Dagger (Programmable CI) MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Execute Graphql Query, Query Cache Volume, Query Container, and more

Name: Dagger (Programmable CI)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

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LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dagger (Programmable CI) 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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The Dagger (Programmable CI) MCP Server for LlamaIndex is a standout in the Loved By Devs category — giving your AI agent 10 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

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 Dagger (Programmable CI). "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Dagger (Programmable CI)?"
    )
    print(response)

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 Dagger (Programmable CI) MCP Server

Connect to the Dagger Engine to orchestrate your delivery pipelines using a powerful, programmable GraphQL API. This server allows your AI agent to interact directly with Dagger's Directed Acyclic Graph (DAG) of operations.

LlamaIndex agents combine Dagger (Programmable CI) tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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

Container Orchestration — Initialize scratch containers, pull images, and manage OCI-compatible states.
GraphQL Workflows — Execute raw GraphQL queries to compose complex build and test logic dynamically.
Source Control — Query Git repositories and host environments to pull source code into your pipelines.
Resource Management — Handle secrets securely, manage persistent cache volumes, and fetch remote files via HTTP.
Module Inspection — Query the current module state and engine version to ensure environment consistency.

The Dagger (Programmable CI) MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 10 Dagger (Programmable CI) tools available for LlamaIndex

When LlamaIndex connects to Dagger (Programmable CI) through Vinkius, your AI agent gets direct access to every tool listed below — spanning ci-cd, container-orchestration, pipeline-automation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

execute

Execute graphql query on Dagger (Programmable CI)

You can chain fields to create a Directed Acyclic Graph (DAG) of operations. Execute a raw GraphQL query against the Dagger engine

query

Query cache volume on Dagger (Programmable CI)

Constructs a cache volume

query

Query container on Dagger (Programmable CI)

Creates a scratch container and returns its ID

query

Query current module on Dagger (Programmable CI)

Queries the current module

query

Query directory on Dagger (Programmable CI)

Creates an empty directory and returns its ID

query

Query git on Dagger (Programmable CI)

Queries a Git repository

query

Query host on Dagger (Programmable CI)

Queries the host environment

query

Query http on Dagger (Programmable CI)

Returns a file from a URL

query

Query secret on Dagger (Programmable CI)

g., env://VAR_NAME, file://PATH, cmd://COMMAND). Creates a new secret

query

Query version on Dagger (Programmable CI)

Get the Dagger Engine version

Connect Dagger (Programmable CI) to LlamaIndex via MCP

Follow these steps to wire Dagger (Programmable CI) into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 Dagger (Programmable CI)

Why Use LlamaIndex with the Dagger (Programmable CI) MCP Server

LlamaIndex provides unique advantages when paired with Dagger (Programmable CI) through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Dagger (Programmable CI) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Dagger (Programmable CI) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Dagger (Programmable CI), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Dagger (Programmable CI) tools were called, what data was returned, and how it influenced the final answer

Dagger (Programmable CI) + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Dagger (Programmable CI) MCP Server delivers measurable value.

Hybrid search: combine Dagger (Programmable CI) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Dagger (Programmable CI) 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 Dagger (Programmable CI) for fresh data

Analytical workflows: chain Dagger (Programmable CI) queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Dagger (Programmable CI) in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Dagger (Programmable CI) immediately.

"Check the current version of the Dagger engine."

"Initialize a scratch container and return its ID."

"Get the state of the git repository at https://github.com/dagger/dagger."

Troubleshooting Dagger (Programmable CI) MCP Server with LlamaIndex

Common issues when connecting Dagger (Programmable CI) to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Dagger (Programmable CI) + LlamaIndex FAQ

Common questions about integrating Dagger (Programmable CI) MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Dagger (Programmable CI) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.