Can the agent fetch the exact mock server endpoints for one of my APIs?

Yes. You can use the `get_doc_url` capability to retrieve both the public facing documentation url and the private mock server endpoint string. Your agent can immediately use this url to simulate requests internally using other modules or tests.

Am I able to update the Blueprint markup completely through the agent?

Absolutely. Instruct your AI agent to fetch the blueprint with `get_api`. Once read into context, prompt the agent to make your desired changes (e.g. 'Add a new POST route for user creation'). Then, instruct it to use `publish_blueprint` to upload the newly revised markdown.

How does the team feature list function mapping work?

Apiary enables collaboration. Utilizing `list_teams`, the agent finds the slugs of your organizations. Then, using `list_team_apis`, it fetches all collaborative APIs available for that precise team. This hierarchy avoids hallucinating random endpoint repositories.

Apiary MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Apiary
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Apiary 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 Apiary. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Apiary?"
    )
    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 Apiary MCP Server

Connect your Apiary workspace to your AI agent and take full control of your API design, specification, and validation processes through natural conversation.

LlamaIndex agents combine Apiary 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

Read API Blueprints — Fetch raw Markdown-based API Blueprints or Swagger files defining endpoints, methods, and parameters without leaving your editor
Publish Documentation — Instantly update your Apiary projects with new markdown code. Mock servers and public documentation sync automatically
Run Dredd Tests — Validate your backend implementation against the blueprint specifications using integrated tests
Team Management — Query team API projects, list team members, and manage your engineering ecosystem intuitively

The Apiary 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 Apiary to LlamaIndex via MCP

Follow these steps to integrate the Apiary 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 Apiary

Why Use LlamaIndex with the Apiary MCP Server

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

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

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

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

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

Apiary + LlamaIndex Use Cases

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

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

Data enrichment: query Apiary 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 Apiary for fresh data

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

Apiary MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Apiary to LlamaIndex via MCP:

get_api

Get the full API Blueprint source code of an Apiary API project

get_doc_url

Get the documentation and mock server URLs for an Apiary API project

get_me

Get the authenticated Apiary user profile

get_team

Get details of a specific Apiary team

list_apis

Apiary is an API design-first platform. List all API projects on Apiary

list_team_apis

List all API projects belonging to a specific Apiary team

list_team_members

List all members of an Apiary team

list_teams

List all teams the authenticated user belongs to on Apiary

publish_blueprint

Use with valid Markdown blueprint/Swagger. Publish (update) the API Blueprint of an Apiary API project

run_tests

Get or run Dredd-style API tests against an Apiary project

Example Prompts for Apiary in LlamaIndex

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

"Get the API Blueprint code for my project named 'payments-gateway'."

"List all team members of the 'frontend-team' in Apiary."

"Update my 'users-api' with this new blueprint code..."

Troubleshooting Apiary MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Apiary + LlamaIndex FAQ

Common questions about integrating Apiary 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 Apiary tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.