Tettra MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tettra as an MCP tool provider through the 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 Tettra. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Tettra?"
)
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 Tettra MCP Server
Connect your Tettra internal knowledge base to any AI agent and bring your company's documentation directly into your developer workflow. No more switching tabs to look up API specs or onboarding guides.
LlamaIndex agents combine Tettra tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the 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
- Deep Search — Perform full-text searches across all your company's Tettra pages to instantly find answers and organizational knowledge
- Knowledge Retrieval — Read the complete markdown/HTML content of any page, technical guide, or team policy natively inside your chat
- Content Creation — Command your agent to draft and publish new wiki pages, or suggest documentation updates on the fly
- Category Navigation — Browse through your team's top-level categories, root folders, and subcategories visually
- Q&A Management — Post new questions to your team's internal Q&A board or list unanswered questions right from your IDE
The Tettra MCP Server exposes 12 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 Tettra to LlamaIndex via MCP
Follow these steps to integrate the Tettra 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 12 tools from Tettra
Why Use LlamaIndex with the Tettra MCP Server
LlamaIndex provides unique advantages when paired with Tettra through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tettra tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tettra tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tettra, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tettra tools were called, what data was returned, and how it influenced the final answer
Tettra + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tettra MCP Server delivers measurable value.
Hybrid search: combine Tettra real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tettra 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 Tettra for fresh data
Analytical workflows: chain Tettra queries with LlamaIndex's data connectors to build multi-source analytical reports
Tettra MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Tettra to LlamaIndex via MCP:
create_qa_question
Posts a new question in the Tettra Q&A system
create_wiki_page
Provide title, content, and category ID. Creates a new wiki page in a specific category
get_category_details
Retrieves details for a specific Tettra category
get_page_content
Returns title and Markdown/HTML body. Retrieves the full content and metadata of a specific Tettra page
list_categories
Lists all top-level categories in the Tettra wiki
list_pages_in_category
Lists all wiki pages within a specific category
list_qa_questions
Lists all questions posted in the Tettra Q&A system
list_subcategories
Lists all subcategories under a specific parent category
search_pages
Returns up to 5 matching pages. Full-text search across all Tettra wiki pages
suggest_new_page
Suggests a new wiki page to the team
update_wiki_page
Provide the page ID and the new fields. Updates the title or content of an existing Tettra page
verify_wiki_page
Marks a Tettra page as verified and up-to-date
Example Prompts for Tettra in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Tettra immediately.
"Search the wiki for 'Database Migration Checklist'."
"Create a new wiki page in the 'Support' category explaining how to handle refund requests."
"Mark page ID 883 as verified and up to date."
Troubleshooting Tettra MCP Server with LlamaIndex
Common issues when connecting Tettra to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTettra + LlamaIndex FAQ
Common questions about integrating Tettra 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 Tettra 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 Tettra to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.