Planable MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Planable through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Planable "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Planable?"
)
print(result.data)
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.
Pydantic AI validates every Planable tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Planable MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the Planable MCP Server
Pydantic AI provides unique advantages when paired with Planable through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Planable integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Planable connection logic from agent behavior for testable, maintainable code
Planable + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Planable MCP Server delivers measurable value.
Type-safe data pipelines: query Planable with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Planable tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Planable and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Planable responses and write comprehensive agent tests
Planable MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Planable to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Planable to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiPlanable + Pydantic AI FAQ
Common questions about integrating Planable MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.