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 LangChain 10 tools — connect in under 2 minutes

Name: Apiary
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Apiary through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "apiary": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Apiary, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Apiary through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Apiary MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Apiary via MCP

Why Use LangChain with the Apiary MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Apiary MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Apiary queries for multi-turn workflows

Apiary + LangChain Use Cases

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

RAG with live data: combine Apiary tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Apiary, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Apiary tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Apiary tool call, measure latency, and optimize your agent's performance

Apiary MCP Tools for LangChain (10)

These 10 tools become available when you connect Apiary to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Apiary + LangChain FAQ

Common questions about integrating Apiary MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.