How to Use the Metricool MCP in LlamaIndex

Q: How do I load Metricool tool outputs into a LlamaIndex vector store?

Use the tool spec to load tools like getunifiedsummary into your LlamaIndex agent. The agent executes the tool, wraps the raw social data in a Document object, and writes it directly to your vector index.

Q: Can LlamaIndex query historical Metricool posts semantically?

Yes, by indexing the output of listpublishedposts into your local vector database. Users can then search past social copy and engagement metrics using natural language queries.

Index your live Metricool social analytics directly into LlamaIndex vector stores for ground-truth RAG search.

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Connect Metricool MCP to LlamaIndex

Create your Vinkius account to connect Metricool to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Metricool with LlamaIndex

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Query live social data with LlamaIndex RAG

The Metricool MCP server lets you run `get_unified_summary` and convert the live payload directly into indexable document nodes. This prevents your RAG application from hallucinating social metrics because it queries actual, real-time data. Your LlamaIndex query engine matches user questions against these indexed nodes. When someone asks about weekly performance, the engine pulls the latest metrics from the vector store instead of guessing.

Build semantic indexes of scheduled content

The `get_social_planner` MCP tool feeds your upcoming posts directly into LlamaIndex text splitters. This builds a searchable database of your scheduled social content, allowing your agent to analyze themes and frequency. By indexing this queue, you query your schedule using natural language. You can ask your agent if you have enough video content planned for next week, and it will check the indexed planner data to verify.

Match past performance with current profiles

The `list_published_posts` tool provides the historical context that LlamaIndex needs to evaluate past campaign success. The tool retrieves actual post data, which your indexer stores alongside profile metadata. This historical index lets your query engine compare current profile details from `get_profile_details` with past engagement. Your agent spots long-term trends by querying this unified knowledge base.

Setup guide

Set up Metricool 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 Metricool 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 Metricool tools.",
)
response = await agent.run("List recent Metricool data")

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Common questions about Metricool MCP in LlamaIndex

Use the tool spec to load tools like `get_unified_summary` into your LlamaIndex agent. The agent executes the tool, wraps the raw social data in a Document object, and writes it directly to your vector index.

Yes, by indexing the output of `list_published_posts` into your local vector database. Users can then search past social copy and engagement metrics using natural language queries.

The agent evaluates the user query against the tool schemas registered by the MCP server. If a query asks about Twitter performance, the agent automatically selects `get_twitter_analytics` to fetch the correct data.

Yes, you can pass an allowed tools filter when defining your MCP tool specification. This lets you restrict your agent to analytical tools like `get_instagram_analytics` while blocking scheduling tools.

Your raw metrics and scheduling data flow through memory directly to your local vector store. The Vinkius sandbox ensures that no social credentials or profile payloads are cached or exposed to third parties.

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