Pydantic AISDK

Convex MCP Server

Bring Real Time Database
to Pydantic AI

Name: Convex MCP Server
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

Learn how to connect Convex 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 Subscribers

Run ActionRun FunctionRun MutationRun Query

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 Convex MCP Server?

Connect your Convex deployment to any AI agent and manage your application's data and logic through natural conversation. This server allows you to interact with your real-time database and serverless functions without leaving your AI interface.

What you can do

Data Fetching — Execute read-only queries to retrieve documents and state from your Convex tables.
Transactional Updates — Run mutations to modify data with full ACID guarantees directly from the agent.
Side Effects & APIs — Trigger Convex actions for external API calls, heavy computation, or non-transactional logic.
Flexible Execution — Call functions using standard colon notation or URL-style identifiers for maximum compatibility.

How it works

Subscribe to this server
Enter your Convex Deployment URL (and optional Access Key)
Start querying and mutating your data from Claude, Cursor, or any MCP-compatible client

Who is this for?

Full-stack Developers — Debug data, run migrations, or check state directly from your IDE or chat.
Product Managers — Query live application metrics and user data using natural language without writing code.
Support Teams — Inspect and update user records or trigger administrative actions through a secure AI interface.

Built-in capabilities (4)

run_action

Call a Convex action function

run_function

g., "messages/list" instead of "messages:list"). Call a Convex function by its URL identifier

run_mutation

Call a Convex mutation function

run_query

Use this for fetching data. Call a Convex query function

Why Pydantic AI?

Pydantic AI validates every Convex 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.

—
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 Convex integration code
—
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
—
Dependency injection system cleanly separates your Convex connection logic from agent behavior for testable, maintainable code

See it in action

Convex in Pydantic AI

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

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

Teams that connect Convex 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 Convex in Pydantic AI

The Convex 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.

Unlock for AI Agents View full Convex 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
Convex for Pydantic AI

Every tool call from Pydantic AI to the Convex 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

What is the difference between run_query and run_mutation?

Use run_query for read-only operations that fetch data. Use run_mutation when you need to write, update, or delete data transactionally in your database.

Can I call external APIs using this server?

Yes, by using the run_action tool. Actions in Convex are designed for side effects like calling third-party APIs or performing long-running tasks.

How do I reference functions in subdirectories?

You can use run_query with colon notation (e.g., 'folder/file:function') or use run_function which accepts URL-style identifiers with slashes.

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