How to Use the Deno Deploy MCP in LlamaIndex

Q: How can LlamaIndex query the status of a Deno Deploy app?

First, your agent uses tools like getapp and getrevision to fetch the data. LlamaIndex then indexes this data. From that point on, queries about status are answered from the local, indexed knowledge base for speed and context.

Q: Can I use LlamaIndex to find out why a Deno Deploy build failed?

Absolutely. Your agent can index the output of getbuildlogs. Then you can ask it semantic questions like, "What was the root cause of the last build failure for the API project?" and get a specific answer from the indexed logs.

Turn your Deno Deploy infrastructure into a queryable knowledge base for LlamaIndex.

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Connect Deno Deploy MCP to LlamaIndex

Create your Vinkius account to connect Deno Deploy 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 Deno Deploy with LlamaIndex

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Index your entire deployment history

Connect this MCP Server to LlamaIndex to build a searchable history of all your Deno Deploy activity. Your agent can periodically run `list_apps` and `list_revisions` for each app, feeding the results directly into a vector store. Once indexed, you can ask complex questions in natural language. Try asking, "What was the last successful deployment for the marketing site?" or "Show me all revisions from last Tuesday." Your agent finds the answer from its knowledge base, grounded in actual deployment data.

Create a semantic search engine for logs

Stop manually searching through logs. Have your LlamaIndex agent ingest output from `get_app_logs` and `get_build_logs` into a knowledge base. This turns raw text into structured, searchable data. Now you can ask questions like, "Were there any database connection errors in the past hour across all production apps?" Your agent understands the *meaning* behind your question and retrieves the exact log entries you need from the index, not just keyword matches.

Query your Deno Deploy configuration with LlamaIndex

Your agent can build and maintain an index of your organization's entire configuration. By calling `list_projects`, `list_domains`, and `get_app`, it creates a snapshot of your infrastructure that you can query anytime. This lets you ask questions that are impossible to answer with a simple dashboard. For example: "Which projects are still using the old v1 project API?" or "List all apps that don't have a custom domain." Your agent gives you instant, accurate answers based on the indexed state.

Setup guide

Set up Deno Deploy 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 Deno Deploy 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 Deno Deploy tools.",
)
response = await agent.run("List recent Deno Deploy data")

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Common questions about Deno Deploy MCP in LlamaIndex

First, your agent uses tools like `get_app` and `get_revision` to fetch the data. LlamaIndex then indexes this data. From that point on, queries about status are answered from the local, indexed knowledge base for speed and context.

Absolutely. Your agent can index the output of `get_build_logs`. Then you can ask it semantic questions like, "What was the root cause of the last build failure for the API project?" and get a specific answer from the indexed logs.

You configure your LlamaIndex agent to periodically re-run the MCP tools and update its knowledge base. This keeps your queryable data in sync with the actual state of your Deno Deploy apps, ensuring your answers are always fresh.

This MCP server provides standardized tools that are ready to use, so you don't write any boilerplate API code. More importantly, it lets LlamaIndex do what it does best: turn live operational data into a persistent, searchable knowledge graph.

The server itself is stateless; it only fetches data like your app list, domains, and logs on demand. Your data is processed in a sandboxed environment and only exists for the duration of the API call. You control where the LlamaIndex agent stores the final index.

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