Typesense Vector Search MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Typesense Vector Search as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Typesense Vector Search. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Typesense Vector Search?"
)
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 Typesense Vector Search MCP Server
Connect your Typesense Vector Search environment to any AI agent and take full autonomous control over vector collections, indexing processes, and semantic querying through daily conversation.
LlamaIndex agents combine Typesense Vector Search tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Vector Semantic Search — Issue combined text-filtering alongside vector similarity (
vec) queries natively through chat - Collection Provisioning — Instantly create new semantic schema datasets holding complex vector embedding structures organically
- Document Indexing — Let your AI insert or update JSON payloads into your database, bypassing manual code-level REST integrations
- Schema & Records Insights — Retrieve absolute schema geometries mapping collections to ensure developers map fields correctly
The Typesense Vector Search MCP Server exposes 6 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 Typesense Vector Search to LlamaIndex via MCP
Follow these steps to integrate the Typesense Vector Search 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 6 tools from Typesense Vector Search
Why Use LlamaIndex with the Typesense Vector Search MCP Server
LlamaIndex provides unique advantages when paired with Typesense Vector Search through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Typesense Vector Search tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Typesense Vector Search tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Typesense Vector Search, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Typesense Vector Search tools were called, what data was returned, and how it influenced the final answer
Typesense Vector Search + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Typesense Vector Search MCP Server delivers measurable value.
Hybrid search: combine Typesense Vector Search real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Typesense Vector Search 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 Typesense Vector Search for fresh data
Analytical workflows: chain Typesense Vector Search queries with LlamaIndex's data connectors to build multi-source analytical reports
Typesense Vector Search MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Typesense Vector Search to LlamaIndex via MCP:
create_collection
Provide the schema details as a JSON object. Creates a new search collection with a specific schema
delete_document
This action is irreversible. Permanently removes a document from a collection by its ID
get_collection_details
Retrieves schema and metadata for a specific collection
index_document
Provide the collection name and the document data as a JSON object. Adds or updates a document in a search collection
list_vector_collections
Lists all collections in the Typesense instance
search_vectors
Provide the collection name, a text query, and a vector_query string (e.g., "vec:(0.1, 0.2, ...)"). Performs a vector similarity search combined with optional text filtering
Example Prompts for Typesense Vector Search in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Typesense Vector Search immediately.
"List all active collections on this vector cluster. Do I have any collections initialized yet?"
"I have an embedding snippet: [0.34, 0.42, 0.99...]. Delete the document carrying ID 'test-123' and re-index it using this JSON data on collection 'faqs'."
"Explain the schema definitions used inside the 'products_inventory' collection."
Troubleshooting Typesense Vector Search MCP Server with LlamaIndex
Common issues when connecting Typesense Vector Search to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTypesense Vector Search + LlamaIndex FAQ
Common questions about integrating Typesense Vector Search 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 Typesense Vector Search 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 Typesense Vector Search to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.