Can it delete documents from my search engines?

Yes. The `st.delete_documents` tool removes specific documents by ID from an engine. Each deletion requires explicit document IDs, so nothing is removed accidentally.

Does it support search analytics like CTR?

Yes. Use `st.analytics_top_searches` and `st.analytics_top_clicks` to retrieve real usage data — top queries, click-through rates, and conversion metrics.

What Swiftype plan is required?

Any plan that includes Site Search API access. You need a valid API key from your Swiftype dashboard — the integration uses standard REST endpoints available to all API-enabled accounts.

Swiftype MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Swiftype
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Swiftype through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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({
        "swiftype": {
            "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 Swiftype, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 Swiftype MCP Server

Empower your conversational AI with robust enterprise search capabilities by securely integrating the Swiftype (Elastic) MCP connector. Stop navigating web dashbaords to manage indexing logic; allow your LLM to act as a direct data architect interacting with your core Swiftype endpoints natively. With full support for reading, creating, and deleting JSON documents on the fly, inspecting live search engine queries, and querying direct analytical metrics like top clicks—this connector brings headless search administration straight to your preferred prompt environment.

LangChain's ecosystem of 500+ components combines seamlessly with Swiftype through native MCP adapters. Connect 10 tools via 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

Headless Search & Suggestions — Execute strict queries interrogating custom content engines running st.post_search and provide predictive autocompletes processing st.post_suggest.
CRUD Document Indexing — Pull exact active records from isolated data maps using st.list_documents, inject new payload structures in bulk operating st.create_documents, or vaporize explicit keys commanding st.delete_documents.
Architectural Discovery — Browse registered core scopes applying st.list_engines and parse schema blueprints identifying object hierarchies with st.list_doc_types.
Search Analytics & CTR — Uncover real-world operational user conversion intent evaluating actual volume via st.analytics_top_searches and calculating active hit paths invoking st.analytics_top_clicks.

The Swiftype MCP Server exposes 10 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 Swiftype to LangChain via MCP

Follow these steps to integrate the Swiftype 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 10 tools from Swiftype via MCP

Why Use LangChain with the Swiftype MCP Server

LangChain provides unique advantages when paired with Swiftype through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Swiftype 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 Swiftype queries for multi-turn workflows

Swiftype + LangChain Use Cases

Practical scenarios where LangChain combined with the Swiftype MCP Server delivers measurable value.

RAG with live data: combine Swiftype tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Swiftype, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Swiftype tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Swiftype tool call, measure latency, and optimize your agent's performance

Swiftype MCP Tools for LangChain (10)

These 10 tools become available when you connect Swiftype to LangChain via MCP:

st.analytics_top_clicks

Identify precise active arrays spanning native Hold parsing

st.analytics_top_searches

Inspect deep internal arrays mitigating specific Plan Math

st.create_documents

Enumerate explicitly attached structured rules exporting active Billing

st.delete_documents

json` eliminating cached pages permanently erasing bounds metrics from search. Dispatch an automated validation check routing explicit Gateway history

st.list_doc_types

json` extracting schema blueprints enforcing exact map types correctly. Retrieve explicit Cloud logging tracing explicit Vault limits

st.list_documents

json` dumping all stored metadata physically tracking IDs per document type. Irreversibly vaporize explicit validations extracting rich Churn flags

st.list_domains

json` verifying automated crawler limits mapped inside explicit index scopes. Identify precise active arrays spanning native Gateway auth

st.list_engines

json` extracting all active isolated Elastic indices bound per tenant. Identify bounded CRM records inside the Headless Swiftype Platform

st.post_search

json` firing raw queries into the specific Engine returning faceted JSON hierarchies. Perform structural extraction of properties driving active Account logic

st.post_suggest

json` bounding predictive keys and spelling tolerant matches decoupled from main indexing. Provision a highly-available JSON Payload generating hard Customer bindings

Example Prompts for Swiftype in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Swiftype immediately.

"List all my available Swiftype search engines, then run a search for 'documentation' on the most relevant one and show me the top 3 analytics clicks it generated last week."

"List all active engines in our Swiftype account."

"Run a test suggestion for 'passw' in the internal wiki engine."

Troubleshooting Swiftype MCP Server with LangChain

Common issues when connecting Swiftype to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Swiftype + LangChain FAQ

Common questions about integrating Swiftype MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use 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?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.