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tokportal

tokportal-mcp

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Python
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langchain-tokportal

PyPI CI License: MIT

LangChain / LangGraph integration for TokPortal.

TokPortal is the managed social infrastructure API: real TikTok and Instagram 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_...

Related MCP server: social0-mcp

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

License

MIT

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