Dagger (Programmable CI) MCP Server for LangChainGive LangChain instant access to 10 tools to Execute Graphql Query, Query Cache Volume, Query Container, and more
GDPR Free for SubscribersLangChain is the leading Python framework for composable LLM applications. Connect Dagger (Programmable CI) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this MCP Server for LangChain
The Dagger (Programmable CI) MCP Server for LangChain is a standout in the Loved By Devs category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"dagger-programmable-ci": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Dagger (Programmable CI), show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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 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.
LangChain's ecosystem of 500+ components combines seamlessly with Dagger (Programmable CI) through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 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 cache volume on Dagger (Programmable CI)
Constructs a cache volume
Query container on Dagger (Programmable CI)
Creates a scratch container and returns its ID
Query current module on Dagger (Programmable CI)
Queries the current module
Query directory on Dagger (Programmable CI)
Creates an empty directory and returns its ID
Query git on Dagger (Programmable CI)
Queries a Git repository
Query host on Dagger (Programmable CI)
Queries the host environment
Query http on Dagger (Programmable CI)
Returns a file from a URL
Query secret on Dagger (Programmable CI)
g., env://VAR_NAME, file://PATH, cmd://COMMAND). Creates a new secret
Query version on Dagger (Programmable CI)
Get the Dagger Engine version
Connect Dagger (Programmable CI) to LangChain via MCP
Follow these steps to wire Dagger (Programmable CI) into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Dagger (Programmable CI) MCP Server
LangChain provides unique advantages when paired with Dagger (Programmable CI) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Dagger (Programmable CI) MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Dagger (Programmable CI) queries for multi-turn workflows
Dagger (Programmable CI) + LangChain Use Cases
Practical scenarios where LangChain combined with the Dagger (Programmable CI) MCP Server delivers measurable value.
RAG with live data: combine Dagger (Programmable CI) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Dagger (Programmable CI), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Dagger (Programmable CI) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Dagger (Programmable CI) tool call, measure latency, and optimize your agent's performance
Example Prompts for Dagger (Programmable CI) in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Dagger (Programmable CI) to LangChain through Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersDagger (Programmable CI) + LangChain FAQ
Common questions about integrating Dagger (Programmable CI) MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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