# Startup Intelligence MCP Server (`ritindersingh/startup-intelligence-mcp`) Actor MCP server for AI agents. Given a company name or domain, returns funding stage, team size, tech stack, news, and hiring signals in one tool call. - **URL**: https://apify.com/ritindersingh/startup-intelligence-mcp.md - **Developed by:** [Ritinder Singh](https://apify.com/ritindersingh) (community) - **Categories:** MCP servers, Lead generation, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start 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 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 ## Startup Intelligence MCP Server Researching a company manually — funding round, team size, tech stack, open roles, recent news — takes 20–30 minutes across five different tabs. This MCP server does it in one tool call, in under 15 seconds. Connect it to Claude Desktop, Cursor, or any MCP-compatible AI agent and ask: *"What's the deal with Acme Corp?"* — you get a structured JSON report with everything you need. --- ### MCP Setup (Claude Desktop) Add this to your `claude_desktop_config.json`: ```json { "mcpServers": { "startup-intelligence": { "url": "https://ritindersingh--startup-intelligence-mcp.apify.actor/mcp", "headers": { "Authorization": "Bearer YOUR_APIFY_API_TOKEN" } } } } ```` Replace `YOUR_APIFY_API_TOKEN` with your API token from [apify.com/account/integrations](https://apify.com/account/integrations). *** ### MCP Setup (Cursor) In Cursor settings → MCP Servers → Add: ```json { "name": "startup-intelligence", "url": "https://ritindersingh--startup-intelligence-mcp.apify.actor/mcp", "headers": { "Authorization": "Bearer YOUR_APIFY_API_TOKEN" } } ``` *** ### Example Tool Call **Input:** ```json { "company_name": "Linear", "domain": "linear.app" } ``` **Output:** ```json { "company": "Linear", "domain": "linear.app", "fundingStage": "Series B", "totalFunding": "$52M", "lastFundingDate": "2022-07-26", "employeeCount": "51-200", "employeeSource": "linkedin", "techStack": ["React", "Next.js", "Cloudflare", "Stripe", "Segment"], "openJobsCount": 8, "careersPageUrl": "https://linear.app/careers", "hiringSignal": "growing", "recentNews": [ { "title": "Linear raises $35M Series B to build the future of project management", "url": "https://techcrunch.com/...", "date": "Mon, 26 Jul 2022 07:00:00 GMT", "source": "TechCrunch" } ], "crunchbaseUrl": "https://www.crunchbase.com/organization/linear", "linkedinUrl": "https://www.linkedin.com/company/linear2/about/", "scrapedAt": "2026-05-19T14:30:00.000Z", "errors": [] } ``` *** ### Data Sources | Field | Source | |-------|--------| | Funding stage, amount, date | Crunchbase public profile | | Employee count | LinkedIn public company page → Crunchbase fallback | | Tech stack | Company homepage HTML (script tags, meta tags, headers) | | Open jobs | Greenhouse API → Lever API → Direct careers page | | Recent news | Google News RSS feed | *** ### Pricing **$0.25 per company lookup. First 3 lookups free.** Charged after data is delivered — you only pay for successful results. *** ### Limitations - **LinkedIn** frequently blocks scrapers; employee count may fall back to Crunchbase or be unavailable - **Crunchbase** shows public data only — financials behind their paywall are not included - **Job counts** from direct careers pages are estimates based on link patterns; ATS integrations (Greenhouse, Lever) are more accurate - Tech stack detection works best for companies with public-facing marketing sites; B2B/enterprise tools may show fewer signals *** ### Use Cases - **VC deal screening** — quickly assess funding history, team size, and hiring velocity before a first call - **B2B sales prospecting** — identify tech stack before outreach, tailor your pitch to their existing tools - **Recruiter research** — gauge company growth signals and active hiring before reaching out to candidates - **Competitive intelligence** — monitor competitors' hiring signals, tech choices, and recent press # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ritindersingh/startup-intelligence-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("ritindersingh/startup-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 ritindersingh/startup-intelligence-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ritindersingh/startup-intelligence-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Startup Intelligence MCP Server", "description": "MCP server for AI agents. Given a company name or domain, returns funding stage, team size, tech stack, news, and hiring signals in one tool call.", "version": "0.1", "x-build-id": "frJAfpBPJetu9RGvj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ritindersingh~startup-intelligence-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ritindersingh-startup-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ritindersingh~startup-intelligence-mcp/runs": { "post": { "operationId": "runs-sync-ritindersingh-startup-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ritindersingh~startup-intelligence-mcp/run-sync": { "post": { "operationId": "run-sync-ritindersingh-startup-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "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 } } } } } } } } } } ```