Bring Dividends
to Pydantic AI
Learn how to connect Massive to Pydantic AI and start using 1 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 Massive MCP Server?
Connect to Massive to retrieve comprehensive historical dividend data for thousands of tickers. Empower your AI agent to perform deep financial analysis and equity research through natural conversation.
What you can do
- Historical Dividends — Fetch full records of cash distributions for any supported stock ticker from the Massive API.
- Granular Filtering — Filter results by ex-dividend date, frequency (annual, quarterly), or specific distribution types.
- Distribution Types — Identify recurring, special, supplemental, or irregular dividends to understand company payout patterns.
- Data Analysis — Sort and limit results (up to 5000 records) to build precise financial models or investment reports.
How it works
- Subscribe to this server
- Enter your Massive API Key
- Start querying financial data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Financial Analysts — quickly retrieve payout histories to calculate yields and dividend growth rates.
- Investors — check upcoming or historical ex-dividend dates to manage portfolio timing.
- Developers — integrate reliable financial distribution data into trading bots or research tools without complex scraping.
Built-in capabilities (1)
Retrieve historical cash dividends for a ticker
Why Pydantic AI?
Pydantic AI validates every Massive tool response against typed schemas, catching data inconsistencies at build time. Connect 1 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.
- —
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 Massive 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 Massive connection logic from agent behavior for testable, maintainable code
Massive in Pydantic AI
Massive and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Massive 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 Massive in Pydantic AI
The Massive 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 1 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
Massive for Pydantic AI
Every tool call from Pydantic AI to the Massive MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I filter dividends by a specific date?
Yes. You can use the ex_dividend_date parameter in the list_dividends tool to find distributions occurring on or after a specific YYYY-MM-DD date.
What types of dividend distributions can I identify?
The list_dividends tool supports filtering by distribution_type, including 'recurring', 'special', 'supplemental', 'irregular', and 'unknown'.
How many dividend records can I retrieve at once?
By default, the list_dividends tool returns 100 results, but you can increase the limit parameter up to a maximum of 5000 records per query.
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 Massive 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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