Can my AI automatically review and bulk-approve pending posts?

Yes. Instruct the agent to run `list_posts` filtered by 'pending_approval'. Tell your LLM to ensure they lack profanities or adhere strictly to brand tone. If they pass, you can command the AI to loop over their unique IDs mapping the `approve_post` mutation simultaneously, scheduling the calendar effortlessly.

Can I leave comments for the rest of my design team through this?

Absolutely. Using the `reject_post` functionality or directly targeting `add_comment` on a specific Post ID, the agent injects your exact critique ("Make the background logo larger") back into the underlying Planable portal in real-time. Your team sees your notes just as if you were logged onto their board.

Will the API tell me exactly which social channel the post is meant for?

Yes! Running `list_pages` maps the organizational identifiers. The API correlates structural pages inside your workspace to logical federated networks (Facebook, LinkedIn, TikTok), telling your agent explicitly what bounds to consider regarding content formatting constraints.

Planable MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Planable
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 Planable 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({
        "planable": {
            "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 Planable, 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 Planable MCP Server

Connect your Planable workspaces directly to your AI agent to radically streamline your social media collaboration loops. You can review scheduled posts, approve mockups, respond to team comments, and oversee the content pipeline directly from your primary interface.

LangChain's ecosystem of 500+ components combines seamlessly with Planable 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

Workspace & Pages — View active workspaces, team members, and all connected social accounts isolated in their respective boundaries.
Content Pipeline — Retrieve post drafts, schedule future publications, and query statuses (draft, pending_approval, scheduled, published).
Approval Workflow — Radically speed up content sign-off. Instruct your AI to transition posts from pending directly to approved, or formally reject them with custom revision notes.
Collaboration — Add, fetch, and monitor chronological threaded comments on any isolated post.

The Planable 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 Planable to LangChain via MCP

Follow these steps to integrate the Planable 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 Planable via MCP

Why Use LangChain with the Planable MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Planable 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 Planable queries for multi-turn workflows

Planable + LangChain Use Cases

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

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

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

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

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

Planable MCP Tools for LangChain (10)

These 10 tools become available when you connect Planable to LangChain via MCP:

add_comment

Add a comment to a Planable post for team collaboration

approve_post

Approve a Planable post in the approval workflow. Moves it to scheduled status

create_post

Create a Planable post. Instructions: Pass workspace_id, page_id, content text, and scheduled_at (ISO 8601). Post enters approval workflow

get_post

Get a Planable post by ID. Returns full content, media, schedule, approval history, and comments

list_comments

List comments on a Planable post. Returns comment IDs, authors, and text

list_pages

List social pages (connected accounts) in a Planable workspace. Returns page IDs, platform types, and display names

list_posts

List posts in a Planable workspace by status. Returns post IDs, content previews, scheduled times, and approval status. Instructions: status = draft|pending_approval|approved|scheduled|published

list_workspace_members

List members of a Planable workspace. Returns member IDs, names, emails, and roles

list_workspaces

List Planable workspaces. Returns workspace IDs, names, and member counts. Planable is a social collaboration platform for content planning and approval

reject_post

Reject a Planable post with feedback. Returns it to draft for revisions

Example Prompts for Planable in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Planable immediately.

"List all posts in the 'Acme Marketing' workspace that are currently awaiting approval."

"Draft a new Twitter post in our workspace announcing our new AI feature."

"Reject post `98341x` and tell the team to rewrite the hook, it's too salesy."

Troubleshooting Planable MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Planable + LangChain FAQ

Common questions about integrating Planable 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.