Tana MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tana 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 Tana. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Tana?"
)
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 Tana MCP Server
Translate your AI conversation into structured personal knowledge management seamlessly with the Tana MCP connector. Evolve your LLM into a dedicated ontological architect capable of pushing rich, contextual data fragments straight into your workspace. Bypass tedious manual entry by programming your assistant to dynamically categorize thoughts, mint native ontological classes (Supertags), and instantiate multi-level hierarchies inside your Tana graph while maintaining maximum focus in your local environment.
LlamaIndex agents combine Tana tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Node Structuring — Swiftly inject clean data fragments anywhere by defining paths invoking
add_nodeor securely drop ideations asynchronously into your capture zone utilizingadd_to_inbox. - Ontology & Metadata — Formalize data classifications mapping real-world objects using
define_supertagand instantiate them powerfully utilizingadd_tagged_nodeandadd_node_with_fields. - Hierarchy & Linking — Push whole outline structures programmatically executing
add_node_with_childrenand enforce complex bi-directional network paths executingadd_node_reference. - Specialized Datatypes — Effortlessly instantiate formatted daily operations leveraging
add_checkbox_task, temporal entries mappingadd_date_node, or external resources resolving viaadd_url_bookmark.
The Tana MCP Server exposes 10 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 Tana to LlamaIndex via MCP
Follow these steps to integrate the Tana 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 10 tools from Tana
Why Use LlamaIndex with the Tana MCP Server
LlamaIndex provides unique advantages when paired with Tana through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tana tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tana tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tana, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tana tools were called, what data was returned, and how it influenced the final answer
Tana + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tana MCP Server delivers measurable value.
Hybrid search: combine Tana real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tana 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 Tana for fresh data
Analytical workflows: chain Tana queries with LlamaIndex's data connectors to build multi-source analytical reports
Tana MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Tana to LlamaIndex via MCP:
add_checkbox_task
Optionally set initial done status. Creates a checkbox/todo item in the Tana inbox
add_date_node
Format: YYYY-MM-DD. Creates a date-typed node in the Tana inbox
add_node
Provide a target node ID (or "INBOX", "LIBRARY") and the node name. Creates a new node in a specific Tana location
add_node_reference
Provide a label and the target node ID. Creates a reference node linking to an existing node
add_node_with_children
Provide a name and comma-separated children. Creates a parent node with multiple child nodes
add_node_with_fields
Provide name, supertag ID, and field data as a JSON object. Creates a supertagged node with structured field values
add_tagged_node
g. #meeting, #person). Requires the supertag ID from Tana schema. Creates a new node with a supertag applied
add_to_inbox
Quickly adds a new node directly to the Tana Inbox
add_url_bookmark
Creates a URL-typed node in Tana
define_supertag
Provide a name and description. Defines a new supertag in the Tana schema
Example Prompts for Tana in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Tana immediately.
"Add a new conceptual outline to my Inbox. The main title should be 'Quarterly Product Strategy', and it should contain three specific child nodes functioning as checkable tasks."
"Create a new node 'Meeting Notes format' structured in our weekly workspace."
"Search my Tana knowledge base for nodes tagged with '#project'."
Troubleshooting Tana MCP Server with LlamaIndex
Common issues when connecting Tana to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTana + LlamaIndex FAQ
Common questions about integrating Tana 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 Tana 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 Tana to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.