Replicate MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Replicate as an MCP tool provider through the 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 Replicate. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Replicate?"
)
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 Replicate MCP Server
Connect your conversational assistant directly to the Replicate ecosystem. This integration grants your AI the ability to interact programmatically with a vast library of open-source machine learning models without running them on your local hardware. From orchestrating complex image generations to spinning up specialized language models, you can command AI workflows directly from your chat.
LlamaIndex agents combine Replicate tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Execute Predictions — Command the assistant to execute specific model versions on your behalf (
create_prediction) by supplying a payload of variables. Monitor long-running processes by retrieving outputs and execution status reliably (get_prediction) or cancel them at will (cancel_prediction). - Discover Models — Instruct the AI to intelligently scan the Replicate platform for models matching a specific use case using
search_models. You can also explore trending and categorized models by leveraging thelist_collectionsaction. - Analyze Model Metadata — Whenever you discover a new model, query its precise owner and name (
get_model) to extract the exact schema and parameter requirements necessary for a successful execution. You can also view a log of your own executed tasks (list_predictions).
The Replicate MCP Server exposes 12 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 Replicate to LlamaIndex via MCP
Follow these steps to integrate the Replicate 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 12 tools from Replicate
Why Use LlamaIndex with the Replicate MCP Server
LlamaIndex provides unique advantages when paired with Replicate through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Replicate tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Replicate tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Replicate, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Replicate tools were called, what data was returned, and how it influenced the final answer
Replicate + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Replicate MCP Server delivers measurable value.
Hybrid search: combine Replicate real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Replicate 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 Replicate for fresh data
Analytical workflows: chain Replicate queries with LlamaIndex's data connectors to build multi-source analytical reports
Replicate MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Replicate to LlamaIndex via MCP:
cancel_prediction
Cancels a prediction that is currently running
create_prediction
g., image generation, LLMs). Provide the model version ID and inputs as a JSON object. Starts a new model prediction on Replicate
get_account
Retrieves the authenticated Replicate account details
get_collection
Provide the collection slug (e.g., "text-to-image"). Retrieves a specific collection of models by its slug
get_model
Retrieves details for a specific model
get_prediction
). Retrieves the status and output of a prediction
list_collections
g., "Image-to-Text", "Audio Generation"). Lists curated collections of models
list_deployments
Lists your active model deployments on Replicate
list_hardware
Lists available GPU hardware options for running models
list_models
Lists public models available on Replicate
list_predictions
Lists recent predictions made by the user
search_models
Searches for public models on Replicate
Example Prompts for Replicate in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Replicate immediately.
"List my recent predictions."
"Query Replicate to search for 'TTS' models."
"Cancel the prediction that has the ID `p_abc123`."
Troubleshooting Replicate MCP Server with LlamaIndex
Common issues when connecting Replicate to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpReplicate + LlamaIndex FAQ
Common questions about integrating Replicate 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 Replicate 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 Replicate to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.