Mode (Collaborative Data Platform) MCP Server for Pydantic AI 7 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in Mode (Collaborative Data Platform)?"
)
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 Mode (Collaborative Data Platform) MCP Server
Connect your Mode Analytics account to any AI agent and take full control of your enterprise business intelligence, collaborative SQL reporting, and data source management through natural conversation.
Pydantic AI validates every Mode (Collaborative Data Platform) tool response against typed schemas, catching data inconsistencies at build time. Connect 7 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
- Report Orchestration — List all managed data reports and retrieve detailed analytical parameters, including chart configurations and query states directly from your agent
- Space Navigation — Explore organizational 'Spaces' (Personal, Shared) to retrieve the exact report tokens needed to query scoped analytical boundaries natively
- Global Analytics Search — Execute workspace-wide searches to identify specific reports and datasets matching literal metadata descriptions or keywords
- Data Source Audit — Enumerate explicit database and warehouse connector sources bound to your Mode account to understand which schemas are available for querying
- Member Tracking — List statically tracked analytical users within your workspace to verify report ownership and collaborative boundaries securely
- Metadata Inspection — Deep-dive into specific Report or Space tokens to retrieve precise configuration details and chart definitions instantly
The Mode (Collaborative Data Platform) MCP Server exposes 7 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 Mode (Collaborative Data Platform) to Pydantic AI via MCP
Follow these steps to integrate the Mode (Collaborative Data Platform) 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 7 tools from Mode (Collaborative Data Platform) with type-safe schemas
Why Use Pydantic AI with the Mode (Collaborative Data Platform) MCP Server
Pydantic AI provides unique advantages when paired with Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Mode (Collaborative Data Platform) connection logic from agent behavior for testable, maintainable code
Mode (Collaborative Data Platform) + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Mode (Collaborative Data Platform) MCP Server delivers measurable value.
Type-safe data pipelines: query Mode (Collaborative Data Platform) with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Mode (Collaborative Data Platform) tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Mode (Collaborative Data Platform) and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Mode (Collaborative Data Platform) responses and write comprehensive agent tests
Mode (Collaborative Data Platform) MCP Tools for Pydantic AI (7)
These 7 tools become available when you connect Mode (Collaborative Data Platform) to Pydantic AI via MCP:
get_report
Get specific analytical parameters mapping a single tracked Mode report token
get_space
Get parameters mapping an explicitly targeted collection Space
list_data_sources
List explicit Database/Warehouse connector sources bound to Mode
list_members
List statically tracked analytical users joined within the workspace
list_reports
List static data reports generated by the Mode workspace
list_spaces
List accessible Spaces isolating datasets across the Mode workspace
search_reports
Search all reports evaluating queries natively against Mode API
Example Prompts for Mode (Collaborative Data Platform) in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Mode (Collaborative Data Platform) immediately.
"List all reports in my 'Shared' space"
"Search for any reports related to 'Marketing ROI' in the workspace"
"Show me the data sources currently connected to our Mode account"
Troubleshooting Mode (Collaborative Data Platform) MCP Server with Pydantic AI
Common issues when connecting Mode (Collaborative Data Platform) to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiMode (Collaborative Data Platform) + Pydantic AI FAQ
Common questions about integrating Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) to Pydantic AI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.