Celoxis MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Celoxis as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Celoxis. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Celoxis?"
)
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 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.
LlamaIndex agents combine Celoxis tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- 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 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 Celoxis to LlamaIndex via MCP
Follow these steps to integrate the Celoxis 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 12 tools from Celoxis
Why Use LlamaIndex with the Celoxis MCP Server
LlamaIndex provides unique advantages when paired with Celoxis through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Celoxis tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Celoxis tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Celoxis, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Celoxis tools were called, what data was returned, and how it influenced the final answer
Celoxis + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Celoxis MCP Server delivers measurable value.
Hybrid search: combine Celoxis real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Celoxis 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 Celoxis for fresh data
Analytical workflows: chain Celoxis queries with LlamaIndex's data connectors to build multi-source analytical reports
Celoxis MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Celoxis to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Celoxis to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCeloxis + LlamaIndex FAQ
Common questions about integrating Celoxis 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 Celoxis 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 Celoxis to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.