How to Use the Cohere (Embed & Rerank) MCP in LlamaIndex

Q: Can I filter tools from this MCP Server in LlamaIndex?

You can use the allowedtools filter to restrict the agent to specific functions. This keeps your indexing logic clean and predictable.

Index your live API data in LlamaIndex using Cohere (Embed & Rerank) to ground your RAG applications in facts.

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Connect Cohere (Embed & Rerank) MCP to LlamaIndex

Create your Vinkius account to connect Cohere (Embed & Rerank) 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 Cohere (Embed & Rerank) with LlamaIndex

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Semantic indexing for LlamaIndex

Convert your documents into high-quality vectors using `embed_texts` and store them in your index. This ensures your semantic searches hit the right data every time. Run `rerank_documents` after your initial search to refine the results LlamaIndex provides to the LLM. It helps your application ignore irrelevant context.

Token-aware RAG in LlamaIndex

Check the structural limits of your knowledge base using `tokenize_text` before you build your index. This prevents overflow errors when inserting documents into your vector store. Call `list_models` to verify the configuration of your indexing engine. You'll know exactly which model is handling your semantic embeddings.

Classify and complete in LlamaIndex

Use `classify_texts` to label nodes within your LlamaIndex knowledge base. It organizes your data so your query engine can fetch specific classes of information. Apply `chat_completion` to format the final output of your RAG pipeline. The tool provides a clean response structure that your agent can present to the user.

Setup guide

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

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Common questions about Cohere (Embed & Rerank) MCP in LlamaIndex

The framework converts tool results into indexed nodes. This turns API-provided data into a searchable knowledge base for your agent.

It improves the precision of your retrievals. By reranking the initial search results, you get better answers without needing to process massive context windows.

You can use the allowed_tools filter to restrict the agent to specific functions. This keeps your indexing logic clean and predictable.

The tools support asynchronous execution. You can call the tool list and integrate them directly into your FunctionAgent without blocking your main event loop.

We isolate your text inputs within a temporary sandbox environment. No persistent storage exists for your data beyond the scope of a single API request.

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