Harness MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Harness 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 Harness. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Harness?"
)
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 Harness MCP Server
Connect your Harness.io platform to any AI agent and take full control of your software delivery and CI/CD pipelines through natural conversation.
LlamaIndex agents combine Harness tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- Pipeline Management — List, inspect, and trigger pipeline executions across your projects.
- Execution Monitoring — Get real-time status updates and step details for active deployments.
- Project Oversight — Browse your organizational structure and list projects within specific organizations.
- Secrets & Infrastructure — Access lists of secrets, connectors, and environments to ensure your infrastructure is correctly configured.
- Audit & Compliance — Retrieve platform audit logs to monitor changes and ensure security standards.
- Service Insights — List microservices and environments defined in your DevOps ecosystem.
The Harness MCP Server exposes 11 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 Harness to LlamaIndex via MCP
Follow these steps to integrate the Harness 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 11 tools from Harness
Why Use LlamaIndex with the Harness MCP Server
LlamaIndex provides unique advantages when paired with Harness through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Harness tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Harness tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Harness, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Harness tools were called, what data was returned, and how it influenced the final answer
Harness + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Harness MCP Server delivers measurable value.
Hybrid search: combine Harness real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Harness 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 Harness for fresh data
Analytical workflows: chain Harness queries with LlamaIndex's data connectors to build multi-source analytical reports
Harness MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Harness to LlamaIndex via MCP:
execute_pipeline
Trigger the execution of a pipeline
get_audit_logs
Retrieve platform audit logs
get_execution_status
Get status and step details for a specific pipeline execution
get_pipeline
Get details and YAML for a specific pipeline
list_connectors
List infrastructure connectors (Git, Docker, K8s, etc.)
list_environments
List environments defined in a project
list_executions
List executions for a specific pipeline
list_pipelines
List pipelines within a specific project
list_projects
List all projects in the configured Harness organization
list_secrets
List secrets configured in a project
list_services
List services (microservices) defined in a project
Example Prompts for Harness in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Harness immediately.
"List all pipelines in project 'E-commerce App'."
"Execute the 'Production Deploy' pipeline for project ID app_502."
"Show the status of the latest execution for pipeline deploy_v1."
Troubleshooting Harness MCP Server with LlamaIndex
Common issues when connecting Harness to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHarness + LlamaIndex FAQ
Common questions about integrating Harness 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 Harness 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 Harness to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.