# MCP Server Trend Radar (`ianymu/mcp-server-trend-radar`) Actor Daily/weekly radar of the fastest-growing MCP (Model Context Protocol) servers on GitHub. Computes star velocity over a configurable window (default 30d) and ranks repos by momentum, not just total stars. Complements mcp-server-catalog which scores quality at a point in time. - **URL**: https://apify.com/ianymu/mcp-server-trend-radar.md - **Developed by:** [Yanlong Mu](https://apify.com/ianymu) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 **MCP Server Trend Radar** is a daily / weekly radar of the **fastest-growing Model Context Protocol (MCP) servers** on GitHub. Instead of ranking by absolute star count like a static catalog, it measures **momentum**: how many stars each MCP server gained in the last 7 / 30 / 90 days. Find the next breakout MCP integration before it's saturated. This Actor was built by [Ian Mu](https://github.com/ianymu) as Actor #12 in a 100-actor portfolio. It pairs naturally with the companion [mcp-server-catalog](https://apify.com/) Actor (point-in-time quality scoring) and with the verification harness at [github.com/ianymu/claude-verify-before-stop](https://github.com/ianymu/claude-verify-before-stop). ### What does MCP Server Trend Radar do? It scans GitHub for repos tagged with the `mcp` and `model-context-protocol` topics that have been actively pushed inside your chosen window, then samples each repo's `stargazers` timeline (via the paginated `application/vnd.github.v3.star+json` accept header) to estimate **stars gained in the last N days**. It computes `starsPerDay` and a normalized `starsVelocityScore` (0-100, anchored to the top mover in the run), then ranks the results. Run it on the **Apify platform** to get free scheduling (daily 09:00 UTC works well), webhook delivery into Slack / Notion / your newsletter pipeline, dataset versioning, and a managed proxy pool if you ever need it. You can hit the dataset via the Apify API and pipe it straight into a curated newsletter or an investor dashboard. ### Why use MCP Server Trend Radar? - **Founders & devs** spot new MCP integrations early, before everyone wraps them. - **Investors** track which MCP ecosystem niches (databases, agents, devops, design) are heating up week-over-week. - **Newsletter writers / curators** auto-generate a "this week in MCP" section without hand-curating GitHub. - **MCP server authors** see who's beating them on momentum and study what's working. Unlike `mcp-server-catalog`, which scores quality at a single point in time (stars + recency + license + activity), this Actor is purely a **momentum signal**. A repo with 50 stars and +40 stars last week will outrank a 5,000-star repo that's been flat. Run both Actors weekly and compare quality vs. velocity to find rising stars worth tracking. ### How to use MCP Server Trend Radar 1. Click **Try for free** in the Apify Console. 2. Set `windowDays` (default 30) — drop to 7 for a weekly radar, or push to 90 for a quarterly view. 3. Set `maxResults` (default 30). 4. Optionally extend `topicFilter` with `["mcp", "model-context-protocol", "llm-tools"]` if you want broader coverage. 5. Click **Start** and let the Actor run. A first run on default settings completes in roughly 60-180 seconds depending on GitHub rate limits. 6. Open the **Dataset** tab to see ranked results, or download the dataset as JSON / CSV / Excel. A Markdown leaderboard is also written to the key-value store as `mcp-trend-radar.md`. ### Input | Field | Type | Default | Description | |---|---|---|---| | `windowDays` | integer | `30` | Window over which to measure star velocity (1-365). | | `maxResults` | integer | `30` | Maximum repos to return (1-200). | | `topicFilter` | string[] | `["mcp", "model-context-protocol"]` | GitHub topics that define "an MCP server" for the radar. | Example input JSON: ```json { "windowDays": 7, "maxResults": 25, "topicFilter": ["mcp", "model-context-protocol"] } ```` ### Output Each row in the dataset looks like: ```json { "repo": "supabase/mcp-server", "url": "https://github.com/supabase/mcp-server", "currentStars": 1250, "starsGainedInWindow": 380, "starsPerDay": 12.7, "starsVelocityScore": 95, "description": "Supabase Model Context Protocol server", "language": "TypeScript", "lastPushed": "2026-05-18T10:23:00Z", "windowDays": 30 } ``` You can download the dataset in various formats such as JSON, HTML, CSV, or Excel. Rows are sorted by `starsVelocityScore` descending. ### Data table | Field | Meaning | |---|---| | `repo` | `owner/name` of the GitHub repo. | | `url` | Direct link to the repo. | | `currentStars` | Star count at the moment the Actor ran. | | `starsGainedInWindow` | Estimated stars gained in the last `windowDays` days. | | `starsPerDay` | `starsGainedInWindow / windowDays`, rounded to one decimal. | | `starsVelocityScore` | 0-100, normalized against the fastest grower in this run. | | `description` | GitHub repo description. | | `language` | Primary language reported by GitHub. | | `lastPushed` | Last push timestamp. | | `windowDays` | The window setting used for this row (useful when combining runs). | ### Pricing / Cost estimation The Actor is lightweight: a typical 30-result run on default settings uses well under one compute unit on the Apify free tier. Most of the work is GitHub API calls; the stargazer-sampling binary search caps probes at six pages per repo, so the worst case for `maxResults=30` is roughly 60 search calls + 360 stargazer calls, comfortably inside the unauthenticated GitHub rate limit when run hourly or less, and totally fine with a token at any cadence. If you set `GITHUB_TOKEN` (or `GH_TOKEN`) in the Actor's environment variables, the radar will use it automatically and lift the GitHub rate limit from 60 to 5,000 requests per hour. ### Tips and advanced options - **Weekly newsletter**: `windowDays: 7, maxResults: 10`, schedule daily at 09:00 UTC, and pipe the output into your newsletter via Apify webhook. - **Investor scan**: `windowDays: 30, maxResults: 100`, schedule weekly, and join with [mcp-server-catalog](https://apify.com/) output to compare velocity vs. quality. - **Niche radar**: extend `topicFilter` with niche topics (e.g. `["mcp", "claude-mcp", "openai-mcp"]`) to slice the ecosystem. - **Speed**: provide a `GITHUB_TOKEN` env var to skip rate-limit pauses entirely. ### FAQ, disclaimers, and support **Is the star velocity exact?** It's an estimate. The GitHub stargazers API is paginated and per-page sampled, so very large repos (>50k stars) are approximated via binary search rather than fully scanned. The accuracy is typically within +/-10 stars over a 30-day window for repos under 10k stars — more than enough to rank momentum. **Why might my favorite MCP server be missing?** It needs the `mcp` or `model-context-protocol` GitHub topic to appear in the candidate pool. Open a PR on the upstream repo to add the topic, or extend `topicFilter` to include topics the project actually uses. **Legal**: this Actor only reads public GitHub data via the official GitHub REST API. No scraping of behind-auth or rate-limit-evasion behavior. **Issues / feature requests**: open an issue against this Actor on Apify, or ping [@ianymu](https://github.com/ianymu) on GitHub. Custom variants (weekly digest delivery, Notion sync, multi-ecosystem radars for LangChain / LlamaIndex / agent frameworks) are available on request. Built by Ian Mu. Quality verification harness: [github.com/ianymu/claude-verify-before-stop](https://github.com/ianymu/claude-verify-before-stop). # Actor input Schema ## `windowDays` (type: `integer`): Measure star velocity over this many days. 7 = weekly radar, 30 = monthly radar. ## `maxResults` (type: `integer`): Maximum number of fastest-growing MCP servers to return. ## `topicFilter` (type: `array`): GitHub topic tags used to find candidate MCP server repos. Add or remove topics to broaden/narrow the radar. ## Actor input object example ```json { "windowDays": 30, "maxResults": 30, "topicFilter": [ "mcp", "model-context-protocol" ] } ``` # Actor output Schema ## `results` (type: `string`): No description # 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("ianymu/mcp-server-trend-radar").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("ianymu/mcp-server-trend-radar").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 ianymu/mcp-server-trend-radar --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ianymu/mcp-server-trend-radar", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Server Trend Radar", "description": "Daily/weekly radar of the fastest-growing MCP (Model Context Protocol) servers on GitHub. Computes star velocity over a configurable window (default 30d) and ranks repos by momentum, not just total stars. Complements mcp-server-catalog which scores quality at a point in time.", "version": "0.1", "x-build-id": "utNAVbPcVcqK8bPOP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ianymu~mcp-server-trend-radar/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ianymu-mcp-server-trend-radar", "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/ianymu~mcp-server-trend-radar/runs": { "post": { "operationId": "runs-sync-ianymu-mcp-server-trend-radar", "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/ianymu~mcp-server-trend-radar/run-sync": { "post": { "operationId": "run-sync-ianymu-mcp-server-trend-radar", "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": { "windowDays": { "title": "Window in days", "minimum": 1, "maximum": 365, "type": "integer", "description": "Measure star velocity over this many days. 7 = weekly radar, 30 = monthly radar.", "default": 30 }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of fastest-growing MCP servers to return.", "default": 30 }, "topicFilter": { "title": "GitHub topics to scan", "type": "array", "description": "GitHub topic tags used to find candidate MCP server repos. Add or remove topics to broaden/narrow the radar.", "default": [ "mcp", "model-context-protocol" ], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```