Codefresh MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Codefresh as an MCP tool provider through 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 Codefresh. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Codefresh?"
)
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 Codefresh MCP Server
Connect your Codefresh account to any AI agent and take full control of your CI/CD and cloud-native delivery through natural conversation. Streamline how you automate and monitor software deployments natively.
LlamaIndex agents combine Codefresh tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through 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 Oversight — List and retrieve details for all CI/CD pipelines including their configurations natively
- Build Management — Trigger new builds for specific pipelines and specify branches or variables flawlessly
- Workflow Intelligence — Access detailed status and execution info for recent builds (workflows) flawlessly
- Cluster Logistics — Monitor all connected Kubernetes and delivery clusters to verify deployment targets securely
- Environment Auditing — List shared contexts, including secrets and variables, used in your workflows securely
- integrated Visibility — Retrieve detailed build metadata and user profile information directly within your workspace
The Codefresh MCP Server exposes 8 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 Codefresh to LlamaIndex via MCP
Follow these steps to integrate the Codefresh 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 8 tools from Codefresh
Why Use LlamaIndex with the Codefresh MCP Server
LlamaIndex provides unique advantages when paired with Codefresh through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Codefresh tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Codefresh tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Codefresh, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Codefresh tools were called, what data was returned, and how it influenced the final answer
Codefresh + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Codefresh MCP Server delivers measurable value.
Hybrid search: combine Codefresh real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Codefresh 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 Codefresh for fresh data
Analytical workflows: chain Codefresh queries with LlamaIndex's data connectors to build multi-source analytical reports
Codefresh MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Codefresh to LlamaIndex via MCP:
get_build_execution_details
Get detailed status and execution info for a specific build
get_my_codefresh_profile
Retrieve information about the authenticated user and account
get_pipeline_configuration
Get detailed information for a specific pipeline
list_codefresh_builds
List all recent builds (workflows) in the account
list_codefresh_pipelines
List all CI/CD pipelines in the account
list_delivery_clusters
List all connected Kubernetes/Delivery clusters
list_shared_contexts
List all shared environment contexts (secrets, variables)
trigger_codefresh_build
Trigger a new build for a specific pipeline
Example Prompts for Codefresh in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Codefresh immediately.
"List all my Codefresh pipelines."
"Trigger the 'api-service-ci' pipeline on the 'develop' branch."
"Show me the status of my recent builds."
Troubleshooting Codefresh MCP Server with LlamaIndex
Common issues when connecting Codefresh to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCodefresh + LlamaIndex FAQ
Common questions about integrating Codefresh 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 Codefresh 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 Codefresh to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.