PingCode MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PingCode 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 PingCode. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PingCode?"
)
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 PingCode MCP Server
Empower your AI agent to orchestrate your software development lifecycle with PingCode, the premier agile project management platform for R&D teams. By connecting PingCode to your agent, you transform complex issue tracking, sprint planning, and knowledge management into a natural conversation. Your agent can instantly list your agile projects, create work items, monitor sprint progress, and even retrieve wiki pages without you needing to navigate the complex PingCode dashboard. Whether you are following Scrum or Kanban, your agent acts as a real-time R&D assistant, ensuring your development pipeline is always moving and your documentation is accessible.
LlamaIndex agents combine PingCode tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Agile Management — List agile projects and get detailed information about your development workspace.
- Work Item Control — Create and track tasks, stories, and bugs with full support for descriptions and metadata.
- Sprint & Release Tracking — Monitor active sprints and upcoming releases to stay on top of your delivery schedule.
- Knowledge Management — Browse wiki repositories and retrieve page content to access project documentation instantly.
- Team Overview — List organization teams and members to manage collaboration and assignments effectively.
The PingCode MCP Server exposes 10 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 PingCode to LlamaIndex via MCP
Follow these steps to integrate the PingCode 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 10 tools from PingCode
Why Use LlamaIndex with the PingCode MCP Server
LlamaIndex provides unique advantages when paired with PingCode through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PingCode tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PingCode tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PingCode, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PingCode tools were called, what data was returned, and how it influenced the final answer
PingCode + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PingCode MCP Server delivers measurable value.
Hybrid search: combine PingCode real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PingCode 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 PingCode for fresh data
Analytical workflows: chain PingCode queries with LlamaIndex's data connectors to build multi-source analytical reports
PingCode MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PingCode to LlamaIndex via MCP:
create_work_item
Create a work item
get_project
Get project details
get_wiki_page
Get wiki page content
list_members
List organization members
list_projects
List PingCode agile projects
list_releases
List project releases
list_sprints
List project sprints
list_teams
List organization teams
list_wiki_pages
List wiki pages
list_work_items
List work items in a project
Example Prompts for PingCode in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with PingCode immediately.
"List all agile projects in my PingCode organization."
"Create a new bug item in project 'Checkout Flow' titled 'Payment timeout on mobile'."
"Retrieve the content of the wiki page 'System Architecture' from repository 'PROJ-DOCS'."
Troubleshooting PingCode MCP Server with LlamaIndex
Common issues when connecting PingCode to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPingCode + LlamaIndex FAQ
Common questions about integrating PingCode 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 PingCode 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 PingCode to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.