How to Use the Cohere (AI Platform) MCP in LlamaIndex

Q: Can I use Cohere (AI Platform) to rerank LlamaIndex query results?

Yes, the agent can call the rerankdocuments tool during the retrieval step. This reorders the retrieved chunks, ensuring your LlamaIndex query engine only processes the most relevant data.

Q: Does this Cohere (AI Platform) MCP Server support resource loading in LlamaIndex?

Yes, when initializing the tool spec, you can set includeresources to true. This allows your LlamaIndex agent to access server resources alongside standard execution tools.

Q: How do I restrict which tools my LlamaIndex agent can access?

You can use the allowedtools filter when setting up the client. This lets you restrict access to specific tools like generateembeddings while blocking others like chatgeneration.

Index Cohere (AI Platform) text generations and embeddings directly into your LlamaIndex knowledge bases.

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Connect Cohere (AI Platform) MCP to LlamaIndex

Create your Vinkius account to connect Cohere (AI Platform) 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 (AI Platform) with LlamaIndex

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Build semantic search indexes with LlamaIndex and Cohere (AI Platform)

By exposing `generate_embeddings` as an MCP tool, this integration lets your LlamaIndex agent index live API data on the fly. Instead of relying on static documents, your RAG application queries live data sources and embeds them instantly. This keeps your search index fresh without manual batch preprocessing. Your agent handles the vector generation dynamically as new data flows into the index.

Optimize retrieval with dynamic document reranking

Exposing `rerank_documents` as an MCP tool, this server lets your LlamaIndex query engine reorder search results dynamically. You can filter out noise from your vector store, bringing the most relevant context chunks to the top before the final generation step. Your agent gets cleaner context, which translates to faster, more accurate answers with fewer hallucinations. This workflow keeps your context windows lean and cost-effective.

Control context limits with live tokenization

Exposing `tokenize_text` and `classify_inputs` as tools, this integration keeps your LlamaIndex queries safe from context window overflows. Your agent checks the exact size of retrieved documents before formatting them into the prompt template. If the text is too long, the agent can call other tools to summarize or categorize the data first. This ensures you never waste API budget on truncated payloads.

Setup guide

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

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Common questions about Cohere (AI Platform) MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the BasicMCPClient with the server URL. Wrap it in McpToolSpec and call to_tool_list_async to pass the tools directly to your FunctionAgent.

Yes, the agent can call the rerank_documents tool during the retrieval step. This reorders the retrieved chunks, ensuring your LlamaIndex query engine only processes the most relevant data.

Yes, when initializing the tool spec, you can set include_resources to true. This allows your LlamaIndex agent to access server resources alongside standard execution tools.

You can use the allowed_tools filter when setting up the client. This lets you restrict access to specific tools like generate_embeddings while blocking others like chat_generation.

Your Cohere API credentials and index text payloads are processed within an isolated V8 sandbox. Vinkius handles the underlying authentication securely, so no raw keys are exposed to the agent or stored in the index.

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