Pointagram MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Player, Get Player Stats, List Players, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Pointagram 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 App Connector for LlamaIndex
The Pointagram app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Pointagram. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Pointagram?"
)
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 Pointagram MCP Server
Connect your Pointagram account to any AI agent to streamline your team gamification and engagement workflows. Pointagram provides a powerful platform for programmatically managing players, teams, and score series through its robust v2.0 REST API.
LlamaIndex agents combine Pointagram tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Player Orchestration — List and create player profiles with detailed tracking of levels, nicknames, and avatars
- Scoring Event Automation — Post real-time scoring events to update player points across different score series programmatically
- Team Management — Access and monitor your gamification teams and retrieve detailed membership metadata
- Score Series Discovery — List all your active score series to understand how points are accumulated and distributed
- Performance Intelligence — Retrieve granular player stats and rankings using natural language commands
The Pointagram MCP Server exposes 6 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.
All 6 Pointagram tools available for LlamaIndex
When LlamaIndex connects to Pointagram through Vinkius, your AI agent gets direct access to every tool listed below — spanning gamification, employee-engagement, leaderboards, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Pass data as a JSON string. Create a new player
Get stats for a player
List all Pointagram players
List all score series
List all Pointagram teams
Pass data as a JSON string. Post a scoring event
Connect Pointagram to LlamaIndex via MCP
Follow these steps to wire Pointagram into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Pointagram MCP Server
LlamaIndex provides unique advantages when paired with Pointagram through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Pointagram tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Pointagram tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Pointagram, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Pointagram tools were called, what data was returned, and how it influenced the final answer
Pointagram + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Pointagram MCP Server delivers measurable value.
Hybrid search: combine Pointagram real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Pointagram 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 Pointagram for fresh data
Analytical workflows: chain Pointagram queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Pointagram in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Pointagram immediately.
"List all active players in Pointagram."
"Post 100 points for player '123' in the 'Sales Bonus' series."
"Show me the top 5 score series."
Troubleshooting Pointagram MCP Server with LlamaIndex
Common issues when connecting Pointagram to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPointagram + LlamaIndex FAQ
Common questions about integrating Pointagram MCP Server with LlamaIndex.