# MCP Server: HackerNews — Search, Comments, Show HN (`dltik/mcp-server-hackernews`) Actor Apify MCP server for AI agents: 6 tools to query HackerNews via Algolia API. Search stories/comments, top front page, Show HN trending, user profiles. Free official API, $0.005/call. - **URL**: https://apify.com/dltik/mcp-server-hackernews.md - **Developed by:** [dltik](https://apify.com/dltik) (community) - **Categories:** AI, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 tool calls This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## HackerNews MCP Server — Search Stories, Comments, Show HN for AI Agents > Apify-hosted **HackerNews MCP server** giving AI agents instant access to 30M+ HN stories and comments via the official Algolia HN Search API. Search the front page, find Show HN trends, pull threaded comments, get user profiles. No API key, no rate limits to fight. **$0.005 per tool call** (cheapest MCP on the Apify Store). ⭐ **Bookmark this HackerNews MCP Server** — Apify ranks actors by bookmarks, so it directly helps the visibility of this server on the Apify Store. ### What is the HackerNews MCP Server? The **HackerNews MCP Server** is an Apify actor implementing the [Model Context Protocol](https://modelcontextprotocol.io/) for [HackerNews](https://news.ycombinator.com/). It wraps the [Algolia HN Search API](https://hn.algolia.com/api) — the canonical search backend HN itself uses — into 6 clean tools your AI agent can call. Wire it into Claude Desktop, Cursor, or Continue.dev to give your AI agent a live pulse on the HN tech community. Tech founders, devs, and PMs read HN for a reason. This MCP server lets an AI agent read it for you and surface what matters. ### Use cases - **Tech trend agents** — let your AI agent scan HN front page daily and summarize what's hot. - **Show HN tracker** — monitor product launches in your niche (`top_show_hn` returns trending Show HN posts). - **Comment sentiment** — pull all comments on a story and analyze tone, dissent, key arguments. - **Founder research** — `get_user` returns submission history for any HN user — useful for due diligence. - **Competitive intel** — `search_stories(query="your competitor")` to find every HN mention. - **Content marketing research** — find which posts on a topic ranked well, model your headlines on theirs. ### Tools available | Tool | What it returns | | --- | --- | | `search_stories(query, sort, limit)` | Story search — sort by relevance or date | | `search_comments(query, limit)` | Comment search — full-text | | `top_stories(limit)` | Current HN front page | | `story_with_comments(story_id)` | One story + nested comment tree | | `top_show_hn(limit)` | Trending Show HN posts | | `get_user(username)` | User profile + recent submissions | | `list_tools` | Free — schema discovery | ### Input ```json { "mode": "call_tool", "tool_name": "search_stories", "arguments": { "query": "model context protocol", "sort": "relevance", "limit": 10 } } ```` ### Output ```json { "id": "39495382", "type": "story", "title": "Show HN: Our open-source MCP server registry", "url": "https://github.com/example/mcp-registry", "author": "example_user", "points": 412, "num_comments": 87, "created_at": "2026-04-20T14:33:12Z", "hn_url": "https://news.ycombinator.com/item?id=39495382" } ``` ### Pricing **PAY\_PER\_EVENT — $0.005 per tool call** (= $5 per 1,000). The `list_tools` discovery call is free. No charge on errors. This is the cheapest MCP server on the Apify Store because the underlying Algolia HN API is fast and free, so we pass the savings on. ### FAQ — HackerNews API for AI agents **HN Algolia vs Firebase API?** Algolia is the search-optimized layer (full-text, fast, scoring). Firebase is the raw data layer (best for "fetch item by ID"). This MCP server uses Algolia for its 6 tools because search and filter is the agent use case. If you need real-time front-page polling, the HN Firebase API is better — DM and we'll add a tool. **Does it support HN polls and Ask HN?** Yes — `search_stories` with the right tag returns Ask HN, polls, jobs. See `list_tools` output for full filter syntax. **Can I use this for Show HN trend analysis?** Yes — `top_show_hn(limit=50)` returns Show HN posts ranked by recent traction. Plug into your AI agent for daily digests. *** ⭐ **Found this useful? Bookmark this HackerNews MCP Server** — it's the strongest signal for Apify Store ranking. #### Related actors - [SEC EDGAR MCP Server](https://apify.com/dltik/mcp-server-sec-edgar) — US public companies, same MCP pattern - [Pappers MCP Server](https://apify.com/dltik/mcp-server-pappers) — 26M French companies - [BODACC MCP Server](https://apify.com/dltik/mcp-server-bodacc) — French commercial court announcements License: MIT · Author: [dltik](https://apify.com/dltik) # Actor input Schema ## `mode` (type: `string`): list\_tools (free) or call\_tool (charged $0.005 per call). ## `tool_name` (type: `string`): Tool to invoke. Use mode=list\_tools to discover. ## `arguments` (type: `object`): Arguments for the chosen tool. See list\_tools mode for full schema. ## Actor input object example ```json { "mode": "call_tool", "tool_name": "search_stories", "arguments": { "query": "claude code", "limit": 10 } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "arguments": { "query": "claude code", "limit": 10 } }; // Run the Actor and wait for it to finish const run = await client.actor("dltik/mcp-server-hackernews").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "arguments": { "query": "claude code", "limit": 10, } } # Run the Actor and wait for it to finish run = client.actor("dltik/mcp-server-hackernews").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "arguments": { "query": "claude code", "limit": 10 } }' | apify call dltik/mcp-server-hackernews --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dltik/mcp-server-hackernews", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Server: HackerNews — Search, Comments, Show HN", "description": "Apify MCP server for AI agents: 6 tools to query HackerNews via Algolia API. Search stories/comments, top front page, Show HN trending, user profiles. Free official API, $0.005/call.", "version": "1.0", "x-build-id": "nLFiB0O6OL66zD85C" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dltik~mcp-server-hackernews/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dltik-mcp-server-hackernews", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dltik~mcp-server-hackernews/runs": { "post": { "operationId": "runs-sync-dltik-mcp-server-hackernews", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dltik~mcp-server-hackernews/run-sync": { "post": { "operationId": "run-sync-dltik-mcp-server-hackernews", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Mode", "enum": [ "call_tool", "list_tools" ], "type": "string", "description": "list_tools (free) or call_tool (charged $0.005 per call).", "default": "call_tool" }, "tool_name": { "title": "Tool name", "enum": [ "search_stories", "search_comments", "top_stories", "story_with_comments", "get_user", "top_show_hn" ], "type": "string", "description": "Tool to invoke. Use mode=list_tools to discover.", "default": "search_stories" }, "arguments": { "title": "Arguments", "type": "object", "description": "Arguments for the chosen tool. See list_tools mode for full schema." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```