Pydantic AISDK

UniCourt MCP Server

Bring Court Records
to Pydantic AI

Name: UniCourt MCP Server
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

Learn how to connect UniCourt to Pydantic AI and start using 27 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Delete Pacer CredentialGenerate TokenGet CaseGet Case Count AnalyticsGet Case Export CallbackGet Case Update StatusGet Document Order CallbackGet Norm AttorneyGet Norm JudgeGet Norm Law FirmGet Norm PartyGet Pacer CredentialImport CaseImport Pacer CaseOrder Case DocumentRequest Case ExportRequest Case UpdateSearch CasesSearch Norm AttorneySearch Norm JudgeSearch Norm Law FirmSearch Norm PartySearch Pacer Case LocatorTrack CaseTrack Norm AttorneyTrack Norm Law FirmUpdate Pacer Credential

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the UniCourt MCP Server?

Connect your UniCourt account to any AI agent to streamline legal research and litigation tracking through natural language.

What you can do

Case Search — Search across millions of court cases using keyword expressions and filters
Automated Tracking — Use track_case to monitor specific litigation and receive updates on a schedule
Normalized Data — Access structured profiles for attorneys, law firms, judges, and parties to perform deep background checks
Document Management — Order court documents and export case data directly into your workflow
Legal Analytics — Retrieve case counts and analytics to understand litigation trends

How it works

Subscribe to this server
Enter your UniCourt Access Token
Start querying legal records from Claude, Cursor, or any MCP-compatible client

Who is this for?

Legal Professionals — automate case monitoring and perform quick conflict checks
Researchers — gather large-scale litigation data for market analysis
Insurance & Finance — assess legal risks by inspecting party histories and active court cases

Built-in capabilities (27)

delete_pacer_credential

Delete PACER account credentials

generate_token

Generate a new UniCourt access token

get_case

Get details for a specific case

get_case_count_analytics

Get case count analytics by case type

get_case_export_callback

Get the file URL for a completed case export

get_case_update_status

Check the status of a requested case update

get_document_order_callback

Get the file URL for a completed document order

get_norm_attorney

Get analytics and details for a normalized attorney

get_norm_judge

Get analytics and details for a normalized judge

get_norm_law_firm

Get analytics and details for a normalized law firm

get_norm_party

Get analytics and details for a normalized party

get_pacer_credential

Retrieve current PACER account credentials status

import_case

High-priority case import

import_pacer_case

Import a case not in UniCourt via PACER

order_case_document

Order a court document

request_case_export

Request an export of case data as a ZIP file

request_case_update

Request an asynchronous update for a case

search_cases

g., caseName:pfizer). Search for court cases

search_norm_attorney

Search for normalized attorneys

search_norm_judge

Search for normalized judges

search_norm_law_firm

Search for normalized law firms

search_norm_party

Search for normalized parties

search_pacer_case_locator

Search PACER directly via Case Locator

track_case

Automatically update cases on a schedule

track_norm_attorney

Schedule recurring bar source refreshes for an attorney

track_norm_law_firm

Schedule recurring source refreshes for a law firm

update_pacer_credential

Manage PACER account credentials

Why Pydantic AI?

Pydantic AI validates every UniCourt tool response against typed schemas, catching data inconsistencies at build time. Connect 27 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
—
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your UniCourt integration code
—
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
—
Dependency injection system cleanly separates your UniCourt connection logic from agent behavior for testable, maintainable code

See it in action

UniCourt in Pydantic AI

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

UniCourt and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect UniCourt 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.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	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

Unlock for AI Agents View step-by-step setup guide for Pydantic AI →

Enterprise Security

Why teams choose Vinkius for UniCourt in Pydantic AI

The UniCourt 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 27 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.

Unlock for AI Agents View full UniCourt details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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

The Vinkius Advantage

How Vinkius secures
UniCourt for Pydantic AI

Every tool call from Pydantic AI to the UniCourt MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

How can I search for court cases involving a specific company?

Use the search_cases tool with a query like caseName:"Company Name". The agent will return a list of matching cases with their UniCourt IDs and basic details.

Can the AI automatically track a case for new updates?

Yes! By using the track_case tool with a specific caseId, you can set up automated monitoring. You can even specify a refreshWindow like '1d' for daily updates.

Is it possible to get professional background data for an attorney?

Absolutely. Use search_norm_attorney to find the attorney and then get_norm_attorney with their ID to retrieve detailed analytics and professional history.

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 UniCourt MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.