bitly MCP to LlamaIndex

Create your Vinkius account to connect Bitly 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 Bitly with LlamaIndex

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Searchable Bitly Data in LlamaIndex

Call `get_clicks` or `get_countries` and let LlamaIndex store the output in your vector database. You get a searchable history of link performance. Instead of simple logs, you build a knowledge base that answers questions about your traffic trends. Your agent queries this index for grounded insights.

Knowledge-Augmented Link Management

Use `list_groups` to index your account structure for faster retrieval. Your LlamaIndex agent understands your link hierarchy because it's part of the search index. This allows the agent to find specific links based on your past conversations. It connects the dots between your link groups and current campaign goals.

Dynamic URL Generation

Generate new links with `shorten_url` and immediately index the metadata for future reference. Your system keeps a record of every generated asset. It ensures your agent doesn't hallucinate about link history. You query the index to verify which URLs were created and when.

Setup guide

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

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Common questions about Bitly MCP in LlamaIndex

You use the MCP tool spec to turn API responses into searchable data. This turns your link analytics into a queryable knowledge base.

It can. The agent fetches the metrics and indexes them, allowing you to ask questions about your audience location and referral sources.

Absolutely. You build RAG applications where live link data is combined with your internal documents for better context.

You use the allowed_tools filter during the agent setup. This ensures your LlamaIndex agent only touches the specific link functions you define.

Vinkius isolates your session and encrypts the link metadata in transit. Your index only contains the specific data points your agent retrieves.

Use it with your favorite AI tools

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