# Dev Radar MCP Server (`ef12/dev-radar-mcp`) Actor MCP server that gives AI agents cross-source developer ecosystem intelligence. Combines GitHub Trending, Hacker News, Stack Overflow, Product Hunt, Dev.to, GitLab, and NPM into synthesized reports with trend detection, topic clustering, and cross-source correlation. - **URL**: https://apify.com/ef12/dev-radar-mcp.md - **Developed by:** [Daniel Wilson](https://apify.com/ef12) (community) - **Categories:** AI, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Dev Radar MCP Server An MCP server that gives AI agents cross-source developer ecosystem intelligence. Connect once to Claude Desktop, Cursor, or any MCP-compatible client, and your AI agent can analyze trends, discover emerging projects, and compare ecosystems across GitHub, Hacker News, Stack Overflow, Product Hunt, Dev.to, GitLab, and NPM. ### What does Dev Radar MCP Server do? This MCP server exposes **5 intelligence tools** that AI agents can discover and call. Each tool synthesizes data from multiple sources into an intelligence report — not raw data, but cross-source correlation, velocity tracking, and topic clustering. The difference between this and a raw scraper: when you ask "what's trending in Python?", a raw scraper gives you 25 repos. Dev Radar gives you topic clusters, cross-source correlations (which repos also appeared on HN, Dev.to, and Product Hunt), velocity trends (compared to last week's snapshot), and a qualitative assessment. ### Why use Dev Radar MCP Server? - **7 data sources, one connection** — GitHub Trending, Hacker News, Stack Overflow, Product Hunt, Dev.to, GitLab, and NPM in a single MCP server - **Cross-source correlation** — projects that appear across multiple platforms get higher signal scores - **Historical velocity tracking** — weekly snapshots stored in KV stores enable week-over-week comparison - **Topic clustering** — repos and discussions grouped by technology theme (AI agents, web frameworks, devtools, etc.) - **Intelligence, not data** — synthesized reports with assessments, not raw JSON dumps - **3 free intelligence reports per call** — try each tool without paying ### Tools | Tool | What it returns | Price per call | |------|----------------|----------------| | `analyze_ecosystem(languages, timeframe)` | Full ecosystem health report with topic clusters, cross-source correlations, velocity metrics, and top trending per source | $0.25 | | `discover_emerging_projects(keywords, min_signal_score, max_total_stars)` | Projects with high cross-source signal but low total stars — early breakout detection | $0.15 | | `get_intelligence_report(topic)` | Comprehensive report on any developer topic: adoption signals, key projects, discussions, velocity, and qualitative assessment | $0.50 | | `detect_topic_shifts(timeframe)` | Rising and falling topic trends — which ecosystems are gaining or losing momentum | $0.15 | | `compare_ecosystems(languages)` | Side-by-side language comparison: trending repos, topic overlap, HN/Dev.to mentions | $0.20 | ### How to connect #### Claude Desktop Add to your MCP config: ```json { "mcpServers": { "dev-radar": { "url": "https://actors.apify.com/ef12~dev-radar-mcp/mcp" } } } ```` #### Cursor Add to `.cursor/mcp.json`: ```json { "mcpServers": { "dev-radar": { "url": "https://actors.apify.com/ef12~dev-radar-mcp/mcp" } } } ``` #### Example prompts for your AI agent - "Analyze the Python ecosystem this week — what are the top trends?" - "Discover emerging AI agent projects that haven't hit 10K stars yet" - "Give me an intelligence report on the MCP ecosystem" - "Detect topic shifts — what's rising and falling in the dev world?" - "Compare Python, JavaScript, and Rust ecosystems side by side" ### Use Cases #### How to track developer ecosystem trends weekly Connect the MCP server to your AI agent and schedule a weekly conversation: "Analyze the Python and JavaScript ecosystems this week." Your agent calls `analyze_ecosystem`, synthesizes the report, and summarizes the key changes. #### How to discover breakout projects before they go mainstream Use `discover_emerging_projects` with keywords like "ai,agent,llm" and a low `max_total_stars` threshold. Projects that show cross-source signal but haven't hit mainstream star counts are your early breakout candidates. #### How to monitor the MCP ecosystem Call `get_intelligence_report(topic="mcp")` to get a full synthesized report: how many repos are trending, how many HN discussions, Product Hunt launches, Dev.to articles, NPM packages, and a qualitative assessment of ecosystem momentum. ### Pricing **Pay-Per-Event** with a free tier: - 3 free intelligence reports per tool call - $0.15-$0.50 per report after the free tier, depending on the tool - `apify-actor-start` at $0.00005 **Cost example:** A weekly ecosystem analysis with 2 follow-up tool calls = 2 paid reports = ~$0.50 per week ### How it works under the hood Each tool call triggers the intelligence engine: 1. Fetches current data from multiple Apify actors in parallel (GitHub, HN, SO, PH, Dev.to, GitLab, NPM) 2. Loads previous snapshots from Apify KV stores for historical comparison 3. Computes cross-source correlations — which projects appear on multiple platforms 4. Clusters items by topic using keyword matching 5. Calculates velocity metrics — week-over-week percentage change 6. Generates a qualitative assessment based on signal volume and cross-source presence 7. Saves current snapshot for next call's velocity comparison The raw scrapers are invisible infrastructure. The intelligence layer is the product. ### FAQ #### Do I need an Apify account? Yes — the server calls Apify actors under the hood. You need an Apify account with API token set in the MCP connection config. Free tier includes $5 in monthly credits. #### How is this different from the individual scrapers? Individual scrapers return raw data (25 repos, 30 HN stories). This server synthesizes across all sources — a project that appears on GitHub, HN, and Dev.to gets a much higher signal score than one that only appears on GitHub. You get a report, not a data dump. #### How fast are the tool calls? Most calls take 10-30 seconds since they fetch from multiple sources in parallel. The intelligence processing (clustering, correlation, velocity) runs in milliseconds once data is fetched. #### Can I schedule automated intelligence reports? Yes — use the Apify API to call individual tools on a schedule, or have your AI agent run a weekly analysis conversation. ### Disclaimer > This Actor is designed for ethical web scraping. It only extracts publicly available data and does not access private user information. You should be aware that your results may contain personal data. Personal data is protected by GDPR in the European Union and by other regulations worldwide. # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ef12/dev-radar-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("ef12/dev-radar-mcp").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 '{}' | apify call ef12/dev-radar-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ef12/dev-radar-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dev Radar MCP Server", "description": "MCP server that gives AI agents cross-source developer ecosystem intelligence. Combines GitHub Trending, Hacker News, Stack Overflow, Product Hunt, Dev.to, GitLab, and NPM into synthesized reports with trend detection, topic clustering, and cross-source correlation.", "version": "0.0", "x-build-id": "QunTp1jWFC1Bp3nnl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ef12~dev-radar-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ef12-dev-radar-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ef12~dev-radar-mcp/runs": { "post": { "operationId": "runs-sync-ef12-dev-radar-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ef12~dev-radar-mcp/run-sync": { "post": { "operationId": "run-sync-ef12-dev-radar-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```