How to Use the Hashnode MCP in LlamaIndex

Q: How do I build a RAG pipeline using Hashnode and LlamaIndex?

You fetch your posts using getpublicationposts, then parse the markdown into document nodes. LlamaIndex indexes these nodes into a vector store, making your entire publication searchable for your agent.

Q: How do I filter specific authors when querying Hashnode in LlamaIndex?

Use the getuser tool to resolve the author's details and unique identifier first. You can then use this ID to filter publication posts or metadata within your index queries.

Q: What tools are available in the MCP Server to retrieve raw markdown for indexing?

The getpost tool is the primary way to retrieve the full markdown payload of an article. It provides the clean text needed to chunk and embed your content.

Index your entire Hashnode publication into LlamaIndex vector stores using this MCP Server to ground your agent in your actual technical writing.

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Connect Hashnode MCP to LlamaIndex

Create your Vinkius account to connect Hashnode 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 Hashnode with LlamaIndex

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Index your blog history with the Hashnode MCP Server

The `get_publication_posts` tool retrieves your entire article history so LlamaIndex can parse and index it. This turns your live publication into a searchable vector index, letting your agent reference real technical posts instead of hallucinating facts. You feed this live data directly into a query engine. The agent retrieves the exact posts it needs to answer user queries with high precision.

Ground content generation in your existing writing style

The `get_post` tool fetches the raw content of your articles, which LlamaIndex uses to build a style profile. Your agent reads this material to learn how you structure code blocks and explain complex concepts. When you ask the agent to write a new post, it references this index to mimic your voice. It ensures new drafts match your established formatting standards.

Sync local updates to your live publication

The `update_post` tool modifies your live articles when your local knowledge base changes using this MCP server. If you update a code snippet in your local files, LlamaIndex detects the change and pushes the edit to Hashnode. This keeps your online documentation in sync with your source code. The tool handles the API payload delivery, while the framework manages the change detection.

Setup guide

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

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Common questions about Hashnode MCP in LlamaIndex

You fetch your posts using `get_publication_posts`, then parse the markdown into document nodes. LlamaIndex indexes these nodes into a vector store, making your entire publication searchable for your agent.

Yes, you can write a script that checks for local changes and invokes the `update_post` tool. The agent compares the local file hash with the live post content retrieved via `get_post` before pushing updates.

Use the `get_user` tool to resolve the author's details and unique identifier first. You can then use this ID to filter publication posts or metadata within your index queries.

The `get_post` tool is the primary way to retrieve the full markdown payload of an article. It provides the clean text needed to chunk and embed your content.

Your publication posts and draft text are processed strictly within an ephemeral, zero-trust V8 sandbox. This architecture guarantees that sensitive draft content never leaks to third-party logging services.

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