LaunchDarkly MCP

Live

OAUTH 2.0

DEVELOPER TOOLS

Developer Tools

Connect to LaunchDarkly's hosted MCP server to manage feature flags, experiments, and release controls directly from your AI workflows.

  • Acts as the user: Every tool call runs as the authorizing user. Access and audit trail stay intact.
  • Credentials stay vaulted: AES-256 encrypted, resolved at request time, never stored in LLM context.
  • Scoped before every call: Per-user permissions enforced automatically. 90-day audit trail included.
LaunchDarkly MCP
agent · Acme Q3
Run
Apply Approval Request in LaunchDarkly MCP
S
launchdarklymcp_apply_approval_request
85ms
LaunchDarkly MCP agent
Apply an approved change request, executing the flag changes it contains..
Sources: LaunchDarkly MCP
launchdarklymcpmcp
1 tool call
18:29
Message Claude...

LaunchDarkly MCP tools for AI agents

CALL ANY TOOL
12 tools covering archive, check, apply.
launchdarklymcp_apply_approval_request
Apply an approved change request, executing the flag changes
Apply an approved change request, executing the flag changes it contains.
Parameters
Name
Type
Required
Description
environmentKey
string
Required
The environment key where the approval request applies.
featureFlagKey
string
Required
The feature flag key associated with the approval request.
id
string
Required
The ID of the approval request to apply.
projectKey
string
Required
The project key where the approval request resides.
comment
string
Optional
Optional comment to include when applying the approval request.
flagKey
string
Optional
Alias for featureFlagKey.
launchdarklymcp_archive_flag
Archive a feature flag, marking it as retired. archived flag
launchdarklymcp_check_removal_readiness
Check whether a feature flag is ready to be permanently remo
launchdarklymcp_clone_ai_config_variation
Clone an existing ai config variation to create a new variat
launchdarklymcp_copy_flag_config
Copy a flag's targeting configuration from one environment t
launchdarklymcp_create_agent_graph
Create a new agent graph definition for multi-step ai workfl
launchdarklymcp_create_ai_config
Create a new ai config in a project. an ai config manages ai
launchdarklymcp_create_ai_config_variation
Create a new variation for an ai config with specific model,
launchdarklymcp_create_ai_tool
Create a new ai tool definition for use in ai config variati
launchdarklymcp_create_approval_request
Create an approval request for a proposed flag change. the c
launchdarklymcp_create_flag
Create a new feature flag in a project. the flag is created
launchdarklymcp_create_playground
Create a new ai playground for testing prompts and ai config
Build your Agent
Same auth pattern across every framework.
Python · LlamaIndex
from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="launchdarklymcp")

mcp = MultiServerMCPClient({
"launchdarklymcp": {
"url": "https://mcp.scalekit.com/launchdarklymcp",
"headers": {"Authorization": "Bearer " + token}
}
})
tools = await mcp.get_tools()
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "launchdarklymcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/launchdarklymcp
// Pass: Authorization: Bearer + token
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "launchdarklymcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/launchdarklymcp
// Pass: Authorization: Bearer + token
from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="launchdarklymcp")
# Connect to MCP at https://mcp.scalekit.com/launchdarklymcp
# Pass: Authorization: Bearer + token
Try these prompts
Paste any prompt into your agent to get started.
Get started
Copy the prompt
Copied
Report a missing capability, bug, parameter gap, or unclear error encountered while using LaunchDarkly MCP tools?
Copy the prompt
Copied
Update an existing experiment’s configuration including name, description, metrics, treatments, and randomization unit?
Advanced
Copy the prompt
Copied
Update an existing agent graph definition, modifying its nodes, edges, or metadata?
Copy the prompt
Copied
Update the targeting rules for a feature flag in a specific environment?
SEE HOW AUTH WORKS
User authorises once. Every agent call after uses their token with scope enforcement.
1
Authorize
Your user connects
LaunchDarkly 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
LaunchDarkly 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
LaunchDarkly 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
LaunchDarkly 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 connectors.
ENGINEERING
DevOps assistant agent
Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.
ENGINEERING
Auto-release notes agent
Group merged GitHub PRs by feature, fix, or chore and publish release notes per tag. No manual changelog grooming.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
Other connector libraries treat auth as a demo afterthought.
01.
Shared tokens break per-user analytics
A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
One connector today. Ten 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 LaunchDarkly as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the LaunchDarkly OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in LaunchDarkly?
Yes. Pass a tool name filter to listScopedTools so the DevOps agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches LaunchDarkly.

What happens when a user revokes LaunchDarkly access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Can the agent flip feature flags in production?
Only if the authorizing user's LaunchDarkly role permits it, and only after approval workflows the project enforces. Filter out flag-write tools to run a read-only release analyst agent.

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"": {
""launchdarklymcp"": {
""url"": ""https://mcp.scalekit.com/launchdarklymcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.launchdarklymcp]
url = ""https://mcp.scalekit.com/launchdarklymcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""launchdarklymcp"": {
""url"": ""https://mcp.scalekit.com/launchdarklymcp"",
""type"": ""http""
}
}
}