How to Use the Gripr MCP in LlamaIndex
Index construction safety data and log hours directly into your LlamaIndex knowledge base.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Gripr MCP to LlamaIndex
Create your Vinkius account to connect Gripr to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Index construction safety reports into LlamaIndex RAG
This integration allows LlamaIndex to query `list_forms` and load your HSE reports directly into a vector index. Your agent can then perform semantic search across all historical safety inspections. By converting live API data from `get_project_details` into indexable documents, you eliminate hallucinations. Your agent answers safety compliance questions using actual, verified project records.
Query active project tasks using your LlamaIndex agent
This MCP Server lets your LlamaIndex agent retrieve real-time task lists by invoking `list_tasks` and mapping the results to your semantic query index. This lets you ask natural language questions about outstanding safety hazards and get answers grounded in live data. The agent combines task data with customer information pulled via `list_customers`. This gives you a clear picture of project bottlenecks without digging through databases.
Track labor logs via MCP Server data loaders
This MCP Server exposes tools like `list_registered_hours` directly to your LlamaIndex data loaders. You can automatically index logged work hours to detect anomalies or missing time entries across your teams. If a worker forgets to log their time, your agent can verify their active status with `list_workspace_users`. It then uses `register_hours` to update the records automatically.
Set up Gripr MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 Gripr MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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 Gripr tools.",
)
response = await agent.run("List recent Gripr data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Gripr. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
Why Choose Vinkius
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Built-in savings
60%
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Common questions about Gripr MCP in LlamaIndex
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