How to Use the Cohere MCP in LlamaIndex

Q: How does LlamaIndex handle Cohere embeddings?

The server provides the embed tool which LlamaIndex uses to convert text into vector representations. These vectors are then stored and retrieved during your search operations.

Q: Can I use Cohere reranking in a LlamaIndex pipeline?

You can. After retrieving documents, feed them into the rerank tool to sort them by relevance before passing them to the final generation step.

Q: Does it support async tool calls?

The server is built for async operations. You call totoollistasync to integrate tools without blocking your main application loop.

Index your API data into LlamaIndex using Cohere embeddings for precise semantic search and retrieval.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cohere MCP to LlamaIndex

Create your Vinkius account to connect Cohere 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.

GDPR Free for Subscribers

Setup Cohere with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Generate embeddings with LlamaIndex

Turn your text into vectors using the `embed` tool so LlamaIndex can store them in a vector database. This makes your custom data searchable by meaning instead of just keywords. Once indexed, your knowledge base becomes much more effective. You can query past documents and get answers grounded in the specific data you provided.

Build RAG apps with Cohere and LlamaIndex

Combine the `chat` tool with your existing index to ground model responses in your own records. The agent retrieves relevant chunks and uses the model to synthesize a final answer. This approach stops the model from making things up. It sticks to the facts contained within your indexed documents.

Filter tools for LlamaIndex agents

Restrict which tools your agent can use by applying an allowed_tools filter. This keeps your LlamaIndex application focused on specific tasks like retrieval or ranking. It makes the agent more predictable. You decide exactly which capabilities are available for any given query.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cohere. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cohere now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cohere MCP in LlamaIndex

The server provides the `embed` tool which LlamaIndex uses to convert text into vector representations. These vectors are then stored and retrieved during your search operations.

You can. After retrieving documents, feed them into the `rerank` tool to sort them by relevance before passing them to the final generation step.

It is useful for validating document lengths before they hit the index. You use `tokenize` to ensure your data chunks fit within the context window of the model.

The server is built for async operations. You call `to_tool_list_async` to integrate tools without blocking your main application loop.

Your embedding vectors are never persisted by our infrastructure. The server acts as a transient bridge, and your data privacy is maintained by the ephemeral nature of the sandbox.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript