How to Use the Fixably MCP in LlamaIndex

Q: How do I configure LlamaIndex to query the Fixably MCP Server?

Use pip install llama-index-tools-mcp and initialize the BasicMCPClient. Wrap it in McpToolSpec and call totoollistasync() to expose the repair and inventory tools to your FunctionAgent.

Q: Can LlamaIndex search through attached files inside Fixably?

LlamaIndex can use listorderfiles to retrieve file references associated with a repair ticket. You can then use those references to index metadata, helping your agent locate schematics or photos linked to specific jobs.

Q: How does this Fixably MCP Server prevent hallucinations in LlamaIndex?

It feeds real-time data from getorder directly into the agent's context window. Because the agent queries actual ticket statuses instead of guessing, its answers are always grounded in live operational facts.

Q: Can I filter which inventory tools the agent can access?

Yes. Use the allowedtools filter during setup to restrict your LlamaIndex agent to read-only queries like liststockproducts, blocking it from executing state-changing tools like updateorder.

Index live Fixably repair data and stock statuses directly into your LlamaIndex knowledge graphs.

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Connect Fixably MCP to LlamaIndex

Create your Vinkius account to connect Fixably 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.

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Setup Fixably with LlamaIndex

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Ground your RAG queries in live inventory stock levels

The `get_stock` tool pulls current inventory levels directly into your LlamaIndex query engine. Your agent uses this live data to answer customer questions about part availability without relying on stale, cached database records. By converting the output of `list_stocks` into queryable index nodes, LlamaIndex ensures your RAG pipeline always knows which warehouse holds the replacement parts. This eliminates hallucinated stock counts during support agent conversations.

Build semantic search indexes over Fixably customer profiles

The `list_customers` tool retrieves the active customer directory to build a searchable vector index. LlamaIndex ingests these records, allowing your support agent to find matching profiles using natural language queries instead of exact ID matches. When a user asks about a client, the agent calls `get_customer` to pull the precise details and updates the local vector store. This MCP Server integration keeps your local knowledge base perfectly synchronized with your live CRM data.

Search past repair histories using this MCP Server

The `list_orders` tool fetches historical repair tickets to populate your LlamaIndex document store. Your agent indexes these past tickets so technicians can query previous solutions for similar hardware failures. When a new device comes in, the agent checks `get_order` details and compares them against the indexed history. It retrieves the exact steps used to fix identical issues, speeding up diagnostic times at the repair bench.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fixably. 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.

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Common questions about Fixably MCP in LlamaIndex

Use `pip install llama-index-tools-mcp` and initialize the `BasicMCPClient`. Wrap it in `McpToolSpec` and call `to_tool_list_async()` to expose the repair and inventory tools to your `FunctionAgent`.

LlamaIndex can use `list_order_files` to retrieve file references associated with a repair ticket. You can then use those references to index metadata, helping your agent locate schematics or photos linked to specific jobs.

It feeds real-time data from `get_order` directly into the agent's context window. Because the agent queries actual ticket statuses instead of guessing, its answers are always grounded in live operational facts.

Yes. Use the `allowed_tools` filter during setup to restrict your LlamaIndex agent to read-only queries like `list_stock_products`, blocking it from executing state-changing tools like `update_order`.

All inventory counts and warehouse location data fetched via `get_stock` pass through an ephemeral, zero-trust V8 sandbox. Vinkius never caches your warehouse layout or stock levels, keeping your proprietary supply chain data strictly private.

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