SwaggerHub MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SwaggerHub as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to SwaggerHub. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in SwaggerHub?"
)
print(response)
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 SwaggerHub MCP Server
Integrate SwaggerHub, the enterprise platform for API design and documentation, directly into your conversational workflows with the intelligent MCP connector. Transform your LLM into an active technical architect, empowering it to securely index, validate, and retrieve full OpenAPI specifications directly from your organizational directories. Eradicate context-switching by verifying CI/CD integration pipelines, scanning centralized API definitions, and pulling structural component domains intuitively without having to hunt through graphical interfaces.
LlamaIndex agents combine SwaggerHub tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- API Cataloging & Specs — Query an entire organizational API roster using
list_apisand pull exact OpenAPI JSON configurations cleanly callingget_api_version_spec. - Component Reusability Insights — Investigate generic shared definitions executing
list_domainsand fetch core parameters seamlessly viaget_domain_details. - Project & Lifecycle Control — Map team infrastructures inspecting groupings natively with
list_projectsand verify operational logic by callingget_project_details. - Ecosystem Verification — Audit backend dependencies natively invoking
list_api_integrationsto test GitHub, AWS, and GitLab sync parameters tied to your specs.
The SwaggerHub MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 SwaggerHub to LlamaIndex via MCP
Follow these steps to integrate the SwaggerHub MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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 SwaggerHub
Why Use LlamaIndex with the SwaggerHub MCP Server
LlamaIndex provides unique advantages when paired with SwaggerHub through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SwaggerHub tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SwaggerHub tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SwaggerHub, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SwaggerHub tools were called, what data was returned, and how it influenced the final answer
SwaggerHub + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SwaggerHub MCP Server delivers measurable value.
Hybrid search: combine SwaggerHub real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SwaggerHub to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying SwaggerHub for fresh data
Analytical workflows: chain SwaggerHub queries with LlamaIndex's data connectors to build multi-source analytical reports
SwaggerHub MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect SwaggerHub to LlamaIndex via MCP:
get_api_details
Retrieves metadata for a SwaggerHub API definition
get_api_version_spec
Retrieves a specific version of a SwaggerHub API definition (OpenAPI spec)
get_domain_details
Retrieves metadata for a SwaggerHub domain
get_project_details
Retrieves details of a SwaggerHub project
list_api_integrations
Lists all CI/CD integrations configured for a SwaggerHub API
list_api_templates
Lists all available API templates on SwaggerHub
list_apis
List all API definitions owned by a SwaggerHub user or organization
list_domains
Lists all shared domains (reusable components) owned by a user or org
list_projects
Lists all projects in a SwaggerHub organization
search_apis
Search all public APIs on SwaggerHub by keyword
Example Prompts for SwaggerHub in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with SwaggerHub immediately.
"Search for public API specifications related to 'payment gateway' on SwaggerHub."
"List all active projects in our SwaggerHub organization."
"Ensure that the 'Acme-Billing' API has AWS API Gateway integration synced currently."
Troubleshooting SwaggerHub MCP Server with LlamaIndex
Common issues when connecting SwaggerHub to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSwaggerHub + LlamaIndex FAQ
Common questions about integrating SwaggerHub MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect SwaggerHub 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 SwaggerHub to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.