watsonx Discovery MCP Server for LangChain 6 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect watsonx Discovery through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"watsonx-discovery": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using watsonx Discovery, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with watsonx Discovery through native MCP adapters. Connect 6 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the watsonx Discovery MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 6 tools from watsonx Discovery via MCP
Why Use LangChain with the watsonx Discovery MCP Server
LangChain provides unique advantages when paired with watsonx Discovery through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine watsonx Discovery MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across watsonx Discovery queries for multi-turn workflows
watsonx Discovery + LangChain Use Cases
Practical scenarios where LangChain combined with the watsonx Discovery MCP Server delivers measurable value.
RAG with live data: combine watsonx Discovery tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query watsonx Discovery, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain watsonx Discovery tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every watsonx Discovery tool call, measure latency, and optimize your agent's performance
watsonx Discovery MCP Tools for LangChain (6)
These 6 tools become available when you connect watsonx Discovery to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting watsonx Discovery to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adapterswatsonx Discovery + LangChain FAQ
Common questions about integrating watsonx Discovery MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.