Google Search Console 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 Google Search Console 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 Google Search Console. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Google Search Console?"
)
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 Google Search Console MCP Server
Connect your Google Search Console to your AI agent and take control of your technical SEO. Use natural language to query search traffic data, inspect URLs for indexing errors, and manage your sitemaps across all your verified web properties.
LlamaIndex agents combine Google Search Console tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Search Analytics — Query clicks, impressions, CTR, and position data by date, country, device, or specific queries
- URL Inspection — Instantly check if a specific page on your site is indexed by Google and identify any mobile usability or indexing errors
- Sitemap Management — List all submitted sitemaps, verify their status, and submit new ones directly from the chat
- Site Management — View all your verified properties and add or remove sites from your Search Console account
The Google Search Console 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 Google Search Console to LlamaIndex via MCP
Follow these steps to integrate the Google Search Console 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 Google Search Console
Why Use LlamaIndex with the Google Search Console MCP Server
LlamaIndex provides unique advantages when paired with Google Search Console through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Google Search Console tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Google Search Console tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Google Search Console, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Google Search Console tools were called, what data was returned, and how it influenced the final answer
Google Search Console + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Google Search Console MCP Server delivers measurable value.
Hybrid search: combine Google Search Console real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Google Search Console 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 Google Search Console for fresh data
Analytical workflows: chain Google Search Console queries with LlamaIndex's data connectors to build multi-source analytical reports
Google Search Console MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Google Search Console to LlamaIndex via MCP:
add_site
Add a site to Search Console
delete_site
Remove a site from Search Console
delete_sitemap
Delete a submitted sitemap
get_site
Get details for a specific site
get_sitemap
Get details about a specific sitemap
inspect_url
Inspect the index status of a specific URL
list_sitemaps
List submitted sitemaps for a site
list_sites
List verified sites in Search Console
query_search_analytics
Query search traffic data
submit_sitemap
Submit a new sitemap
Example Prompts for Google Search Console in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Google Search Console immediately.
"Check if https://mysite.com/new-post is indexed by Google."
"What were the top 5 search queries for our site last week?"
"Submit our new sitemap at https://mysite.com/sitemap-products.xml to Search Console."
Troubleshooting Google Search Console MCP Server with LlamaIndex
Common issues when connecting Google Search Console to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGoogle Search Console + LlamaIndex FAQ
Common questions about integrating Google Search Console 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 Google Search Console 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.
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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 Google Search Console to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.