Metricool 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 Metricool 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 Metricool. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Metricool?"
)
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 Metricool MCP Server
Connect your Metricool account to any AI agent and take full control of your social media performance and planning through natural conversation.
LlamaIndex agents combine Metricool 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
- Social Analytics — Retrieve detailed metrics for Instagram, Facebook, Twitter, and LinkedIn profiles in real-time
- Unified Summary — Access high-level cross-channel performance reports to understand your total digital reach
- Content Planning — List and inspect your social media planner to stay ahead of upcoming scheduled posts
- Ads Performance — Monitor spend and conversion data for social advertising platforms directly from your agent
- Profile Management — Enumerate all connected brands and social accounts linked to your workspace
The Metricool 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 Metricool to LlamaIndex via MCP
Follow these steps to integrate the Metricool 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 Metricool
Why Use LlamaIndex with the Metricool MCP Server
LlamaIndex provides unique advantages when paired with Metricool through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Metricool tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Metricool tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Metricool, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Metricool tools were called, what data was returned, and how it influenced the final answer
Metricool + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Metricool MCP Server delivers measurable value.
Hybrid search: combine Metricool real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Metricool 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 Metricool for fresh data
Analytical workflows: chain Metricool queries with LlamaIndex's data connectors to build multi-source analytical reports
Metricool MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Metricool to LlamaIndex via MCP:
get_ads_performance
Get performance for ads
get_facebook_analytics
Get Facebook analytics
get_instagram_analytics
Get Instagram analytics
get_linkedin_analytics
Get LinkedIn analytics
get_profile_details
Get details for a specific profile
get_social_planner
Get scheduled posts planner
get_twitter_analytics
Get Twitter analytics
get_unified_summary
Get unified cross-channel summary
list_metricool_profiles
List all connected social profiles
list_published_posts
List recently published posts
Example Prompts for Metricool in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Metricool immediately.
"Show my Instagram analytics for the last 30 days."
"What posts are scheduled in my planner?"
"Show a summary of my performance across all channels."
Troubleshooting Metricool MCP Server with LlamaIndex
Common issues when connecting Metricool to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMetricool + LlamaIndex FAQ
Common questions about integrating Metricool 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 Metricool 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 Metricool to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.