Parseur MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Parseur as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Parseur. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Parseur?"
)
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 Parseur MCP Server
Bring Parseur Document Extraction arrays directly into your AI workflows. By explicitly mapping into powerful OCR and templating engines, your agent can push unstructured PDFs or bulk emails into remote routing limits, parsing exact text fields securely. Extract fields, examine documents, list defined parse-templates, and retry pipelines without manual intervention.
LlamaIndex agents combine Parseur tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Mailboxes & Templates — Examine specifically bound mailboxes tracking which explicit templates dictate data extraction limits mapped natively
- Document Navigation — Extract properties showing precisely which unstructured strings were identified inside uploaded payloads checking
status: parsedcorrectly - Payload Uploading — Instruct the node limits mapping
upload_documentgenerating raw payloads routing straight into the engine for OCR logic - Job Management — Discover disconnected states mitigating failed validations by pushing
retry_documentinstantly forcing physical pipeline resets
The Parseur MCP Server exposes 10 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.
How to Connect Parseur to LlamaIndex via MCP
Follow these steps to integrate the Parseur MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Parseur
Why Use LlamaIndex with the Parseur MCP Server
LlamaIndex provides unique advantages when paired with Parseur through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Parseur tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Parseur tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Parseur, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Parseur tools were called, what data was returned, and how it influenced the final answer
Parseur + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Parseur MCP Server delivers measurable value.
Hybrid search: combine Parseur real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Parseur 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 Parseur for fresh data
Analytical workflows: chain Parseur queries with LlamaIndex's data connectors to build multi-source analytical reports
Parseur MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Parseur to LlamaIndex via MCP:
create_mailbox
The type determines the parsing engine (e.g., "pdf", "email", "attachment"). Once created, you can configure templates and forward documents to the mailbox for automatic extraction. Create a new Parseur mailbox for document parsing
create_template
Pass the template name and a JSON config string defining field mappings. Parseur will use this template to extract structured data from matching documents. Create a new extraction template for a Parseur mailbox
get_document_data
Fields depend on the template configuration (e.g., invoice_number, total_amount, line_items). Only works for documents with status "processed". Retrieve the fully extracted JSON data from a parsed document
get_document_details
Does not include the parsed data itself — use get_document_data for that. Get metadata of a single parsed document
get_mailbox
Use this to verify mailbox setup before sending documents. Get detailed configuration of a specific Parseur mailbox
list_documents
Each entry includes document ID, status (processed, failed, pending), and metadata like sender and received date. List all parsed documents inside a Parseur mailbox
list_mailboxes
Each mailbox represents a parsing pipeline for a specific document type (invoices, receipts, emails). Use the returned mailbox IDs for subsequent operations like listing documents or uploading files. List all Parseur parsing mailboxes
list_templates
Templates define the extraction rules (field names, locations, regex patterns) used to pull structured data from incoming documents. List available extraction templates for a Parseur mailbox
retry_document
Useful after fixing template rules or when the original parse failed due to a transient error. The document will be matched against the latest template rules. Retry parsing a failed or errored Parseur document
upload_document
eml) to the specified mailbox for automatic parsing. The document enters the processing queue and will be parsed according to the mailbox template. Returns the new document ID for tracking. Upload a document URL to a Parseur mailbox for parsing
Example Prompts for Parseur in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Parseur immediately.
"Check my Parseur mailboxes to find the specific bounding IDs."
"Get the data schema parsed tightly inside document doc_987."
"Upload this snippet of parsed text directly into Mailbox xyz12 for OCR processing."
Troubleshooting Parseur MCP Server with LlamaIndex
Common issues when connecting Parseur to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpParseur + LlamaIndex FAQ
Common questions about integrating Parseur MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Parseur with your favorite client
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Parseur to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.