Smithery MCP Server for Pydantic AI 11 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Smithery through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Smithery "
"(11 tools)."
),
)
result = await agent.run(
"What tools are available in Smithery?"
)
print(result.data)
asyncio.run(main())
* 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
About Smithery MCP Server
What you can do
Connect AI agents to the Smithery Registry for comprehensive MCP server discovery and management:
Pydantic AI validates every Smithery tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through the 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.
- Search MCP servers — find servers by name, description, or tags with semantic search
- Get server details — review metadata, verification status, and user counts
- Discover tools — list all tools (functions) exposed by any registered MCP server
- Discover resources — list all data resources available from MCP servers
- Discover prompts — list all prompt templates exposed by MCP servers
- Create connections — connect to MCP servers via Smithery Connect with automatic OAuth handling
- Manage connections — list, inspect, and remove MCP server connections
- Generate service tokens — create scoped, time-limited tokens for frontend/agent access
- View analytics — monitor server usage, adoption trends, and performance metrics
The Smithery MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Smithery to Pydantic AI via MCP
Follow these steps to integrate the Smithery MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 11 tools from Smithery with type-safe schemas
Why Use Pydantic AI with the Smithery MCP Server
Pydantic AI provides unique advantages when paired with Smithery through the Model Context Protocol.
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 Smithery integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Smithery connection logic from agent behavior for testable, maintainable code
Smithery + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Smithery MCP Server delivers measurable value.
Type-safe data pipelines: query Smithery with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Smithery tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Smithery and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Smithery responses and write comprehensive agent tests
Smithery MCP Tools for Pydantic AI (11)
These 11 tools become available when you connect Smithery to Pydantic AI via MCP:
create_connection
Smithery handles OAuth, tokens, and sessions automatically. Requires the server namespace and connection configuration (mcpUrl, optional headers, metadata). Returns the connection ID, status, and server info. Use this to integrate MCP servers into your applications without managing authentication complexity. Create a new connection to an MCP server via Smithery Connect
create_service_token
The token has limited permissions defined by the policy (namespaces, resources, operations, metadata, TTL). Returns the token string. Use this to provide secure, time-limited access to MCP servers without exposing your main API key. Generate a scoped service token for frontend/agent access to MCP servers
delete_connection
This action cannot be undone. Requires namespace and connection ID. Use this to clean up unused connections or revoke access. Remove an MCP server connection
get_connection
Requires namespace and connection ID. Use this to review connection details or troubleshoot connectivity issues. Get detailed information about a specific MCP connection
get_server_analytics
Requires the server qualified name. Use this to monitor server adoption, identify usage trends, or troubleshoot performance issues. Get usage analytics for a specific MCP server
get_server_details
Requires the qualified name (e.g., "smithery/hello-world" or "github/github") from search_servers results. Use this to review server capabilities before connecting. Get detailed information about a specific MCP server from the Smithery registry
get_server_prompts
Returns prompt names, descriptions, and argument definitions. Requires the server qualified name. Use this to discover reusable prompt workflows available from the server. List all prompt templates exposed by a specific MCP server
get_server_resources
Returns resource URIs, names, descriptions, and MIME types. Requires the server qualified name. Use this to understand what data the server provides read access to. List all resources exposed by a specific MCP server
get_server_tools
Returns tool names, descriptions, input schemas, and annotations. Requires the server qualified name. Use this to understand what actions the server can perform before connecting it to your agents. List all tools exposed by a specific MCP server
list_connections
Returns connection IDs, names, statuses, creation dates, and metadata. Use this to audit which connections are active, review connection configurations, or identify unused connections. List all connections for a specific MCP server namespace
search_servers
Returns matching servers with qualified names, descriptions, verification status, user counts, and deployment info. Use optional filters to narrow by namespace, verified status, or deployment state. Results include pagination metadata. Use this as the first step to discover available MCP servers before connecting or installing them. Search the Smithery registry for MCP servers by name, description, or tags
Example Prompts for Smithery in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Smithery immediately.
"Search for verified GitHub-related MCP servers"
"Show me all tools exposed by the Stripe MCP server"
"Create a connection to the Slack MCP server for my workspace"
Troubleshooting Smithery MCP Server with Pydantic AI
Common issues when connecting Smithery to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiSmithery + Pydantic AI FAQ
Common questions about integrating Smithery MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Smithery with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Smithery to Pydantic AI
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.