tokportal-mcp
Enables AI agents to manage real TikTok, Instagram, and YouTube accounts via a REST API and MCP server, allowing for account creation, content publishing, warming, and analytics without OAuth or daily post limits.
README
langchain-tokportal
LangChain / LangGraph integration for TokPortal.
TokPortal is the managed social infrastructure API: real TikTok, Instagram and YouTube accounts created, warmed and operated by human account managers in 16+ countries — exposed as a REST API and an MCP server. No OAuth per account, no 25-posts/day cap, no app review.
This package gives your agents two ways to drive TokPortal:
| Mode | What you get | When to use |
|---|---|---|
Native tools (toolkit.get_tools()) |
6 hand-written BaseTools wrapping the tokportal Python SDK: credit balance, create bundle, configure video, publish bundle, get bundle, list accounts |
Simple sync agents, no MCP dependency at runtime |
MCP tools (await toolkit.aget_tools("mcp")) |
Every tool of the remote TokPortal MCP server (90+ operations: uploads, warming, analytics, webhooks, comments, bans…) loaded through langchain-mcp-adapters |
Full API surface, async agents |
Installation
pip install langchain-tokportal
# optional, for the LangGraph example below
pip install langchain langchain-openai
Get an API key at https://app.tokportal.com/developer and export it:
export TOKPORTAL_API_KEY=sk_...
Quick start (native tools)
from langchain_tokportal import TokPortalToolkit
toolkit = TokPortalToolkit.from_api_key() # reads TOKPORTAL_API_KEY
tools = toolkit.get_tools()
for tool in tools:
print(tool.name)
# tokportal_get_credit_balance
# tokportal_create_bundle
# tokportal_configure_bundle_video
# tokportal_publish_bundle
# tokportal_get_bundle
# tokportal_list_accounts
print(tools[0].invoke({})) # -> {"data": {"balance": ...}}
Every tool returns a JSON string (the raw TokPortal data envelope, or an
{"error": {...}} object with status, code, request_id and retryable
so the model can self-correct).
LangGraph agent example (langchain.agents.create_agent, LangChain 1.x)
import asyncio
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent
from langchain_tokportal import TokPortalToolkit
SYSTEM = (
"You operate TokPortal, a managed social infrastructure API. "
"Always check the credit balance before creating bundles. "
"A bundle must be published for a human account manager to start working on it."
)
async def main() -> None:
toolkit = TokPortalToolkit.from_api_key()
# "native" = 6 SDK tools, "mcp" = full remote MCP tool set, "all" = both
tools = await toolkit.aget_tools("mcp")
agent = create_agent(ChatOpenAI(model="gpt-4.1"), tools, system_prompt=SYSTEM)
result = await agent.ainvoke(
{
"messages": [
(
"user",
"Order one new TikTok account in the US with 3 video slots and "
"advanced warming on the terms 'home workout', 'protein snacks', "
"'gym motivation'. Publish it and tell me the bundle id.",
)
]
}
)
print(result["messages"][-1].content)
asyncio.run(main())
Sync-only code paths can call toolkit.get_mcp_tools() (wraps asyncio.run).
Using the MCP connection directly
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_tokportal import mcp_connection
client = MultiServerMCPClient({"tokportal": mcp_connection("sk_...")})
tools = await client.get_tools()
mcp_connection() returns a Streamable-HTTP connection dict pointing at
https://app.tokportal.com/api/ext/mcp with Authorization: Bearer <api_key>.
Tool reference (native)
| Tool | REST operation |
|---|---|
tokportal_get_credit_balance |
GET /credits/balance |
tokportal_create_bundle |
POST /bundles |
tokportal_configure_bundle_video |
PUT /bundles/{id}/videos/{position} |
tokportal_publish_bundle |
POST /bundles/{id}/publish |
tokportal_get_bundle |
GET /bundles/{id} |
tokportal_list_accounts |
GET /accounts |
Typical flow: get_credit_balance → create_bundle (draft) → configure_bundle_video × N → publish_bundle → poll get_bundle or subscribe to webhooks.
Resources
- API docs: https://developers.tokportal.com
- OpenAPI: https://developers.tokportal.com/openapi.json
- MCP server: https://developers.tokportal.com/mcp (remote
https://app.tokportal.com/api/ext/mcp, stdionpx -y tokportal-mcp) - Python SDK: https://github.com/tokportal/tokportal-python
- Support: team@tokportal.com
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.