Stoplight MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Stoplight as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Stoplight. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Stoplight?"
)
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 Stoplight MCP Server
Integrate the industry-leading API design and documentation capabilities of Stoplight into your conversational AI workflows. Empower your engineering teams to explore workspaces, evaluate OpenAPI schemas, and audit API projects natively from their conversational assistant. Securely map your AI to your Stoplight workspace, enabling the orchestration of complex documentation tasks, project navigation, and architectural reviews naturally without switching contexts or opening complex dashboards.
LlamaIndex agents combine Stoplight tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the 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
- Workspace Exploration — Rapidly inspect top-level organizational containers invoking
list_workspaces, and track operational changes programmatically leveraginglist_workspace_activity. - Project Management — Audit your API documentation repositories cataloging initiatives securely using
list_projects, and retrieve full visibility metadata invokingget_project_details. - Schema & Documentation Discovery — Dive deeply into specific documentation structures retrieving files, endpoints, and models leveraging
list_project_nodes, and parse their raw text safely utilizingget_node_details. - Team & Governance — Map project ownership accurately and enforce governance metrics iteratively assigning roles retrieving authorized contributors naturally via
list_workspace_members.
The Stoplight MCP Server exposes 7 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 Stoplight to LlamaIndex via MCP
Follow these steps to integrate the Stoplight 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 7 tools from Stoplight
Why Use LlamaIndex with the Stoplight MCP Server
LlamaIndex provides unique advantages when paired with Stoplight through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Stoplight tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Stoplight tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Stoplight, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Stoplight tools were called, what data was returned, and how it influenced the final answer
Stoplight + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Stoplight MCP Server delivers measurable value.
Hybrid search: combine Stoplight real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Stoplight 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 Stoplight for fresh data
Analytical workflows: chain Stoplight queries with LlamaIndex's data connectors to build multi-source analytical reports
Stoplight MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Stoplight to LlamaIndex via MCP:
get_node_details
Retrieves details for a specific documentation node
get_project_details
Retrieves details for a specific Stoplight project
list_project_nodes
Lists all documentation nodes (files, endpoints, models) within a project
list_projects
Lists all projects in a specific Stoplight workspace
list_workspace_activity
Lists recent activity logs for a Stoplight workspace
list_workspace_members
Lists all members of a Stoplight workspace
list_workspaces
Lists all accessible Stoplight workspaces
Example Prompts for Stoplight in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Stoplight immediately.
"List my Stoplight projects and show recent workspace activity."
"Retrieve the detailed schema documentation for the processing node in our core billing API project."
"List all active members in the current workspace."
Troubleshooting Stoplight MCP Server with LlamaIndex
Common issues when connecting Stoplight to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpStoplight + LlamaIndex FAQ
Common questions about integrating Stoplight 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 Stoplight 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 Stoplight to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.