watsonx Discovery MCP Server for Pydantic AI 6 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect watsonx Discovery 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 watsonx Discovery "
"(6 tools)."
),
)
result = await agent.run(
"What tools are available in watsonx Discovery?"
)
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 watsonx Discovery MCP Server
Connect your IBM watsonx Discovery account to any AI agent and harness the power of cognitive search and NLP-driven text analytics through natural conversation.
Pydantic AI validates every watsonx Discovery tool response against typed schemas, catching data inconsistencies at build time. Connect 6 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.
What you can do
- Cognitive Search — Perform natural language or Discovery Query Language (DQL) queries against your data collections for high-quality semantic search
- Data Discovery — Browse and list all data collections within your project to retrieve collection and document IDs
- Document Analysis — Retrieve comprehensive metadata, ingestion status, and technical details for specific indexed documents
- NLP Enrichments — List and monitor available enrichments (NLP models) like Sentiment, Entities, and Keywords being applied to your data
- Component Health — Verify project-level configurations, ingestion notices, and health settings for all project components
- Semantic Insights — Surface relevant information and hidden patterns from massive unstructured datasets through simple chat commands
The watsonx Discovery MCP Server exposes 6 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 watsonx Discovery to Pydantic AI via MCP
Follow these steps to integrate the watsonx Discovery 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 6 tools from watsonx Discovery with type-safe schemas
Why Use Pydantic AI with the watsonx Discovery MCP Server
Pydantic AI provides unique advantages when paired with watsonx Discovery 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 watsonx Discovery integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your watsonx Discovery connection logic from agent behavior for testable, maintainable code
watsonx Discovery + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the watsonx Discovery MCP Server delivers measurable value.
Type-safe data pipelines: query watsonx Discovery with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple watsonx Discovery tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query watsonx Discovery and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock watsonx Discovery responses and write comprehensive agent tests
watsonx Discovery MCP Tools for Pydantic AI (6)
These 6 tools become available when you connect watsonx Discovery to Pydantic AI via MCP:
get_component_settings
Retrieves the configuration and health settings for project components
get_document_details
Retrieves metadata and status for a specific indexed document
list_available_enrichments
g., Sentiment, Entities) are being applied to documents. Lists all enrichments (NLP models) configured for the project
list_collection_documents
Lists all documents indexed within a specific collection
list_discovery_collections
Lists all data collections within the current watsonx Discovery project
query_discovery_content
Provide a collection ID and the query text. Performs a natural language or DQL query against a discovery collection
Example Prompts for watsonx Discovery in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with watsonx Discovery immediately.
"List all my Discovery collections."
"Search the 'Legal Documents' collection for 'contract termination clauses'."
"What enrichments are currently active in my project?"
Troubleshooting watsonx Discovery MCP Server with Pydantic AI
Common issues when connecting watsonx Discovery to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiwatsonx Discovery + Pydantic AI FAQ
Common questions about integrating watsonx Discovery 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 watsonx Discovery 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 watsonx Discovery to Pydantic AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.