How to Use the Microsoft Ads MCP in LlamaIndex

Q: How do I set up the Microsoft Ads MCP Server in LlamaIndex?

Install the tool using pip install llama-index-tools-mcp. Use BasicMCPClient to connect to the MCP Server, convert the tools with McpToolSpec, and pass them to your FunctionAgent.

Q: Can LlamaIndex index my Microsoft Ads keyword data?

Yes, LlamaIndex can call listmskeywords and parse the output into document nodes. This lets you perform semantic vector search over your entire keyword library.

Q: How does LlamaIndex prevent hallucinations with Microsoft Ads?

By indexing actual data from getmsaccountinfo, LlamaIndex grounds its responses in real API facts. The agent queries the local vector index instead of guessing account details.

Q: Can I filter which Microsoft Ads tools are exposed to LlamaIndex?

Yes, you can use the allowedtools filter when setting up your McpToolSpec. This lets you restrict your agent to specific tools like listmsbudgets while ignoring others.

Q: Is my Microsoft Ads keyword and ad copy data private?

Yes, your keyword list retrieved via listmskeywords is processed entirely within our secure MCP sandbox architecture. The data is only transmitted to your local LlamaIndex instance over encrypted channels.

Index Microsoft Ads campaign data directly into LlamaIndex vector stores for RAG-powered search.

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Connect Microsoft Ads MCP to LlamaIndex

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Setup Microsoft Ads with LlamaIndex

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Index Microsoft Ads campaigns into LlamaIndex

The `list_ms_campaigns` tool, powered by our MCP Server, retrieves active campaign metadata, allowing LlamaIndex to build a searchable knowledge base of your marketing structure. This tool feeds raw campaign details directly into your indexing pipeline, turning live API data into searchable vector nodes. Your LlamaIndex agent queries this index to answer questions about campaign status without calling the API repeatedly. By combining `get_ms_campaign_details` with your vector store, you ground your agent's answers in actual performance data.

Build RAG pipelines for Microsoft Ads targeting

The `list_ms_targeting_settings` tool extracts targeting criteria so LlamaIndex can index your audience parameters for semantic search. This allows your query engine to map user questions directly to active targeting profiles. You can run `get_ms_ad_group_details` alongside these targeting indexes to generate comprehensive context. The `McpToolSpec` adapter handles the data conversion, ensuring your vector database stays updated with fresh targeting records.

Query keyword lists through LlamaIndex vector nodes

The `list_ms_keywords` tool pulls active keyword lists directly into your LlamaIndex document store. This lets you run semantic queries over your keyword strategy to find gaps or overlaps across ad groups. Your `FunctionAgent` uses these indexed keywords to evaluate relevance against search trends. By storing the output of `list_ms_ads` alongside these keywords, LlamaIndex creates a rich, searchable map of your creative assets.

Setup guide

Set up Microsoft Ads MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Microsoft Ads MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Microsoft Ads tools.",
)
response = await agent.run("List recent Microsoft Ads data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Microsoft Ads. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Microsoft Ads MCP in LlamaIndex

Install the tool using `pip install llama-index-tools-mcp`. Use `BasicMCPClient` to connect to the MCP Server, convert the tools with `McpToolSpec`, and pass them to your `FunctionAgent`.

Yes, LlamaIndex can call `list_ms_keywords` and parse the output into document nodes. This lets you perform semantic vector search over your entire keyword library.

By indexing actual data from `get_ms_account_info`, LlamaIndex grounds its responses in real API facts. The agent queries the local vector index instead of guessing account details.

Yes, you can use the `allowed_tools` filter when setting up your `McpToolSpec`. This lets you restrict your agent to specific tools like `list_ms_budgets` while ignoring others.

Yes, your keyword list retrieved via `list_ms_keywords` is processed entirely within our secure MCP sandbox architecture. The data is only transmitted to your local LlamaIndex instance over encrypted channels.

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