How to Use the Courier MCP in LlamaIndex

Q: How do I configure the Courier MCP integration with LlamaIndex?

Install llama-index-tools-mcp via pip. Initialize a BasicMCPClient with your HTTP endpoint and convert it using McpToolSpec.

Q: How do I filter Courier tools in LlamaIndex?

Use the allowedtools parameter when building your tool list. This restricts the agent to specific operations like reading templates.

Index Courier MCP Server delivery logs and user profiles into searchable LlamaIndex vector stores.

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Connect Courier MCP to LlamaIndex

Create your Vinkius account to connect Courier 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 Courier with LlamaIndex

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Ground RAG applications in Courier MCP Server data

LlamaIndex turns raw API outputs into searchable knowledge. You connect the Courier MCP server, and your application ingests live delivery states right alongside your internal documents. An agent pulls records via `list_messages` and indexes them. When someone asks why an alert failed, the system queries the vector store and returns an answer based on actual provider logs, not guesses.

Index brand and template configurations

Managing multiple notification layouts gets messy. Your RAG app can read your entire workspace setup and make it semantically searchable. The system runs `list_brands` and `list_templates` to build a configuration index. Developers can ask the agent which template supports SMS, and it retrieves the exact ID needed for the next step.

Query historical delivery patterns

Support teams need fast access to past events. By indexing notification data, you create a dedicated search engine for your communication history. Your application fetches provider responses using `get_message_details`. It embeds these logs so support agents can search for specific failure codes or bounce reasons using natural language.

Setup guide

Set up Courier 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 Courier 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 Courier tools.",
)
response = await agent.run("List recent Courier data")

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Common questions about Courier MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Initialize a `BasicMCPClient` with your HTTP endpoint and convert it using `McpToolSpec`.

It sure can. The agent fetches logs, embeds the text, and stores it in your vector database for fast semantic retrieval.

Use the `allowed_tools` parameter when building your tool list. This restricts the agent to specific operations like reading templates.

Yes. Once it finds the right context in the index, the agent can trigger a live message to the user.

The MCP server operates inside a stateless V8 Isolate. Your provider responses and rendering metadata only exist during the active session and vanish the moment the connection closes.

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