OpenCritic MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add OpenCritic as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 OpenCritic. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in OpenCritic?"
)
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 OpenCritic MCP Server
Equip your AI agent with the most reliable video game intelligence available via OpenCritic. This unified server provides your agent with instant access to aggregate review scores, detailed critic snippets, and historical rankings for thousands of games. Your agent can instantly search for specific titles, audit recent review trends, and retrieve the Hall of Fame for any given year without you ever needing to browse a review site. Whether you are identifying the best games of the year or auditing individual critic opinions, your agent acts as a dedicated gaming analyst through natural conversation.
LlamaIndex agents combine OpenCritic tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Game Discovery — Search for thousands of video games by title and retrieve their OpenCritic rating and tier.
- Review Auditing — Fetch detailed snippets and scores from individual critics and publications for any game.
- Market Trends — Retrieve lists of upcoming releases and currently popular/trending games on the platform.
- Historical Rankings — Access the 'Hall of Fame' to identify the top-rated games for a specific year.
- Critic Intelligence — List and inspect recognized critics and publications to understand the source of reviews.
The OpenCritic MCP Server exposes 8 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 OpenCritic to LlamaIndex via MCP
Follow these steps to integrate the OpenCritic 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 8 tools from OpenCritic
Why Use LlamaIndex with the OpenCritic MCP Server
LlamaIndex provides unique advantages when paired with OpenCritic through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OpenCritic tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OpenCritic tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OpenCritic, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OpenCritic tools were called, what data was returned, and how it influenced the final answer
OpenCritic + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OpenCritic MCP Server delivers measurable value.
Hybrid search: combine OpenCritic real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OpenCritic 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 OpenCritic for fresh data
Analytical workflows: chain OpenCritic queries with LlamaIndex's data connectors to build multi-source analytical reports
OpenCritic MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect OpenCritic to LlamaIndex via MCP:
get_game_details
Get game details
get_game_reviews
Get game reviews
get_hall_of_fame
Get Hall of Fame games
get_popular_games
Get popular games
get_recent_reviews
Get recent reviews
get_upcoming_games
Get upcoming games
list_critics
List critics
search_games
Search for video games
Example Prompts for OpenCritic in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OpenCritic immediately.
"What is the OpenCritic score for 'Elden Ring'?"
"List the top games from 2023."
"Show me upcoming games on OpenCritic."
Troubleshooting OpenCritic MCP Server with LlamaIndex
Common issues when connecting OpenCritic to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpenCritic + LlamaIndex FAQ
Common questions about integrating OpenCritic 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 OpenCritic with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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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 OpenCritic to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.