Bring Xbrl
to Pydantic AI
Learn how to connect SEC XBRL (Financial Reporting) to Pydantic AI and start using 4 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the SEC XBRL (Financial Reporting) MCP Server?
Connect your AI agent to the SEC EDGAR database and perform deep financial analysis using standardized XBRL data. This server provides programmatic access to the U.S. Securities and Exchange Commission's public filing infrastructure.
What you can do
- Filing History — Retrieve the complete submission history for any entity using its Central Index Key (CIK)
- Company Facts — Fetch the entire dictionary of XBRL facts reported by a company, covering all taxonomies (US-GAAP, IFRS, etc.)
- Concept Analysis — Drill down into specific financial concepts (e.g., Net Income, Assets) for a single company over time
- Market-Wide Frames — Aggregate specific financial data points across all reporting entities for a particular period and unit
How it works
- Subscribe to this server
- Enter your SEC User-Agent string (required by SEC Fair Access policy)
- Start querying financial statements from Claude, Cursor, or any MCP client
Who is this for?
- Financial Analysts — instantly pull raw XBRL data for modeling without manual spreadsheet entry
- Investors — monitor recent filings and compare metrics across industries using standardized frames
- Compliance Officers — verify submission histories and disclosure accuracy for specific CIKs
Built-in capabilities (4)
Get all XBRL disclosures for a single company concept
Get all company concepts data for a specific company
Includes metadata and recent filings. Get filing history for a specific entity
Get aggregated facts for a specific concept and period
Why Pydantic AI?
Pydantic AI validates every SEC XBRL (Financial Reporting) tool response against typed schemas, catching data inconsistencies at build time. Connect 4 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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your SEC XBRL (Financial Reporting) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your SEC XBRL (Financial Reporting) connection logic from agent behavior for testable, maintainable code
SEC XBRL (Financial Reporting) in Pydantic AI
SEC XBRL (Financial Reporting) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect SEC XBRL (Financial Reporting) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for SEC XBRL (Financial Reporting) in Pydantic AI
The SEC XBRL (Financial Reporting) 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. All 4 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
SEC XBRL (Financial Reporting) for Pydantic AI
Every tool call from Pydantic AI to the SEC XBRL (Financial Reporting) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I find the filing history for a specific company?
Use the get_submissions tool with the company's Central Index Key (CIK). For example, Apple is 320193. The tool automatically handles leading zeros.
Can I retrieve specific financial metrics like 'Net Income' for a company?
Yes. Use get_company_concept by providing the CIK, the taxonomy (usually 'us-gaap'), and the XBRL tag (like 'NetIncomeLoss').
How can I compare a single metric across all companies for a specific year?
Use the get_xbrl_frames tool. You can specify a concept, unit, and period (e.g., 'CY2023') to get data for every reporting entity in that timeframe.
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 XBRL (Financial Reporting) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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