Extracta MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Extracta through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Extracta "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Extracta?"
)
print(result.data)
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 Extracta MCP Server
Connect your Extracta.ai account to any AI agent and take full control of your automated data extraction and document classification through natural conversation.
Pydantic AI validates every Extracta 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
- Extraction Orchestration — Create and configure new data extraction processes by defining JSON schemas for fields like dates, amounts, and item descriptions natively
- Live Document Processing — Submit publicly accessible file URLs (PDF, JPG, PNG) to trigger asynchronous extraction workflows and retrieve structured JSON data seamlessly
- AI Classification — Set up document classification rules to automatically sort documents into types like invoices, receipts, or contracts based on AI predictions
- Result Auditing — Retrieve extraction status and finalized structured data for specific documents, evaluating confidence scores and predicted categories flawlessly
- Batch History Monitoring — Fetch paginated lists of previously extracted documents and their associated data payloads to track historical processing limitlessly
- Configuration Mutation — Update existing extraction settings and mapping rules without creating new endpoints to refine your data parsing logic
- Workflow Management — View and manage extraction and classification configurations, including configured fields and webhook settings securely
The Extracta 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 Extracta to Pydantic AI via MCP
Follow these steps to integrate the Extracta 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 Extracta with type-safe schemas
Why Use Pydantic AI with the Extracta MCP Server
Pydantic AI provides unique advantages when paired with Extracta 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 Extracta integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Extracta connection logic from agent behavior for testable, maintainable code
Extracta + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Extracta MCP Server delivers measurable value.
Type-safe data pipelines: query Extracta with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Extracta tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Extracta and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Extracta responses and write comprehensive agent tests
Extracta MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Extracta to Pydantic AI via MCP:
create_classification
g. invoice, receipt, contract). Pass JSON schema defining categories. Create a new Extracta document classification setup
create_extraction
g. language, format, expected fields like invoice_date, total_amount). Returns a new extractionId used for subsequent document processing. Create a new Extracta.ai data extraction process
delete_extraction
Subsequent uploads to this extractionId will fail. Delete an Extracta.ai extraction process
get_batch_results
Get bulk historical results from an Extraction process
get_classification_results
Get the predicted document category from Extracta
get_results
If not completed, it will indicate processing status. Get extraction results for a specific document
update_extraction
Modifies mapping rules without needing to create a new endpoint. Update an existing Extracta extraction configuration
upload_file_url
Returns a documentId. Use ea.get_results to poll for extracted data. Upload a document URL to Extracta for processing
view_classification
View details of an existing document classification process
view_extraction
View configuration of an existing Extracta extraction process
Example Prompts for Extracta in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Extracta immediately.
"Create an extraction process for invoices with fields: date, vendor, total"
"Extract data from this receipt URL: https://example.com/receipt.pdf"
"What type of document is doc_789 according to my classification rules?"
Troubleshooting Extracta MCP Server with Pydantic AI
Common issues when connecting Extracta to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiExtracta + Pydantic AI FAQ
Common questions about integrating Extracta MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Extracta 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 Extracta to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.