How to Use the Humanloop (LLM Prompt Management API) MCP in LlamaIndex

Q: Can I run semantic search over prompts managed by the Humanloop (LLM Prompt Management API) in LlamaIndex?

Yes, your agent can run listprompts to fetch all prompt configurations, load them as documents, and index them into a vector store. This lets you run semantic queries over your prompt registry.

Q: How does this MCP Server help monitor LlamaIndex retrieval performance?

You can run logtoprompt to record the generated answers along with the retrieved source nodes. This logs the complete trace to Humanloop, making it easy to spot where retrieval or generation failed.

Q: What happens when I update a prompt configuration?

You run upsertprompt to push the new configuration. This creates a new version that you can test immediately before running deployprompt to make it active in your staging or production environments.

Index and query your Humanloop prompts and environments directly within your LlamaIndex RAG pipelines via this MCP Server.

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Connect Humanloop (LLM Prompt Management API) MCP to LlamaIndex

Create your Vinkius account to connect Humanloop (LLM Prompt Management API) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Humanloop (LLM Prompt Management API) with LlamaIndex

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Index prompt history for context-aware RAG

Use `list_prompts` and `list_prompt_versions` to pull all prompt definitions and metadata into your LlamaIndex document store. This allows your agent to query past configurations and find semantic patterns in your prompt engineering history. By indexing this data, your RAG application can ground its decisions on actual prompt configurations instead of guessing. It turns your prompt registry into a searchable knowledge base for your agents.

Execute and log runs within LlamaIndex workflows

Run `call_prompt_stream` to execute prompt templates and feed the generated output directly into your index query engines. This ensures your retrieval-augmented generations use the exact prompt configurations managed in your cloud registry. After the run, use `log_to_prompt` to send the execution trace and retrieved document contexts back to Humanloop. This lets you track how well different prompt versions perform against your retrieved index contexts.

Control deployments using this LlamaIndex MCP Server

Run `deploy_prompt` to set active prompt versions for specific environments directly from your indexing scripts. This allows you to automate prompt promotion when evaluation metrics meet your target thresholds. You can also use `remove_deployment` or `delete_prompt_version` to clean up outdated or underperforming configurations. It gives your LlamaIndex pipelines complete programmatic control over your prompt lifecycle.

Setup guide

Set up Humanloop (LLM Prompt Management API) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Humanloop (LLM Prompt Management API) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Humanloop (LLM Prompt Management API) tools.",
)
response = await agent.run("List recent Humanloop (LLM Prompt Management API) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Humanloop. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Humanloop (LLM Prompt Management API) MCP in LlamaIndex

Install the LlamaIndex MCP tool package and initialize the basic client. Wrap the client in the tool spec class to expose the prompt management tools directly to your query engines or agents.

Yes, your agent can run `list_prompts` to fetch all prompt configurations, load them as documents, and index them into a vector store. This lets you run semantic queries over your prompt registry.

You can run `log_to_prompt` to record the generated answers along with the retrieved source nodes. This logs the complete trace to Humanloop, making it easy to spot where retrieval or generation failed.

You run `upsert_prompt` to push the new configuration. This creates a new version that you can test immediately before running `deploy_prompt` to make it active in your staging or production environments.

The MCP server processes your prompt configurations, execution logs, and environment variables inside secure, ephemeral V8 isolates. Your sensitive API tokens and prompt payloads are never cached or exposed to external networks.

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