LinkAce MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LinkAce as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to LinkAce. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in LinkAce?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About LinkAce MCP Server
Connect your LinkAce instance to any AI agent to automate your personal knowledge base and link archiving. This MCP server enables your agent to add new bookmarks, organize them into lists and tags, and search your entire library directly from natural language interfaces.
LlamaIndex agents combine LinkAce tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Instant Archiving — Quickly add new URLs to your LinkAce library with custom titles and descriptions
- Deep Organization — Create and manage tags and lists to keep your bookmarks categorized and easy to find
- Semantic Discovery — Search through your entire archived library using keywords via natural language commands
- Library Maintenance — Retrieve detailed metadata for specific links or permanently remove outdated bookmarks
- Self-Hosted Support — Works with any self-hosted LinkAce instance using your personal API token
The LinkAce MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect LinkAce to LlamaIndex via MCP
Follow these steps to integrate the LinkAce MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from LinkAce
Why Use LlamaIndex with the LinkAce MCP Server
LlamaIndex provides unique advantages when paired with LinkAce through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LinkAce tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LinkAce tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LinkAce, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LinkAce tools were called, what data was returned, and how it influenced the final answer
LinkAce + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LinkAce MCP Server delivers measurable value.
Hybrid search: combine LinkAce real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LinkAce to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying LinkAce for fresh data
Analytical workflows: chain LinkAce queries with LlamaIndex's data connectors to build multi-source analytical reports
LinkAce MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect LinkAce to LlamaIndex via MCP:
create_new_bookmark
Requires at least a URL. Add a new link to your archive
create_new_collection
Add a new collection (list)
create_new_tag
Add a new tag
delete_bookmark
Remove a bookmark from your archive
get_bookmark_details
Get details for a specific bookmark
list_all_bookmarks
List all bookmarks (links) in your LinkAce account
list_all_collections
List all bookmark collections (lists)
list_all_tags
List all tags used for organizing bookmarks
search_bookmarks
Search for bookmarks by keyword
Example Prompts for LinkAce in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with LinkAce immediately.
"Add 'https://www.wikipedia.org' to my LinkAce bookmarks."
"Search my LinkAce library for 'Artificial Intelligence'."
"List all my bookmark collections."
Troubleshooting LinkAce MCP Server with LlamaIndex
Common issues when connecting LinkAce to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLinkAce + LlamaIndex FAQ
Common questions about integrating LinkAce MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect LinkAce with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect LinkAce to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.