How to Use the BL.INK MCP in LlamaIndex
Index your link data into LlamaIndex knowledge bases for smarter, grounded RAG applications.
Works with every AI agent you already use
…and any MCP-compatible client
Connect BL.INK MCP to LlamaIndex
Create your Vinkius account to connect BL.INK 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.
Ground LlamaIndex queries in real link data
Map `list_links` output into your vector index. You build a knowledge base that knows the current state of every redirect you own. When you ask about your top-performing content, the agent uses `get_link_analytics` to pull fresh metrics. It answers your questions using live API data instead of stale training sets.
Query BL.INK link metadata
The `get_link` tool provides granular details about your URLs. LlamaIndex stores these results as searchable documents for later retrieval. Your RAG application searches through these indexed details to find specific campaigns. It then presents a summary of your link performance based on the actual API records.
Automate domain indexing for LlamaIndex
Use `list_domains` to pull your branding infrastructure into your LlamaIndex context. The agent learns which domains are active for specific product lines. Retrieve specific domain constraints with `get_domain` to inform your index. This keeps your RAG system aware of your current naming conventions and link routing rules.
Set up BL.INK MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 BL.INK MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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 BL.INK tools.",
)
response = await agent.run("List recent BL.INK data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BL.INK. 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.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about BL.INK MCP in LlamaIndex
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