hn-developer-sentiment-mcp

_What developers really think about your tool._ **A deterministic read on Hacker News developer sentiment for any brand, tool, or library** — sentiment, mention volume, recurring themes, and feature requests across HN stories and comments, exposed as **four agent-callable MCP tools**. The differentiator is **judgment and aggregation, not raw access**: it scores, clusters, and mines public HN discussion so an AI agent can act in a single call, and returns the raw matched items alongside the aggregates so every number is auditable. **Zero setup — no API key.** The Hacker News (Algolia) API is open, so this Actor boots and returns data immediately. No credentials, no approval queue, no first-run friction. No LLM in the analysis layer — sentiment is a transparent lexicon-plus-rules scorer, so the same posts always produce the same answer. ## Who is this for? - **AI agents** that need a one-call read on how developers feel about a product before drafting a comparison, a launch post, or a support reply. - **DevRel and product teams** tracking how a tool, framework, or release is landing with the Hacker News crowd. - **Founders and marketers** validating positioning against what developers actually say in public. ## Why use Hacker News Developer Sentiment? - **Zero setup** — no API key, no login, no approval process. Deploy and call. - **One call, a usable answer** — sentiment label, net score, volume, top themes, and representative samples in a single structured response. - **Deterministic and auditable** — no model temperature, no per-call drift. Same posts plus same config always produce the same output, and the raw items ship alongside the aggregates. - **Built for agents** — a Standby MCP server speaking the Streamable HTTP transport, so any MCP client can connect and call it directly. - **Cheap and fast** — one HN payload per query, cached and shared across the query tools. No LLM cost in the analysis path. ## How to use Hacker News Developer Sentiment 1. **Deploy the Actor** — run it in Standby mode on Apify. It boots with no input required and returns data immediately. 2. **Connect your MCP client** to the Standby endpoint (`https://<your-standby-url>/mcp`) using the Streamable HTTP transport. 3. **Call `get_sentiment_summary`** with a `query` (a brand, tool, or topic) for the headline read. 4. **Drill in** with `search_mentions`, `get_feature_requests`, or `get_trending_discussions` when you need detail. The query tools share the same cached payload per query. ## The four tools `query` is a brand/tool/topic string (e.g. `"prisma"`, `"tailwind"`). `channels`, `windowDays`, and `limit` are optional and fall back to the Actor's configured defaults. `channels` selects HN post types — `story`, `ask_hn`, `show_hn`, `comment` — and defaults to all (stories **and** comments, since HN opinion lives in the comments). ### `get_sentiment_summary` — the headline read Aggregate sentiment, mention volume, top themes, and representative samples for a query. Input: `query` (required), `channels?`, `windowDays?`. ```jsonc { "query": "prisma", "channelsQueried": ["story", "ask_hn", "show_hn", "comment"], "windowDays": 30, "mentionVolume": 52, "netSentiment": 0.18, "sentimentLabel": "Positive", "breakdown": { "positive": 29, "neutral": 16, "negative": 7 }, "topThemes": [ { "term": "type safety", "count": 15 }, { "term": "migrations", "count": 12 }, { "term": "query performance", "count": 7 } ], "topSamples": [ { "title": "Prisma 6 made our migrations painless", "channel": "story", "score": 312, "url": "https://news.ycombinator.com/item?id=48800001", "createdAt": "2026-06-02T14:11:00.000Z", "sentiment": "Positive" } ], "fetchedAt": "2026-07-14T00:00:00.000Z" } ``` `netSentiment` is the mean compound score in `[-1, 1]`; `sentimentLabel` buckets it at ±0.05. `topSamples` are the three highest-engagement items. `topThemes` are stopword- and query-term-filtered unigrams/bigrams ranked by frequency. ### `search_mentions` — matched items with per-item sentiment Stories and comments mentioning the query, each scored, ranked by score then recency. Input: `query` (required), `channels?`, `windowDays?`, `limit?`. ```jsonc { "query": "prisma", "channelsQueried": ["story", "comment"], "windowDays": 30, "count": 2, "items": [ { "id": "48800001", "title": "Prisma 6 made our migrations painless", "excerpt": "We migrated a 40-table schema with zero downtime and the typed client caught three bugs before...", "channel": "story", "score": 312, "numComments": 88, "url": "https://news.ycombinator.com/item?id=48800001", "createdAt": "2026-06-02T14:11:00.000Z", "sentiment": "Positive", "sentimentScore": 0.74 } ], "fetchedAt": "2026-07-14T00:00:00.000Z" } ``` `excerpt` is the item text truncated to 280 characters. `sentimentScore` is the per-item compound in `[-1, 1]`. Comments have an empty `title` and `numComments: 0`. ### `get_feature_requests` — requests and pain points, grouped Items that voice a feature request, wish, gap, roadmap question, or pain point about the query — grouped by cue category. Input: `query` (required), `channels?`, `windowDays?`, `limit?`. ```jsonc { "query": "prisma", "channelsQueried": ["story", "ask_hn", "comment"], "windowDays": 30, "count": 3, "items": [ { "id": "48800042", "title": "Ask HN: does Prisma support deep JSON filtering yet?", "excerpt": "Please add deep JSON path filters to the query API.", "cueCategory": "request", "channel": "ask_hn", "score": 54, "url": "https://news.ycombinator.com/item?id=48800042", "createdAt": "2026-06-09T18:30:00.000Z" } ], "byCategory": { "wish": 1, "missing": 1, "request": 1, "plans": 0, "painpoint": 0 }, "fetchedAt": "2026-07-14T00:00:00.000Z" } ``` `cueCategory` is one of `request` / `wish` / `missing` / `plans` / `painpoint` (matched by priority in that order). `excerpt` is the matching sentence. `byCategory` totals always sum to `count`. ### `get_trending_discussions` — highest-engagement stories (no query) Top Hacker News stories by engagement, no query required. Input: `windowDays?`, `limit?`. ```jsonc { "channelsQueried": ["story"], "windowDays": 7, "count": 2, "items": [ { "id": "48841676", "title": "Postgres rewritten in Rust, now passing 100% of the regression tests", "channel": "story", "score": 980, "numComments": 240, "engagement": 1220, "url": "https://news.ycombinator.com/item?id=48841676", "createdAt": "2026-07-12T09:02:00.000Z", "sentiment": "Neutral" } ], "fetchedAt": "2026-07-14T00:00:00.000Z" } ``` `engagement` is `score + numComments`; items are ranked by it descending. Trending is story-level (comments excluded — they have no thread engagement). ## Input The server boots with **no input required** and returns data immediately — the Hacker News (Algolia) API needs no credentials. All fields below are optional. | Field | Type | Default | Description | |---|---|---|---| | `defaultChannels` | array | `["story","ask_hn","show_hn","comment"]` | HN post types searched when a tool call doesn't specify its own. Allowed: `story`, `ask_hn`, `show_hn`, `comment`. | | `windowDays` | integer | `30` | Default trailing look-back window in days (1–90). | | `cacheTtlMinutes` | integer | `20` | How long a query's fetched payload is reused before refetching (1–240). | | `maxItems` | integer | `100` | Upper bound on items per call; per-call `limit` is clamped to this (10–250). | ## Output Each tool returns a single structured JSON object (shown above per tool). Responses are deterministic — the same posts plus the same config always produce the same result. Every response includes a `fetchedAt` timestamp reflecting when the underlying Hacker News payload was pulled; a cached read returns the same payload (and timestamp) for that TTL window. ## Data fields reference | Field | What it tells you | |---|---| | `netSentiment` | Mean compound sentiment across matched items, in `[-1, 1]` | | `sentimentLabel` | Positive / Neutral / Negative, bucketed at ±0.05 | | `breakdown` | Count of positive / neutral / negative items | | `mentionVolume` | Number of matched items in the window | | `topThemes` | Most frequent unigrams/bigrams (stopword- and query-term-filtered) | | `topSamples` | The three highest-engagement matched items | | `sentimentScore` | Per-item compound sentiment, in `[-1, 1]` | | `channel` | HN post type: `story` / `ask_hn` / `show_hn` / `comment` | | `cueCategory` | Feature-request category: request / wish / missing / plans / painpoint | | `byCategory` | Count of feature-request items per cue category | | `engagement` | `score + numComments` for a story (trending rank key) | | `fetchedAt` | When the underlying Hacker News payload was fetched | ## Pricing This Actor runs in Standby mode and is billed for the compute used while warm and serving requests. Reads are lightweight — each query's HN data is fetched once, cached (default 20 minutes), and shared across the query tools, so calling several tools for the same query costs a single upstream fetch. There is no LLM in the analysis path, so no per-call model cost, and the Hacker News API is free. ## Tips - **Start with `get_sentiment_summary`** for the headline, then call a detail tool only when you need it — the query tools share the same cached payload. - **Narrow `channels`** (e.g. `["ask_hn"]`) to focus on solicitation threads, or drop `comment` to look only at story-level signal. - **Raise `cacheTtlMinutes`** when batching many queries; lower it when you need fresh reads. - **Widen `windowDays`** (up to 90) to capture a steadier baseline. ## How it works 1. An MCP client sends a tool call (e.g. `get_sentiment_summary`) with a `query`. 2. The server checks an in-memory LRU + TTL cache. On a miss, it makes one request to the open Hacker News (Algolia) search API across the selected channels and caches the raw items. 3. HN text (which arrives HTML-ish) is stripped of tags and decoded, then pure analysis functions score sentiment (lexicon + negation/intensifier/emoji rules), aggregate it, extract themes, mine feature-request cues, and rank — with no further network calls. 4. The matched window is filtered to the exact day count, and the tool's structured result — aggregates plus raw items plus `fetchedAt` — is returned to the MCP client. All analysis is deterministic and clock-injected (testable without network). Transient errors (network blips, 5xx) are retried with bounded backoff; rate limits (429) surface a `resetAt` for the caller to handle. ## Works well with Part of a small slate of agent-focused Apify Actors: - **[GitHub Repo Intelligence MCP](https://apify.com/joeslade/github-repo-intelligence-mcp)** — opinionated maintainability verdicts for any GitHub repo across five MCP tools. - **[SERP Topic Gap Monitor](https://apify.com/joeslade/serp-topic-gap-monitor)** — finds topic gaps in search-result coverage and produces scored gap reports. - **[Docs MCP Server](https://apify.com/joeslade/docs-mcp-server-starter)** — an MCP server giving AI assistants queryable access to framework documentation. ## FAQ and support **Do I need an API key or account?** No. The Hacker News (Algolia) search API is open — the Actor boots and returns data with zero setup. **Is the sentiment from an LLM?** No. Sentiment is a transparent lexicon-plus-rules scorer (negation, intensifiers, emoji, VADER-style normalization). There is no model in the analysis layer, which is what makes every call deterministic and auditable. **Does it read comments or just stories?** Both, by default — HN opinion lives in the comments. Use the `channels` argument to narrow to specific post types. **Is the output stable?** Yes. Same items plus same config always yield the same aggregates, and the raw matched items are returned alongside so you can audit every number. **It returned a rate-limit message — what do I do?** Hacker News rate limits (429) surface a `resetAt` timestamp; retry after it. Raising `cacheTtlMinutes` reduces upstream calls when batching. **Found a bug or want a feature?** Open an issue on the Actor's **Issues** tab. Custom variations are available on request. ## Acknowledgements The sentiment lexicon is expanded from [VADER](https://github.com/cjhutto/vaderSentiment) (Hutto, C.J. & Gilbert, E.E., 2014), used under its MIT license. Hacker News data via the [Algolia HN Search API](https://hn.algolia.com/api). ## License Copyright © 2026 Joe Slade. Licensed under the [GNU Affero General Public License v3.0](LICENSE) (AGPL-3.0). You're free to use, study, modify, and self-host this software; if you run a modified version as a network service, the AGPL requires you to offer your modified source to its users under the same license. For a commercial license not subject to the AGPL's network-copyleft, contact the author.