Bring Property Listings
to Pydantic AI
Learn how to connect AgentFire to Pydantic AI and start using 10 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the AgentFire MCP Server?
Connect your AgentFire account to any AI agent and take full control of your real estate website and automated lead capture workflows through natural conversation.
What you can do
- Lead Portfolio Orchestration — List and manage all captured property inquiries programmatically, retrieving detailed lead profile metadata and contact tags
- Web Engagement Intelligence — Programmatically monitor property clicks and access engagement metadata to coordinate your sales follow-up strategy
- Property Graph Monitoring — Access real-time updates for active listings and track user interaction duration directly through your agent for instant reporting
- Metadata Management — Programmatically retrieve interest signals and search history to maintain a perfectly coordinated CRM record
- Operational Monitoring — Verify account-level API connectivity and monitor lead capture volume directly through your agent for perfectly coordinated service scaling
How it works
1. Subscribe to this server
2. Retrieve your API Key from your AgentFire dashboard (Integrations > REST API)
3. Start orchestrating your real estate growth from Claude, Cursor, or any MCP client
No more manual logging into WordPress or missing critical property inquiries. Your AI acts as your dedicated marketing coordinator and real estate architect.
Who is this for?
- Real Estate Agents — instantly retrieve lead summaries and monitor property interest using natural language commands
- Marketing Leads — verify individual lead engagement and track web conversions without leaving your workspace
- Developers — integrate high-speed AgentFire data into custom automation workflows through simple AI queries
Built-in capabilities (10)
Verify AgentFire API connectivity
Email is required. Create a new lead
Get lead details
Get listing details
Get your AgentFire profile
List all contacts
List all leads
List all property listings
Search property listings
Only provided fields are changed. Update a lead
Why Pydantic AI?
Pydantic AI validates every AgentFire 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.
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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 AgentFire 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 AgentFire connection logic from agent behavior for testable, maintainable code
AgentFire in Pydantic AI
AgentFire and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect AgentFire 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 | 3,400+ 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 AgentFire in Pydantic AI
The AgentFire 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 10 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
AgentFire for Pydantic AI
Every tool call from Pydantic AI to the AgentFire 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 my AgentFire API Key?
Log in to your AgentFire dashboard, navigate to Integrations > REST API, and copy your unique Access Token.
Can I see individual property clicks via AI?
Yes! The list_leads tool allows your agent to retrieve interest signals and property engagement metadata for all your contacts.
How do I list my active listings?
Use the list_listings tool to retrieve your complete directory along with the unique identifiers for all managed properties.
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 AgentFire MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
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Update: pip install --upgrade pydantic-ai