Mattermark MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mattermark through 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 Mattermark "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Mattermark?"
)
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 Mattermark MCP Server
Connect your Mattermark account to any AI agent and access deep insights into the startup ecosystem through natural conversation.
Pydantic AI validates every Mattermark 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.
What you can do
- Company Research — Search for startups, fetch detailed metadata, and monitor funding history
- Investor Intelligence — List venture firms and inspect their portfolios and profiles
- Funding Rounds — Query specific investment rounds and their details
- Competitive Analysis — Find similar companies and track employee growth trends
The Mattermark MCP Server exposes 10 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 Mattermark to Pydantic AI via MCP
Follow these steps to integrate the Mattermark 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 10 tools from Mattermark with type-safe schemas
Why Use Pydantic AI with the Mattermark MCP Server
Pydantic AI provides unique advantages when paired with Mattermark 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 Mattermark integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Mattermark connection logic from agent behavior for testable, maintainable code
Mattermark + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Mattermark MCP Server delivers measurable value.
Type-safe data pipelines: query Mattermark with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Mattermark tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Mattermark and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Mattermark responses and write comprehensive agent tests
Mattermark MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Mattermark to Pydantic AI via MCP:
get_company_details
Get details for a specific company
get_company_employees
Get employee data for a company
get_company_funding_rounds
Get funding history for a company
get_company_news
Get news for a specific company
get_funding_round_details
Get details for a funding round
get_investor_details
Get details for an investor
list_investors
List venture capital investors
list_similar_companies
Find similar companies
search_companies
Search for companies
search_funding_rounds
Search for funding rounds
Example Prompts for Mattermark in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Mattermark immediately.
"Search for companies in the 'Fintech' sector in New York."
"Get funding history for company ID 123."
"List similar companies to 'Stripe'."
Troubleshooting Mattermark MCP Server with Pydantic AI
Common issues when connecting Mattermark to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiMattermark + Pydantic AI FAQ
Common questions about integrating Mattermark 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 Mattermark 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 Mattermark to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.