How do I find my AlgoDocs API Key?

Log in to AlgoDocs, go to your **Account Settings**, and you will find your API key there. You will need this along with your registered email for authentication.

What is an 'Extractor' in AlgoDocs?

An Extractor is a set of rules and AI models configured to pull specific fields from a certain type of document (e.g., an Invoice Extractor). You must specify an `extractor_id` when uploading documents.

Can I retrieve data from a previously processed document?

Yes! Use the `get_document_data` tool and provide the unique `document_id`. Your agent will retrieve the extracted structured data from AlgoDocs storage.

AlgoDocs MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: AlgoDocs
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect AlgoDocs through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to AlgoDocs "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in AlgoDocs?"
    )
    print(result.data)

asyncio.run(main())

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* 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.

Pydantic AI validates every AlgoDocs tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the AlgoDocs MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the AlgoDocs MCP Server

Pydantic AI provides unique advantages when paired with AlgoDocs through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your AlgoDocs integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your AlgoDocs connection logic from agent behavior for testable, maintainable code

AlgoDocs + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the AlgoDocs MCP Server delivers measurable value.

Type-safe data pipelines: query AlgoDocs with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple AlgoDocs tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query AlgoDocs and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock AlgoDocs responses and write comprehensive agent tests

AlgoDocs MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect AlgoDocs to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting AlgoDocs to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

AlgoDocs + Pydantic AI FAQ

Common questions about integrating AlgoDocs MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your AlgoDocs MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.