How to Use the Dagger (Programmable CI) MCP in LlamaIndex

Q: Can I filter Dagger (Programmable CI) tools in LlamaIndex?

You can use the allowedtools filter during initialization. This limits the agent to only the specific build capabilities you want it to access.

Q: How do I check my Dagger Engine version via LlamaIndex?

You call the queryversion tool. The result is returned as a string, which you can then log or index to keep track of your environment dependencies.

Index your build metadata into LlamaIndex with the Dagger (Programmable CI) MCP Server.

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Connect Dagger (Programmable CI) MCP to LlamaIndex

Create your Vinkius account to connect Dagger (Programmable CI) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Dagger (Programmable CI) with LlamaIndex

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Perplexity

Graph-based builds for LlamaIndex

Execute complex build logic by piping `execute_graphql_query` output into your RAG pipeline. LlamaIndex takes these results and indexes them for semantic retrieval. You turn your CI logs into a searchable knowledge base. When you ask about past build failures, your agent finds the exact GraphQL trace that caused the error.

Index container state in LlamaIndex

Use `query_container` to capture the state of your build environments. LlamaIndex stores these IDs, allowing your agent to reference previous container configurations. You gain a history of your infrastructure. Instead of guessing how a container was configured, you query the index for the actual metadata generated during the session.

Search build modules with LlamaIndex

Query your current module structure using `query_current_module`. The server returns the hierarchy, which LlamaIndex then indexes for your assistant. Your agent understands your project structure better. It knows exactly which directories and files are available to the build engine before it tries to run a task.

Setup guide

Set up Dagger (Programmable CI) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Dagger (Programmable CI) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Dagger (Programmable CI) tools.",
)
response = await agent.run("List recent Dagger (Programmable CI) data")

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Common questions about Dagger (Programmable CI) MCP in LlamaIndex

The server outputs structured data that the indexing engine parses. You query your past builds just like you query any other document set in your knowledge base.

Yes, the tool outputs map directly to your vector store records. You configure the embedding model to handle the GraphQL response formats provided by the server.

You can use the allowed_tools filter during initialization. This limits the agent to only the specific build capabilities you want it to access.

You call the `query_version` tool. The result is returned as a string, which you can then log or index to keep track of your environment dependencies.

The raw execution data is treated as transient input for your index. We isolate the memory buffers used during the query, so your sensitive build logs never leak into public storage.

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