How to Use the Ocoya Social Media MCP in LlamaIndex

Index your social media strategy into LlamaIndex using Ocoya Social Media tools for semantic search and retrieval.

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Connect Ocoya Social Media MCP to LlamaIndex

Create your Vinkius account to connect Ocoya Social Media to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Ocoya Social Media with LlamaIndex

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Searchable social knowledge in LlamaIndex

The server tools integrate with your vector index to make your social media history queryable. Use `list_posts` to feed your knowledge base with past content performance data. This turns your static posts into a searchable resource. Your application now answers questions about your brand voice by retrieving actual past content.

Contextual retrieval for Ocoya Social Media

Ground your AI responses in real data by calling `get_profile` before generating content. This ensures the agent understands the specific audience and tone for each workspace. Your RAG pipeline uses these live details to avoid guessing. It pulls the exact metadata required for accurate and relevant social output.

Automated indexing of social workflows

Map your active workflows by indexing the output of `list_automations` into your LlamaIndex storage. This allows the system to understand your existing processes at a semantic level. Querying your setup becomes simple. You ask the agent about specific workflows and it retrieves the current configuration directly from the source.

Setup guide

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

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Common questions about Ocoya Social Media MCP in LlamaIndex

Use the MCP tool spec to convert server tools into functions for your agent. Once converted, the agent queries the API and indexes the results into your vector store.

It does. You pass the tool list to a function agent to allow it to retrieve and process your social profiles or posts on demand.

Absolutely. By indexing the output of the listing tools, you enable semantic search across your entire post history.

Use the allowed tools filter during the tool spec initialization. This restricts the agent to only the specific endpoints you want it to access.

Data is retrieved only when the agent calls the specific tool. The index stores vector representations of your content, not your raw credentials or private account tokens.

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