Planable MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Planable as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Planable. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Planable?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Planable 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
- 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 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 Planable to LlamaIndex via MCP
Follow these steps to integrate the Planable 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 Planable
Why Use LlamaIndex with the Planable MCP Server
LlamaIndex provides unique advantages when paired with Planable through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Planable tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Planable tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Planable, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Planable tools were called, what data was returned, and how it influenced the final answer
Planable + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Planable MCP Server delivers measurable value.
Hybrid search: combine Planable real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Planable 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 Planable for fresh data
Analytical workflows: chain Planable queries with LlamaIndex's data connectors to build multi-source analytical reports
Planable MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Planable to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Planable to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPlanable + LlamaIndex FAQ
Common questions about integrating Planable MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Planable with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Planable to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.