How to Use the Albi Restoration MCP in LlamaIndex

Q: How do I connect the Albi Restoration MCP Server to LlamaIndex?

Run pip install llama-index-tools-mcp and initialize BasicMCPClient with your Vinkius URL. Wrap it in McpToolSpec and call totoollistasync() to feed the tools into your LlamaIndex FunctionAgent.

Q: Can I limit which Albi Restoration tools LlamaIndex can access?

Yes, you can use the allowedtools filter when initializing your LlamaIndex McpToolSpec. This prevents the agent from calling writing tools like createnewrestorationproject if you only want a read-only search index.

Q: How does semantic search work with Albi Restoration project logs?

LlamaIndex calls listprojectactivities to retrieve active task descriptions. It then converts this Albi Restoration text into vector embeddings, allowing you to search for project milestones using natural language.

Q: Does this integration support real-time data updates?

Yes, you can configure LlamaIndex to fetch fresh data via checkapihealth and update its index dynamically. This ensures your search results reflect active Albi Restoration field operations.

Index your Albi Restoration projects into LlamaIndex to query real-time job data using semantic search.

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Connect Albi Restoration MCP to LlamaIndex

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Setup Albi Restoration with LlamaIndex

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Index Albi Restoration data into LlamaIndex

Stop digging through old project folders to find contractor details in Albi Restoration. Your LlamaIndex agent can call `list_restoration_projects` and index the results into a vector store. The LlamaIndex agent uses `get_project_details` to pull granular data and generate embeddings. When you query LlamaIndex about past water damage jobs, the system retrieves actual Albi Restoration API data instead of guessing.

Query financial KPIs using LlamaIndex RAG

Combine static documents with live restoration metrics inside your LlamaIndex pipeline. Your pipeline can fetch live data using this MCP Server to run `get_project_financial_kpis` and merge it with historical PDF reports. By using `list_inventory_equipment`, your LlamaIndex agent can check real-time asset availability while answering user queries. The system matches physical Albi Restoration inventory records with your semantic search queries automatically.

Search contacts using this MCP Server

Keep your contact directories synchronized across your LlamaIndex knowledge bases. Your agent uses `list_restoration_contacts` and `list_restoration_organizations` to map relationships between Albi Restoration adjusters and contractors. If a new contact is added via `create_new_contact`, the LlamaIndex agent immediately updates the local vector index. This keeps your semantic search results accurate without requiring a full database rebuild.

Setup guide

Set up Albi Restoration 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 Albi Restoration 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 Albi Restoration tools.",
)
response = await agent.run("List recent Albi Restoration 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 Albi. 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 Albi Restoration MCP in LlamaIndex

Run `pip install llama-index-tools-mcp` and initialize `BasicMCPClient` with your Vinkius URL. Wrap it in `McpToolSpec` and call `to_tool_list_async()` to feed the tools into your LlamaIndex `FunctionAgent`.

Yes, you can use the `allowed_tools` filter when initializing your LlamaIndex `McpToolSpec`. This prevents the agent from calling writing tools like `create_new_restoration_project` if you only want a read-only search index.

LlamaIndex calls `list_project_activities` to retrieve active task descriptions. It then converts this Albi Restoration text into vector embeddings, allowing you to search for project milestones using natural language.

Yes, you can configure LlamaIndex to fetch fresh data via `check_api_health` and update its index dynamically. This ensures your search results reflect active Albi Restoration field operations.

Your Albi Restoration contact lists and financial KPIs remain private because Vinkius executes the MCP Server in an isolated, ephemeral V8 sandbox. No data is stored on Vinkius, and communication with LlamaIndex is encrypted in transit.

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