AlgoDocs 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 AlgoDocs as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 AlgoDocs. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in AlgoDocs?"
)
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 AlgoDocs MCP Server
Connect your AlgoDocs account to your AI agent to unlock professional automated document extraction. From automatically parsing invoices, receipts, and complex tables to auditing extraction models (extractors) and managing folder hierarchies, your agent handles your data ingestion pipeline through natural conversation.
LlamaIndex agents combine AlgoDocs 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
- Document Ingestion — Upload and parse documents from public URLs or Base64 strings for high-accuracy JSON extraction
- Extractor Oversight — List and retrieve details for your AI extractors to ensure the correct rulesets are applied to your docs
- Data Auditing — Retrieve structured JSON results for individual documents or list extracted data in bulk for entire extractors
- Folder Management — List and audit your folder hierarchy to organize your document processing projects
- Usage Monitoring — Quickly retrieve account details and API usage statistics directly from your chat interface
The AlgoDocs 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 AlgoDocs to LlamaIndex via MCP
Follow these steps to integrate the AlgoDocs 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 AlgoDocs
Why Use LlamaIndex with the AlgoDocs MCP Server
LlamaIndex provides unique advantages when paired with AlgoDocs through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AlgoDocs tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AlgoDocs tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AlgoDocs, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AlgoDocs tools were called, what data was returned, and how it influenced the final answer
AlgoDocs + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AlgoDocs MCP Server delivers measurable value.
Hybrid search: combine AlgoDocs real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AlgoDocs 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 AlgoDocs for fresh data
Analytical workflows: chain AlgoDocs queries with LlamaIndex's data connectors to build multi-source analytical reports
AlgoDocs MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect AlgoDocs to LlamaIndex via MCP:
get_api_usage
Get usage stats
get_document_data
Get parsed data
get_document_status
Check processing status
get_folder_details
Get folder metadata
get_my_account
Check account status
list_extractor_data
Bulk extraction results
list_extractors
List AI extractors
list_folders
List storage folders
list_recent_documents
List latest parsed docs
upload_document_from_url
Parse document from URL
Example Prompts for AlgoDocs in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AlgoDocs immediately.
"List all extractors in my AlgoDocs account."
"Parse this invoice URL: https://example.com/inv.pdf using extractor ID 'ext_123'."
"Show the extracted data for document ID 'doc_98765'."
Troubleshooting AlgoDocs MCP Server with LlamaIndex
Common issues when connecting AlgoDocs to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAlgoDocs + LlamaIndex FAQ
Common questions about integrating AlgoDocs 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 AlgoDocs 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 AlgoDocs to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.