AddSearch 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 AddSearch 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 AddSearch. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in AddSearch?"
)
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 AddSearch MCP Server
Connect your AddSearch account to your AI agent and turn your site's search index into an interactive, manageable database. Perfect for content teams and developers who need to audit site search performance without opening dashboards.
LlamaIndex agents combine AddSearch 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
- Deep Search — Query your indexed content using natural language, apply custom field filters (e.g. "category=shoes"), or sort by custom variables
- Document Management — List all indexed pages, import new content directly via JSON, or permanently delete outdated documents from the index
- Search Analytics — Retrieve live statistics on user queries, identifying top searches, zero-result queries, and click-through rates
- Frontend Emulation — Test your auto-suggestions and pagination just like a real user interacting with your search bar
The AddSearch 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 AddSearch to LlamaIndex via MCP
Follow these steps to integrate the AddSearch 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 AddSearch
Why Use LlamaIndex with the AddSearch MCP Server
LlamaIndex provides unique advantages when paired with AddSearch through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AddSearch tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AddSearch tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AddSearch, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AddSearch tools were called, what data was returned, and how it influenced the final answer
AddSearch + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AddSearch MCP Server delivers measurable value.
Hybrid search: combine AddSearch real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AddSearch 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 AddSearch for fresh data
Analytical workflows: chain AddSearch queries with LlamaIndex's data connectors to build multi-source analytical reports
AddSearch MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect AddSearch to LlamaIndex via MCP:
autosuggest
Get autocomplete suggestions
delete_document
Requires Secret Key. Permanently delete a document
index_document
Requires Secret Key. Add or update an indexed document
list_documents
Requires Secret Key. List all indexed documents
search_filtered
g., "category=shoes", "brand=nike"). Search indexed content by custom field
search_keyword
Search indexed content by keyword
search_pagination
Retrieve a specific page of search results
search_sorted
Search indexed content with custom sort
stats_clicks
Requires Secret Key. Retrieve click-through analytics
stats_queries
Requires Secret Key. Retrieve search query analytics
Example Prompts for AddSearch in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AddSearch immediately.
"Show me the top search queries that resulted in 0 hits."
"Search my site for "pricing updates" filtered by category=news."
"Test the auto-suggest for the prefix "shoe"."
Troubleshooting AddSearch MCP Server with LlamaIndex
Common issues when connecting AddSearch to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAddSearch + LlamaIndex FAQ
Common questions about integrating AddSearch 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 AddSearch 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 AddSearch to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.