# GitHub Org Intelligence (MCP) (`scrap_them_all/github-org-intelligence-mcp`) Actor MCP-friendly Apify Actor returning a structured GitHub intelligence card. Three modes: org\_overview, repo\_intelligence, user\_profile. Languages, stars, activity classification, top contributors, releases, LLM-ready markdown report. Public GitHub REST API. - **URL**: https://apify.com/scrap\_them\_all/github-org-intelligence-mcp.md - **Developed by:** [scrap\_them\_all](https://apify.com/scrap_them_all) (community) - **Categories:** AI, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 github intelligence cards 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 ## GitHub Org Intelligence (MCP) ### What does GitHub Org Intelligence do? Pass a GitHub org, repo, or user and get back a structured intelligence card: language mix, stars, activity level, top repos, top contributors, latest releases, recent commit cadence, and a deterministic LLM-ready markdown report. No LLM calls inside, fully reproducible. Designed to be **called from an MCP agent loop** (Claude Code, Cursor, Continue, custom agents) where the agent needs to evaluate a GitHub presence in one shot. > Built on the public GitHub REST API. Optional GitHub PAT (free, no scopes needed) lifts the rate limit from 60/h to 5000/h. ### Why use it from an AI agent? - **One call, full picture** - org metadata + top 15 repos + language mix + activity classification + LLM-ready report. - **Deterministic** - same input today, same output tomorrow (no LLM jitter). - **Tight schema** - agents don't waste tokens parsing the GitHub REST output. - **Activity-aware** - the actor classifies dormant / maintained / active / very_active per repo and per org so the agent knows whether a project is alive. - **Standby-mode HTTP server** - true MCP-over-HTTP endpoint with one-shot fallback. ### Modes #### A. Org Overview Analyze an organization or user account: portfolio summary, language mix, top repos. ```json { "mode": "org_overview", "org": "anthropics", "topReposLimit": 15 } ```` #### B. Repo Intelligence Single repository deep dive: language byte breakdown, top contributors, releases, recent commit frequency. ```json { "mode": "repo_intelligence", "repo": "anthropics/claude-code" } ``` #### C. User Profile Developer profile: account metadata + their public repos + topic interests. ```json { "mode": "user_profile", "username": "torvalds" } ``` ### Output shape (all modes) ```json { "meta": { "mode": "org_overview", "query": "anthropics", "scrapedAt": "2026-05-08T15:00:00Z", "primaryLanguage": "TypeScript", "totalStars": 32450, "publicRepos": 47, "activityLevel": "very_active" }, "intelligence": { "primaryLanguage": "TypeScript", "languageMix": [ { "language": "TypeScript", "repoCount": 18, "percent": 38.3 } ], "totalStars": 32450, "publicRepos": 47, "activityLevel": "very_active", "activeRepos90d": 28, "archivedRepos": 5, "forks": 3, "medianStarsPerRepo": 47, "licensesUsed": ["MIT", "Apache-2.0"], "topRepos": [ { "name": "claude-code", "fullName": "anthropics/claude-code", "url": "https://github.com/anthropics/claude-code", "stars": 12340, "language": "TypeScript", "activityLevel": "very_active" } ] }, "org": { "login": "anthropics", "name": "Anthropic", "description": "...", "type": "Organization" }, "llm_ready": { "summary": "Anthropic on GitHub: 47 public repos (28 active in 90 days), 32450 total stars, primarily TypeScript...", "markdown_report": "### GitHub Org Intelligence Report\n..." } } ``` ### Authentication The Actor calls GitHub's public REST API. Two auth modes: #### Anonymous (default) 60 req/hour. Fine for low-volume agent use (1-2 calls / agent session). No setup required. #### BYOK GitHub PAT (optional, recommended for higher volume) Lift the rate limit to 5000/hour by supplying a Personal Access Token. No special scopes needed - even a no-scope PAT works for public data access. Two ways to provide it: 1. **Per-call** via `githubToken` input field (encrypted by Apify). 2. **Operator-scoped** via `GITHUB_TOKEN` env var on the Actor (Apify Console -> Settings -> Environment variables). Generate a PAT at `https://github.com/settings/tokens?type=beta` (fine-grained, public-repo read). ### Pricing (PPE) | Mode | Price per call | | --- | --- | | All modes | $0.001 actor start + $0.005 intelligence card = **$0.006/call** | ### Activity classification | Level | Criterion | | --- | --- | | `very_active` | Last push <= 7 days ago | | `active` | Last push 8-30 days ago | | `maintained` | Last push 31-180 days ago | | `dormant` | Last push > 180 days OR archived | For org/user mode, the org-level level reflects the distribution across the org's repos (e.g. `very_active` if 30%+ of live repos pushed in the last week). ### Calling from an MCP agent The Apify MCP server (`mcp.apify.com`) exposes this Actor as the tool `call-actor` with name `github-org-intelligence-mcp`. Typical prompt: *"Use github-org-intelligence-mcp to analyze the anthropics org's GitHub portfolio."* Direct Standby HTTP integration: ```bash curl -X POST -H 'Content-Type: application/json' \ -H "Authorization: Bearer $APIFY_TOKEN" \ -d '{"mode":"repo_intelligence","repo":"vercel/next.js"}' \ https:// ``` ### Limits - 60 req/h anonymous, 5000/h with PAT. The actor consumes 1-3 GitHub API calls per intelligence card depending on mode. - Repo language byte breakdown is GitHub's own classification (occasionally noisy on monorepos with vendored deps). - Contributors list caps at 100 per page; very large repos may have more total contributors than reported. ### Sources, freshness, legality - All data comes from GitHub's public REST API. No HTML scraping, no auth required for public data. - The Actor exposes only public GitHub content. Private repos / orgs / users not accessible. - User-Agent identifies the Actor with a descriptive string per GitHub API best practices. # Actor input Schema ## `mode` (type: `string`): Pick one of the three intelligence modes. ## `org` (type: `string`): Required for org\_overview. GitHub org/login (e.g. 'anthropics', 'vercel', 'apify'). ## `repo` (type: `string`): Required for repo\_intelligence. Full repo identifier 'owner/name' (e.g. 'anthropics/claude-code') or full URL ('https://github.com/anthropics/claude-code'). ## `username` (type: `string`): Required for user\_profile. GitHub user login without '@' (e.g. 'torvalds', 'gaearon'). ## `topReposLimit` (type: `integer`): Max repos to fetch when listing org/user repos. Caps at 50. ## `githubToken` (type: `string`): Optional. Bring your own GitHub PAT to lift the rate limit from 60 req/h (anonymous) to 5000 req/h. No special scopes required - a 'public\_repo' read-only PAT is enough. Generate one at https://github.com/settings/tokens. ## Actor input object example ```json { "mode": "org_overview", "topReposLimit": 15 } ``` # Actor output Schema ## `card` (type: `string`): No description ## `dataset` (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("scrap_them_all/github-org-intelligence-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("scrap_them_all/github-org-intelligence-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 scrap_them_all/github-org-intelligence-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrap_them_all/github-org-intelligence-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GitHub Org Intelligence (MCP)", "description": "MCP-friendly Apify Actor returning a structured GitHub intelligence card. Three modes: org_overview, repo_intelligence, user_profile. Languages, stars, activity classification, top contributors, releases, LLM-ready markdown report. Public GitHub REST API.", "version": "0.1", "x-build-id": "KG897xPBBdReNTnsp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrap_them_all~github-org-intelligence-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrap_them_all-github-org-intelligence-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrap_them_all~github-org-intelligence-mcp/runs": { "post": { "operationId": "runs-sync-scrap_them_all-github-org-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrap_them_all~github-org-intelligence-mcp/run-sync": { "post": { "operationId": "run-sync-scrap_them_all-github-org-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "org_overview", "repo_intelligence", "user_profile" ], "type": "string", "description": "Pick one of the three intelligence modes.", "default": "org_overview" }, "org": { "title": "Organization (mode A)", "type": "string", "description": "Required for org_overview. GitHub org/login (e.g. 'anthropics', 'vercel', 'apify')." }, "repo": { "title": "Repository owner/name (mode B)", "type": "string", "description": "Required for repo_intelligence. Full repo identifier 'owner/name' (e.g. 'anthropics/claude-code') or full URL ('https://github.com/anthropics/claude-code')." }, "username": { "title": "Username (mode C)", "type": "string", "description": "Required for user_profile. GitHub user login without '@' (e.g. 'torvalds', 'gaearon')." }, "topReposLimit": { "title": "Top repos to include", "minimum": 5, "maximum": 50, "type": "integer", "description": "Max repos to fetch when listing org/user repos. Caps at 50.", "default": 15 }, "githubToken": { "title": "GitHub Personal Access Token (BYOK, optional)", "type": "string", "description": "Optional. Bring your own GitHub PAT to lift the rate limit from 60 req/h (anonymous) to 5000 req/h. No special scopes required - a 'public_repo' read-only PAT is enough. Generate one at https://github.com/settings/tokens." } } }, "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 } } } } } } } } } } ```