How to Use the Apidog MCP in LlamaIndex

Ground your LlamaIndex knowledge base in real-time API specifications from Apidog.

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Connect Apidog MCP to LlamaIndex

Create your Vinkius account to connect Apidog to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Apidog with LlamaIndex

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Index Apidog specs into LlamaIndex

Convert your API documentation into searchable vector embeddings. `list_endpoints` feeds the data directly into your knowledge base for retrieval during queries. Your RAG application references these exact schemas rather than hallucinated API structures. It builds a map of your entire project architecture automatically.

Query your workspace via LlamaIndex

Ask questions about your API organization using natural language. The agent calls `list_projects` to locate the right context before answering your technical inquiries. It performs semantic search across your DTOs and entities. The results are grounded in the actual code definitions found in your Apidog project.

Filter tools for precise data access

Use the allowed_tools filter to restrict what your agent can touch within Apidog. You maintain full control over which parts of your API surface the agent explores. Include resources to pull documentation directly into the context window. This makes the retrieved data immediately available for your generative tasks.

Setup guide

Set up Apidog MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Apidog MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Apidog tools.",
)
response = await agent.run("List recent Apidog data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Apidog. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Apidog MCP in LlamaIndex

Use the BasicMCPClient to connect to the server and pass the tool spec to your agent. This turns your API definitions into queryable knowledge for the model.

It indexes the output of the tool calls into your vector store. You can search your entire API library using natural language queries.

By grounding the agent in actual schema data, you force it to use real definitions. It reads the live project state instead of guessing how your endpoints work.

Security is handled via a private token that limits the server to your specific organization. No external entities see your API specifications or project structure.

Yes, you can call the tool list asynchronously to keep your application responsive. It handles large project schemas without blocking your main execution thread.

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