Humanloop (LLM Prompt Management API) Connector for AI agents.
12 live capabilities
Manage and deploy LLM prompts across production environments.
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Why people use Humanloop (LLM Prompt Management API)
Humanloop LLMOps: Stop manually syncing prompts across environments
With this Connector, you just talk to your agent. You can tell it to push a new version or check the status of your staging environment. It turns a multi-tab chore into a single command.
What Vinkius changes
You get a unified, conversational interface for managing your entire LLMOps pipeline.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
The Production Hotfix
An engineer needs to swap a failing prompt.
- Real-world use case 02
The Audit
A PM wants to see what's live.
- Real-world use case 03
The Rapid Iteration
An AI dev wants to test 5 versions.
Complete set · 12capabilities
The complete Humanloop (LLM Prompt Management API) capability set.
These are the exact actions your AI can choose when you ask it to work with Humanloop (LLM Prompt Management API).
01—04
4 capabilities in this set.
Part of 12 available through Humanloop (LLM Prompt Management API).
- 01 Capability
Delete prompt version
Permanently remove an old or incorrect version of a prompt. This helps keep your library clean.
- 02 Capability
Deploy prompt
Set a specific prompt version as the active one for staging or production. Use this to push updates live.
- 03 Capability
Get prompt
Fetch the full configuration and details for a specific prompt ID. This lets you inspect the current settings.
- 04 Capability
List prompt environments
See which prompt versions are currently live in every environment. Use this to check production status.
05—08
4 capabilities in this set.
Part of 12 available through Humanloop (LLM Prompt Management API).
- 05 Capability
List prompts
Get a complete list of every prompt in your organization. Use this to see your whole library.
- 06 Capability
Log to prompt
Save a model's generation to your logs for later evaluation. This helps you track performance over time.
- 07 Capability
Remove deployment
Take a specific prompt version offline from an environment. Use this to roll back changes quickly.
- 08 Capability
Update monitoring
Turn monitoring evaluators on or off for specific prompts. This gives you control over your logs.
09—12
4 capabilities in this set.
Part of 12 available through Humanloop (LLM Prompt Management API).
- 09 Capability
Update prompt version
Change the name or description of an existing prompt version. This keeps your metadata accurate.
- 10 Capability
Call prompt stream
Execute a prompt and get the response back in a live stream. This is useful for seeing model output as it happens.
- 11 Capability
List prompt versions
View the full history of versions for a single prompt. This is great for comparing iterations.
- 12 Capability
Upsert prompt
Create a new prompt or update an existing configuration. Use this to manage your prompt library.
Set up in minutes
One URL. Then ask Humanloop (LLM Prompt Management API) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Humanloop (LLM Prompt Management API) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Humanloop (LLM Prompt Management API), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Humanloop (LLM Prompt Management API) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Humanloop (LLM Prompt Management API) URL.
- Step 03
Save and start
Save the connection and enable Humanloop (LLM Prompt Management API) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"humanloop-llm-prompt-management-api": {
"url": "https://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Humanloop (LLM Prompt Management API)
Open Agent mode in chat and ask: "Using Humanloop (LLM Prompt Management API), help me...". 12 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"humanloop-llm-prompt-management-api": {
"url": "https://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Humanloop (LLM Prompt Management API)
Ask Copilot: "Using Humanloop (LLM Prompt Management API), help me...". 12 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"humanloop-llm-prompt-management-api": {
"url": "https://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Humanloop (LLM Prompt Management API)
Open Cascade and ask: "Using Humanloop (LLM Prompt Management API), help me...". 12 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"humanloop-llm-prompt-management-api": {
"url": "https://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Humanloop (LLM Prompt Management API)
Ask Cline: "Using Humanloop (LLM Prompt Management API), help me...". 12 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add humanloop-llm-prompt-management-api --transport http "https://edge.vinkius.com/vk_preview_SDhmK5J7LYd6XJCbZRuzCR6WIRna9REWi8m4RQ2M/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Humanloop (LLM Prompt Management API)
Ask Claude: "Using Humanloop (LLM Prompt Management API), show me...". 12 tools are ready
Where the request belongs
Work Humanloop can move forward.
This is for the AI engineer tired of manual prompt testing and the DevOps lead who needs to automate prompt promotion across environments.
AI Engineer
Uses this to push new prompt iterations to production and stream live responses during testing without leaving the editor.
Product Manager
Audits prompt versions and reviews model logs to ensure the AI output aligns with brand guidelines.
DevOps Engineer
Automates the deployment of prompt configurations across staging and production environments to ensure consistency.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Helicone (LLM Observability)
Monitor LLM usage via Helicone. track requests, analyze costs, measure latency, and manage prompts.
Bring your own AI
Change the model, client or framework. Keep Humanloop connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Humanloop.
The practical details behind the request, access and result.
What is Humanloop MCP for?
It is for managing your entire LLM prompt library. It allows you to version, deploy, and monitor prompts directly through your AI agent.
Can I see what's live in production with Humanloop MCP?
Yes, you can see the current deployment status for all your prompts. The Connector lets your agent list every environment and show which specific version is active right now.
How do I update a prompt in Humanloop MCP?
You can update a prompt by asking your agent to change the name or description of an existing version. It uses the upsert capability to keep your Humanloop library current.
Can Humanloop MCP help with version control?
Yes, it's built for that. You can list all versions of a specific prompt, retrieve history, and push new versions to specific environments like staging or production.
Does Humanloop MCP support streaming responses?
Yes, it does. Your agent can execute a prompt and stream the response back to you in real-time, which is much better for long-form content generation.
How does Humanloop MCP handle logging?
It allows your agent to record model generations as logs. You can then review these logs to evaluate how your prompts are performing in the wild.
Can I turn off monitoring in Humanloop MCP?
Yes, you can activate or deactivate evaluators for monitoring logs. This gives you full control over which prompts are being tracked for quality.
Is Humanloop MCP good for production deployments?
It's designed specifically for it. You can manage environment-specific configurations and deploy new versions to production with a single command from your AI client.
Can I see the full version history of a specific prompt?
Yes! Use the list_prompt_versions capability with the Prompt ID. It will return all historical versions, allowing you to track changes and metadata over time.
How do I deploy a prompt version to a specific environment like production?
You can use the deploy_prompt capability. Provide the Prompt ID and the Environment ID to set that specific version as the active deployment for that environment.
Is it possible to record model outputs for later evaluation?
Absolutely. Use the log_to_prompt capability to record a generation, including the prompt path, messages, and output, which can then be used for evaluation in Humanloop.
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