Docparser 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 Docparser 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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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 Docparser. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Docparser?"
)
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 Docparser MCP Server
Integrate Docparser, the leading document data extraction platform, directly into your AI workflow. Automate the extraction of structured data from PDFs, scanned documents, and images, monitor your parser configurations, and retrieve parsed results using natural language.
LlamaIndex agents combine Docparser 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
- Parser Oversight — List and retrieve detailed settings and status for all your document parsers and extraction rules.
- Data Intelligence — Access the actual structured data extracted from your documents, including table data and custom fields.
- Document Tracking — Monitor the processing status of your uploaded documents and identify any extraction failures.
- Result Auditing — Retrieve a chronological feed of recent extraction results across all your active parsers.
The Docparser 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 Docparser to LlamaIndex via MCP
Follow these steps to integrate the Docparser 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 Docparser
Why Use LlamaIndex with the Docparser MCP Server
LlamaIndex provides unique advantages when paired with Docparser through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Docparser tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Docparser tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Docparser, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Docparser tools were called, what data was returned, and how it influenced the final answer
Docparser + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Docparser MCP Server delivers measurable value.
Hybrid search: combine Docparser real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Docparser 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 Docparser for fresh data
Analytical workflows: chain Docparser queries with LlamaIndex's data connectors to build multi-source analytical reports
Docparser MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Docparser to LlamaIndex via MCP:
get_docparser_account_metadata
Retrieve metadata and usage limits for your Docparser account
get_document_extraction_results
Get the actual data extracted from a specific document
get_parser_details
Get detailed settings and status for a specific document parser
list_document_parsers
List all document parsers configured in your Docparser account
list_documents_awaiting_parsing
List documents that are currently in the parsing queue
list_failed_document_extractions
Identify documents that failed the parsing or extraction process (mock logic)
list_parsed_documents
List all documents processed by a specific parser
list_recent_extractions
List the most recent document extraction results across all parsers
quick_parser_health_audit
Retrieve a high-level summary of parser activity and success rates
search_parsed_documents
Search for parsed documents by filename within a parser
Example Prompts for Docparser in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Docparser immediately.
"List all documents processed by the 'Invoices' parser."
"Show me the extracted data for document 'DOC-9988' in the 'Orders' parser."
"Are there any document extractions that failed today?"
Troubleshooting Docparser MCP Server with LlamaIndex
Common issues when connecting Docparser to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDocparser + LlamaIndex FAQ
Common questions about integrating Docparser 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 Docparser with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
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 Docparser to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.