XBRL (eXtensible Business Reporting Language) is a standardized format for financial data required by the SEC since 2009. It tags every financial number (revenue, assets, debt, etc.) with a machine-readable label, making it possible to extract and compare financial data across companies automatically.

What is a US-GAAP concept?

US-GAAP comprises the standard accounting principles in the US. Each accounting term (like 'Revenues' or 'NetIncomeLoss') maps directly to specific facts filed in XBRL format.

The SEC provides 'Frames' to view an entire industry's metric at once (e.g., all revenues in Q1 2024) instead of polling company-by-company.

SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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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 SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison "
            "(4 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison?"
    )
    print(result.data)

asyncio.run(main())

SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison

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About SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server

SEC XBRL financial data.

Pydantic AI validates every SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison tool response against typed schemas, catching data inconsistencies at build time. Connect 4 tools through the 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.

4 Tools

Key Financials — Revenue, income, assets, EPS, cash
Financial Metric — Any US-GAAP concept
All Facts — Complete XBRL data dump
Industry Comparison — Cross-company metric frames

Zero Auth

Like a free Bloomberg terminal

The SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server exposes 4 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 SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to Pydantic AI via MCP

Follow these steps to integrate the SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison 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 4 tools from SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison with type-safe schemas

Why Use Pydantic AI with the SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server

Pydantic AI provides unique advantages when paired with SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison 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 SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison integration code

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

Dependency injection system cleanly separates your SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison connection logic from agent behavior for testable, maintainable code

SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server delivers measurable value.

Type-safe data pipelines: query SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison responses and write comprehensive agent tests

SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to Pydantic AI via MCP:

get_all_company_facts

This is the raw, comprehensive dataset — hundreds of concepts across multiple years. Use get_key_financials for a curated summary, or this for deep analysis. Get ALL XBRL financial facts for a company — complete financial data dump

get_financial_metric

Common concepts: Revenues, NetIncomeLoss, Assets, Liabilities, StockholdersEquity, EarningsPerShareBasic, LongTermDebt, ResearchAndDevelopmentExpense, CashAndCashEquivalentsAtCarryingValue, CommonStockSharesOutstanding. If the concept is not found, returns available concepts. Get a specific US-GAAP financial concept for a company (e.g., Revenue, Debt, R&D)

get_industry_comparison

Useful for industry comparison and screening. Example: get all companies' Revenue for CY2024. Period format: CY2024 (annual), CY2024Q1 (quarterly), CY2024Q1I (instant). Compare a financial metric across ALL companies — industry-wide XBRL frame data

get_key_financials

Returns the most recent 5 reported values across 10-K and 10-Q filings. This is like a mini Bloomberg terminal — for free. Get key financial data for a company — revenue, net income, assets, equity, EPS, cash

Example Prompts for SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison immediately.

"Get Apple's key financial data — revenue, income, assets, and EPS"

"What is Meta's exact Research and Development Expense?"

"Show me a comparison of Revenue across all companies for CY2024"

Troubleshooting SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server with Pydantic AI

Common issues when connecting SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison + Pydantic AI FAQ

Common questions about integrating SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison 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 SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.