How to Use the Airparser MCP in LlamaIndex

Q: How do I configure the Airparser MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient. Pass the tools to your FunctionAgent using McpToolSpec to start querying your inboxes.

Q: Can LlamaIndex query specific Airparser inboxes?

Yes. The agent uses listinboxes to find the correct target and then retrieves the schema. This allows LlamaIndex to structure its vector queries based on that specific inbox's fields.

Q: How does LlamaIndex handle large PDF extractions via Airparser?

Your agent triggers parsedocumentasync to avoid timing out on large files. It then polls getdocumentdetails until the extraction completes, then indexes the final JSON.

Q: Can I filter which tools LlamaIndex has access to?

Yes. You can use the allowedtools filter during setup to restrict your agent to specific operations like document retrieval while blocking webhook modifications.

Index parsed PDF and email data directly into LlamaIndex vector stores using this managed MCP Server.

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Connect Airparser MCP to LlamaIndex

Create your Vinkius account to connect Airparser 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 Airparser with LlamaIndex

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Convert unstructured files into queryable indexes

`parse_document_sync` processes raw PDFs, images, and emails into structured JSON on the fly. LlamaIndex takes this clean output and indexes it directly into your vector database, bypassing the need for manual chunking. You avoid the loss of context that happens with standard text splitters. Because the data is already structured, your RAG pipeline queries specific fields instead of searching through raw, noisy text blocks.

Map document structures to your knowledge base

`get_inbox_schema` retrieves the exact layout of your extracted data fields. Your LlamaIndex agent uses this schema to build precise metadata filters, ensuring search queries target the right document properties. This makes your semantic search highly accurate. Instead of guessing where an invoice number or date is, your agent checks `get_document_details` and maps those fields directly to vector metadata.

Feed your LlamaIndex RAG pipeline with this MCP Server

`list_documents` lets your agent scan incoming files to find unindexed content. Once found, `parse_document_async` runs in the background, updating your vector store as soon as the structured extraction completes. Your knowledge base stays fresh without manual imports. The agent coordinates the entire flow, checking `get_inbox_details` to monitor inbox status and ingest new data automatically.

Setup guide

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

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient`. Pass the tools to your `FunctionAgent` using `McpToolSpec` to start querying your inboxes.

Yes. The agent uses `list_inboxes` to find the correct target and then retrieves the schema. This allows LlamaIndex to structure its vector queries based on that specific inbox's fields.

Your agent triggers `parse_document_async` to avoid timing out on large files. It then polls `get_document_details` until the extraction completes, then indexes the final JSON.

Yes. You can use the `allowed_tools` filter during setup to restrict your agent to specific operations like document retrieval while blocking webhook modifications.

All document extraction, including parsed PDFs, emails, images, and metadata, runs inside a secure sandbox. Vinkius handles the authorization token securely, ensuring your raw files are never exposed.

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