Kling AI MCP

Live

OAUTH 2.1

VIDEO GENERATION

AI

Kling AI MCP gives agents authenticated access to video generation: generate video from text or reference images, transfer motion, and track generation tasks.

  • Per-user credentials: each call uses the actual user's token, never a shared bot.
  • Encrypted per-tenant vault: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: pre-call scope check, 90-day SIEM-exportable audit chain.
Kling AI MCP
agent · Acme Q3
Run
Generate a 5-second product video from this still image.
S
klingmcp_kling_generate_video_from_image
142ms
Kling agent
Task started, ID task_7f2a. Video will render in 90-120s at 1080p.
Sources: 1 image, task_7f2a
klingmcp
1 task
18:29
Message Claude...

Tools your creative agent reaches for on Kling AI, scoped per user.

CALL ANY TOOL
Generate and track video content end to end, scoped to each user's own Kling AI access.
klingmcp_kling_generate_video
Generate video
Generate AI video from a text prompt using Kling.
Parameters
Name
Type
Required
Description
prompt
string
Required
Description of the video to generate.
duration
integer
Optional
Video duration in seconds.
aspect_ratio
string
Optional
Video aspect ratio.
klingmcp_kling_generate_video_from_image
Generate video from image
klingmcp_kling_generate_motion
Generate motion transfer
klingmcp_kling_extend_video
Extend video
klingmcp_kling_get_task
Get task status
klingmcp_kling_list_models
List models
Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
import { ScalekitClient } from "@scalekit-sdk/node";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

// Kling AI tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["klingmcp"], toolNames: [
    "klingmcp_kling_generate_video",
    "klingmcp_kling_generate_video_from_image",
    "klingmcp_kling_generate_motion"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Generate a 5-second product video from this still image" }] });
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["klingmcp"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Generate a 5-second product video from this still image" }],
  tools,
});

// Execute the tool call with the user's vaulted klingmcp credential
await sk.tools.executeTool(res.choices[0].message.tool_calls[0], "user_123");
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["klingmcp"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Generate a 5-second product video from this still image" }],
  tools,
});

// Tool call runs with the user's vaulted klingmcp credential
await sk.tools.executeTool(msg.content, "user_123");
import { Agent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["klingmcp"] }, pageSize: 100,
});

const agent = new Agent({
  name: "klingmcp_agent",
  model: "gemini-2.5-pro",
  instruction: "Kling AI tools scoped to this user",
  tools,
});

await agent.run("Generate a 5-second product video from this still image");
Try these prompts
Copy any prompt into your agent. Each maps directly to a Kling AI MCP tool. Click to copy, paste into your agent, done.
Generate video
Copy the prompt
Copied
Generate a 10-second video of a coffee cup steaming on a table.
Copy the prompt
Copied
Generate a video from this product image, 5 seconds, 9:16.
Copy the prompt
Copied
List the available Kling models for video generation.
Motion and extension
Copy the prompt
Copied
Transfer the dance motion from this reference video onto my character image.
Copy the prompt
Copied
Extend video vid_9a21 by 4 more seconds continuing the same scene.
Copy the prompt
Copied
Generate a video with this image as the start frame and that one as the end frame.
Track generation tasks
Copy the prompt
Copied
Check the status of task_7f2a.
Copy the prompt
Copied
Query the status of these 5 video generation tasks.
Copy the prompt
Copied
List all Kling API actions available to this account.
SEE HOW AUTH WORKS
Your users connect once. Their Kling AI MCP credentials stay vaulted, every call is checked, and every action is logged.
1
Authorize
Your user connects
Kling AI MCP
once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access
Who:
user ‘A’
when:
Once per user
access:
Limited to user
2
Store
Their
Kling AI MCP
token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection
vault:
encrypted
scope:
per-user
tokens:
auto-refreshed
3
Resolve
When your agent calls a
Kling AI MCP
tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs
speed:
~40ms
check:
before every call
seen by:
nobody
4
Audit
Every
Kling AI MCP
tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it
history:
90 days
export:
SIEM-ready
logged:
every call
Test other agents
See the same per-user auth pattern across other AI generation connectors.
SUPPORT
Support ticket automation (Google ADK)
Google ADK agent that classifies Zendesk tickets, pulls Notion context, and posts to Slack. End-to-end ticket handoff.
SUPPORT
Support triage agent
Read Zendesk tickets, fetch runbooks from Notion, and route to the right Slack channel with a drafted response.
GTM
Salesforce customer insights agent
Surface Salesforce account activity, NPS signals, and renewal flags into Slack threads for the account team.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
Other connector libraries treat auth as a demo afterthought. Scalekit starts with identity, scope enforcement, and audit. Connectors follow.
01.
Shared tokens break per-user analytics
A shared Kling AI MCP token looks fine in a demo. In production every generation task looks like one service account, and you cannot tell which user triggered it. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
Kling AI MCP today. Ten connectors tomorrow.
“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”
Venu Madhav Kattagoni
Head of Engineering / Von
FAQs
Frequently Asked Questions
Does the agent access Kling AI MCP as the user or through a shared key?
As the user. Scalekit resolves the credential of the person who triggered the agent at request time, so every Kling AI MCP action in your audit trail is attributed to a real user, not a shared service account.
Where is the Kling AI MCP token stored?
In an AES-256 encrypted vault with per-tenant namespacing. Tokens are resolved at request time, never enter LLM context, refresh automatically, and can be revoked from one dashboard.
Can I limit what the agent does in Kling AI MCP?
Yes. Filter by tool name in listScopedTools to expose only what you want. Scalekit also enforces scope checks before every API call.
What happens when a user revokes access?
The credential is invalidated at the next tool call. The call fails closed, other users' connections are unaffected, and the revocation is logged in the audit chain.
How does the agent get the finished video without seeing raw credentials?
The agent polls kling_get_task with the task ID returned at generation time. Scalekit resolves the Kling credential server-side on every call; the agent and the LLM never see a raw API key.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""klingmcp"": {
""url"": ""https://mcp.scalekit.com/klingmcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.klingmcp]
url = ""https://mcp.scalekit.com/klingmcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""klingmcp"": {
""url"": ""https://mcp.scalekit.com/klingmcp"",
""type"": ""http""
}
}
}