Can the AI agent track active project milestones and timeline risks?

Yes. The agent uses the explicit milestones and risks endpoints to provide real-time updates on deliverable health, letting you manage complex Portfolio lifecycles entirely through natural language commands without sifting through gantt charts.

Does it map team hours and billable expenses internally?

Absolutely. Using the resources, time entries, and expenses tools, the agent accurately links organizational workers, the exact time they logged natively, and their costs—directly mapping them onto specific project ledgers on the fly.

Can the agent query custom issues and complex gating approvals?

Yes. The integration pulls mapped states of blocking issues and traces routing limits for approvals over timesheets or expenses, granting you full observability into administrative constraints without opening the Celoxis interface.

Celoxis MCP Server for LangChain 12 tools — connect in under 2 minutes

Name: Celoxis
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Celoxis through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "celoxis": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Celoxis, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 Celoxis MCP Server

Connect your Celoxis enterprise platform to any AI agent and take full control of your Project Portfolio Management (PPM) workflow through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Celoxis through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Project & Portfolio Mapping — List strategic portfolios and extract granular project structures including absolute timelines, completion statuses, and mapped budget blocks.
WBS & Tasks — Retrieve explicit Work Breakdown Structure nodes, identifying active assignments, task health, and explicit phase deliverables.
Resource Allocation — Evaluate working resources, parse user mappings, and expose global scheduling types and distinct system roles across your organization.
Timesheets & Accounting — Accurately pull time entries logged by members to measure billable matrices and ledger associations tied directly to tasks natively.
Issue & Risk Governance — Poll blocking issues preventing workflows and assess graded severity impacts modeled inside the Celoxis organizational risk matrix.
Approvals Pipeline — Interrogate pending validations routing over timesheets, assessing gating rules and internal clearance statuses immediately.

The Celoxis MCP Server exposes 12 tools through the Vinkius. Connect it to LangChain 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 Celoxis to LangChain via MCP

Follow these steps to integrate the Celoxis MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 12 tools from Celoxis via MCP

Why Use LangChain with the Celoxis MCP Server

LangChain provides unique advantages when paired with Celoxis through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Celoxis MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Celoxis queries for multi-turn workflows

Celoxis + LangChain Use Cases

Practical scenarios where LangChain combined with the Celoxis MCP Server delivers measurable value.

RAG with live data: combine Celoxis tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Celoxis, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Celoxis tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Celoxis tool call, measure latency, and optimize your agent's performance

Celoxis MCP Tools for LangChain (12)

These 12 tools become available when you connect Celoxis to LangChain via MCP:

get_project

Get an explicit Celoxis project and its complete intrinsic properties structure by ID

list_approvals

List explicit tracking objects identifying pending/cleared approvals over timesheets and expenses constraints

list_clients

List explicit top-level CRM organizational clients linked internally to distinct portfolios

list_expenses

List raw billable/non-billable expenses physically mapped onto task items inside the ecosystem

list_issues

List custom app items representing blocked issues explicit to complex workflows mapping problems

list_milestones

List raw milestones natively mapping absolute phase delivery tracking inside the WBS

list_portfolios

List strategic global tracking Portfolios mapping top-level aggregates over child projects natively

list_projects

List all top-level project portfolio items in Celoxis. Returns physical IDs, names, status, and timeline data

list_resources

List all explicit Celoxis working resources parsing the core user mappings handling allocations

list_risks

List explicit organizational risks bounded natively via the Celoxis custom application matrix

list_tasks

List comprehensive Work Breakdown Structure (WBS) tasks representing concrete deliverables within active projects

list_time_entries

List actual time entries logged explicitly against Celoxis tasks or projects for accounting

Example Prompts for Celoxis in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Celoxis immediately.

"List all active projects in our company portfolio and check their timeline status."

"Check the detailed logged time entries for the Marketing project and verify pending approvals."

"Extract the explicit risk logs and blocked issues reported across our client portfolio."

Troubleshooting Celoxis MCP Server with LangChain

Common issues when connecting Celoxis to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Celoxis + LangChain FAQ

Common questions about integrating Celoxis MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.