Hashnode MCP Server for LlamaIndexGive LlamaIndex instant access to 5 tools to Create Post, Get Post, Get Publication Posts, and more

Name: Hashnode
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Hashnode 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 Hashnode MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 5 tools to work with, ready to go from day one.

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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 Hashnode. "
            "You have 5 tools available."
        ),
    )

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

asyncio.run(main())

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About Hashnode MCP Server

Connect your Hashnode account to any AI agent to streamline your technical writing and blogging workflow. This MCP server allows you to interact with the Hashnode GraphQL API through natural language.

LlamaIndex agents combine Hashnode tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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

User Profiles — Retrieve detailed information about any Hashnode user by their handle using get_user.
Publication Management — List all posts from a specific publication using its domain or hostname with get_publication_posts.
Content Retrieval — Fetch full post content and metadata using slugs and hostnames via get_post.
Publishing — Create and publish new blog posts with Markdown support and custom tags using create_post directly from your conversation.
Editing — Update existing posts, titles, and content with update_post without leaving your AI interface.

The Hashnode MCP Server exposes 5 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 5 Hashnode tools available for LlamaIndex

When LlamaIndex connects to Hashnode through Vinkius, your AI agent gets direct access to every tool listed below — spanning blogging, technical-writing, content-creation, 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.

create

Create post on Hashnode

Publish a new post to a publication

get

Get post on Hashnode

Get a single post by its slug

get

Get publication posts on Hashnode

Get a list of posts from a specific publication

get

Get user on Hashnode

Get details about a specific Hashnode user

update

Update post on Hashnode

Modify an existing post

Connect Hashnode to LlamaIndex via MCP

Follow these steps to wire Hashnode 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 5 tools from Hashnode

Why Use LlamaIndex with the Hashnode MCP Server

LlamaIndex provides unique advantages when paired with Hashnode through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Hashnode tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Hashnode tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Hashnode, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Hashnode tools were called, what data was returned, and how it influenced the final answer

Hashnode + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Hashnode MCP Server delivers measurable value.

Hybrid search: combine Hashnode real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Hashnode 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 Hashnode for fresh data

Analytical workflows: chain Hashnode queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Hashnode in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Hashnode immediately.

"Get details for Hashnode user 'johndoe'."

"List all posts from the publication 'blog.hashnode.com'."

"Create a new post in publication '642f...' titled 'My MCP Guide' with content '# Introduction...'."

Troubleshooting Hashnode MCP Server with LlamaIndex

Common issues when connecting Hashnode to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Hashnode + LlamaIndex FAQ

Common questions about integrating Hashnode 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 Hashnode tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.