Can I import a complete OpenAPI spec into an AI chat session?

Yes. Instruct the agent to run the `export_openapi` tool. It downloads the JSON metadata and safely passes it into your context. Given its size, the agent can use this massive reference guide to rapidly write tests, endpoints, or data models perfectly mimicking your production systems.

How can I check the payload requirements for a single specific route?

If you know the general area of the project, use `list_endpoints` to verify the ID. Then query `get_endpoint`. It brings back the specific HTTP verbs, query arguments, path definitions, and expected JSON body — letting your agent craft exact functional requests to external systems.

Will my Apidog tokens expire?

When generating your Personal Access Token in Apidog, you dictate the validity duration (e.g. 30 days, 1 year or never expire). If you choose a finite span, you will need to come back, generate a new token and insert it once more to reconnect the MCP server successfully.

Apidog MCP Server for LlamaIndex 5 tools — connect in under 2 minutes

Name: Apidog
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 5 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Apidog as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 Apidog. "
            "You have 5 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Apidog?"
    )
    print(response)

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 Apidog MCP Server

Connect your Apidog account to your AI agent and seamlessly access your API specifications, data models, and documentation through natural conversation.

LlamaIndex agents combine Apidog tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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

Discover Projects & Endpoints — Browse your active projects and list all HTTP routes without opening the Apidog client
Inspect Endpoint Schemas — Fetch the complete anatomy of any route, including its HTTP method, dynamic path params, headers, and request/response body schemas
Understand Data Models — Query active reusable schemas (DTOs, entities) defined throughout your API
Export OpenAPI Specs — Extract the complete OpenAPI 3.0 JSON specification from your team’s project to give your AI maximum context for testing or code generation

The Apidog MCP Server exposes 5 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 Apidog to LlamaIndex via MCP

Follow these steps to integrate the Apidog 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 5 tools from Apidog

Why Use LlamaIndex with the Apidog MCP Server

LlamaIndex provides unique advantages when paired with Apidog through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Apidog tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Apidog tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Apidog, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Apidog tools were called, what data was returned, and how it influenced the final answer

Apidog + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Apidog MCP Server delivers measurable value.

Hybrid search: combine Apidog real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Apidog 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 Apidog for fresh data

Analytical workflows: chain Apidog queries with LlamaIndex's data connectors to build multi-source analytical reports

Apidog MCP Tools for LlamaIndex (5)

These 5 tools become available when you connect Apidog to LlamaIndex via MCP:

export_openapi

Export the full OpenAPI 3.0 specification of an Apidog project as JSON

get_endpoint

Fetch the complete schema of a single API endpoint

list_endpoints

List all API endpoints defined within a specific Apidog project

list_projects

List all API projects in the connected Apidog organization

list_schemas

List all data model schemas (DTOs, entities) defined in an Apidog project

Example Prompts for Apidog in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Apidog immediately.

"List all active projects in our Apidog organization."

"Write a TypeScript interface for the response schema of the /users endpoint in the E-commerce project."

"Export the full OpenAPI JSON for the E-commerce project so we can generate unit tests."

Troubleshooting Apidog MCP Server with LlamaIndex

Common issues when connecting Apidog to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Apidog + LlamaIndex FAQ

Common questions about integrating Apidog MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Apidog tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.