Cohere MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cohere as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Cohere. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Cohere?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Cohere MCP Server
Connect your Cohere account to any AI agent and leverage enterprise-grade AI models through natural conversation.
LlamaIndex agents combine Cohere tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Model Discovery — List all available Cohere models with their names, capabilities and context lengths
- Chat API — Send conversations to Command models (command-r-plus, command-r, command-r7b) and receive responses with citations and tool call support
- Embeddings — Generate vector embeddings for semantic search with multiple embedding types (float, int8, uint8, binary)
- Reranking — Rerank documents by relevance to a search query using Cohere's industry-leading reranking models
- Tokenization — Tokenize and detokenize text for estimating token counts and debugging
The Cohere MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Cohere to LlamaIndex via MCP
Follow these steps to integrate the Cohere MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from Cohere
Why Use LlamaIndex with the Cohere MCP Server
LlamaIndex provides unique advantages when paired with Cohere through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cohere tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cohere tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cohere, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cohere tools were called, what data was returned, and how it influenced the final answer
Cohere + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cohere MCP Server delivers measurable value.
Hybrid search: combine Cohere real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cohere to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cohere for fresh data
Analytical workflows: chain Cohere queries with LlamaIndex's data connectors to build multi-source analytical reports
Cohere MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Cohere to LlamaIndex via MCP:
chat
Requires the model ID (e.g. "command-r-plus", "command-r", "command-r7b") and messages array in JSON format. Each message must have a "role" ("user", "assistant", "system" or "tool") and "content" (text or array of content blocks). Optionally set max_tokens, temperature (0-1), p (nucleus sampling 0-1) and tools array for function calling. Returns the model's response with text, citations and tool calls. Send a chat message to a Cohere model
detokenize
Requires the token IDs array. Returns the reconstructed text. Useful for debugging and verifying tokenization. Detokenize token IDs back to text using Cohere
embed
Requires the model ID (e.g. "embed-v4", "embed-v3"), texts array and input_type ("search_document", "search_query", "classification", "clustering"). Returns embedding vectors for each input text. Useful for semantic search, similarity comparison and vector database storage. Generate embeddings using Cohere
list_models
Each model returns its name (e.g. "command-r-plus", "command-r", "embed-v4", "rerank-v3.5"), endpoint compatibility, context length and tokenization info. Use this to discover which models are available and their capabilities. List all available Cohere models
rerank
Requires the model ID (e.g. "rerank-v3.5", "rerank-english-v3.0"), query text and documents array. Optionally set top_n to return only the top N results. Returns ranked documents with relevance scores. Rerank documents by relevance to a query
tokenize
Requires the text to tokenize and optionally the model. Returns the list of token IDs and token strings. Useful for estimating token counts before sending to chat or embed endpoints. Tokenize text using Cohere
Example Prompts for Cohere in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cohere immediately.
"Send a message to Command R+ asking 'What is the capital of Brazil?'"
"Rerank these documents for the query 'machine learning models': ['Neural networks are inspired by biological neurons.', 'Python is a popular programming language.', 'Transformers use attention mechanisms for sequence processing.']"
"Generate embeddings for these texts: ['The weather is nice today.', 'I love programming in Python.'] using embed-v4."
Troubleshooting Cohere MCP Server with LlamaIndex
Common issues when connecting Cohere to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCohere + LlamaIndex FAQ
Common questions about integrating Cohere MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Cohere with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Cohere to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.