Odoo Project MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Odoo Project as an MCP tool provider through the 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 Odoo Project. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Odoo Project?"
)
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 Odoo Project MCP Server
Connect Odoo ERP to any AI agent — manage your entire business without switching tabs.
LlamaIndex agents combine Odoo Project tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the 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
- CRM — Search and create leads, track opportunities through your pipeline
- Contacts — Find individual contacts and companies, create new partners
- Sales — List and manage sales orders with full order details
- Notes — Add comments and notes to any record in your Odoo instance
The Odoo Project MCP Server exposes 7 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 Odoo Project to LlamaIndex via MCP
Follow these steps to integrate the Odoo Project 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 7 tools from Odoo Project
Why Use LlamaIndex with the Odoo Project MCP Server
LlamaIndex provides unique advantages when paired with Odoo Project through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Odoo Project tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Odoo Project tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Odoo Project, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Odoo Project tools were called, what data was returned, and how it influenced the final answer
Odoo Project + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Odoo Project MCP Server delivers measurable value.
Hybrid search: combine Odoo Project real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Odoo Project 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 Odoo Project for fresh data
Analytical workflows: chain Odoo Project queries with LlamaIndex's data connectors to build multi-source analytical reports
Odoo Project MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Odoo Project to LlamaIndex via MCP:
odoo_create_project
project record. The name is the project title. Optionally link it to an existing customer/partner using their res.partner ID. Use when the user wants to set up a new project for internal work, a client engagement, or a product initiative. Create a new project in Odoo, optionally linked to a customer or partner
odoo_create_task
task record in the specified project. Requires the project.project ID (use odoo_list_projects to find it). Optionally assign to a res.users ID and set a deadline in YYYY-MM-DD format. Use when the user wants to add a to-do item, delegate work, or plan project deliverables. Create a new task within an Odoo project, optionally assigning it to a user with a deadline
odoo_list_projects
project records showing all active projects. Returns project name, project manager, linked customer/partner, total task count, start date, and end date. Use when the user asks about ongoing projects, wants a project overview, or needs to find a project ID for task creation. List all projects in Odoo with responsible person, customer, task count, and date range
odoo_list_tasks
task records. Optionally filter by project ID to see tasks for a specific project, or omit to see tasks across all projects. Returns task name, project, assigned users, kanban stage (e.g., New/In Progress/Done), priority (0=normal, 1=important), progress percentage, deadline, and creation date. Use when the user asks about to-do lists, task assignments, project progress, or upcoming deadlines. List project tasks in Odoo with assignee, kanban stage, priority, progress, and deadlines
odoo_list_timesheets
analytic.line records where project_id is set — these are timesheet entries that track hours worked. Returns description, project, task, employee, hours logged (unit_amount), and date. Use when the user asks about time tracking, billable hours, employee workload, or project time allocation. List timesheet entries in Odoo showing employee, project, task, hours logged, and date
odoo_log_timesheet
analytic.line record linking hours to a specific project and task. Requires project.project ID, project.task ID, hours worked, and a description of the work performed. Optionally specify a date (defaults to today). Use when the user wants to log time spent, track billable hours, or record daily work activities. Log a timesheet entry — record hours worked on a specific project task with a description
odoo_update_task
task record. You can change the task name, priority (0=normal, 1=important/starred), deadline (YYYY-MM-DD), or any other writable field. Use when the user wants to rename a task, change its priority, reschedule the deadline, or modify task details. Update an existing task in Odoo — change name, priority, deadline, or other properties
Example Prompts for Odoo Project in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Odoo Project immediately.
"Search for leads from the website"
"Show recent sales orders"
Troubleshooting Odoo Project MCP Server with LlamaIndex
Common issues when connecting Odoo Project to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOdoo Project + LlamaIndex FAQ
Common questions about integrating Odoo Project 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 Odoo Project with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
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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 Odoo Project to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.