# B2B Research MCP Server - Email Finder, Tech Stack, Jobs (`fetchcraft/fetchcraft-mcp`) Actor MCP server giving AI agents 8 B2B research tools: find verified work emails, extract contacts, detect Shopify/WordPress stacks, DNS+SaaS footprint, live ATS jobs, Hugging Face models, YC companies. Streamable HTTP, works with Claude/Cursor/Cline. $0.10 per tool call. - **URL**: https://apify.com/fetchcraft/fetchcraft-mcp.md - **Developed by:** [Emily Ward](https://apify.com/fetchcraft) (community) - **Categories:** MCP servers, AI, Lead generation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 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 ## B2B Research MCP Server - Work Email Finder & Tech Stack ![B2B Research MCP Server - Work Email Finder & Tech Stack](https://ai-sales-personalizer.vercel.app/og/mcp-fetchcraft-mcp.png) **Find verified work emails, extract contacts, detect tech stacks, profile DNS + SaaS, pull live jobs, search Hugging Face models and YC companies, straight from Claude, Cursor, or any MCP client.** Runs in Apify Standby mode (warm, responds in seconds). **Free to try right now** (in the 14-day price-notice window), then $0.10 per tool call. ### Who uses this - **Sales & RevOps teams** enriching inbound leads with verified emails and firmographics before they hit the CRM. - **Recruiters & sourcers** turning a name + company into a reachable work email. - **Agencies & founders** building targeted prospect lists without a stack of paid SaaS subscriptions. - **AI agents** that need to research a company or person mid-conversation and act on structured data. ### Connect in 30 seconds The server speaks **Streamable HTTP** at `/mcp`. Add this to your MCP client config: ```json { "mcpServers": { "fetchcraft": { "url": "https://fetchcraft--fetchcraft-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN" } } } ```` Works in **Claude Desktop, Cursor, Cline, Windsurf, VS Code**, and any Streamable-HTTP MCP client. **Get your token:** sign in at apify.com, then Settings, then Integrations, then copy your **Personal API token**. Paste it in place of `YOUR_APIFY_TOKEN`. The first call wakes the standby server (about 5 seconds); after that, calls are instant. **Test it works** (replace the token): ```bash curl -s -X POST "https://fetchcraft--fetchcraft-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" -H "Accept: application/json, text/event-stream" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' ``` ### What you get A representative result (clean structured JSON): ```json { "input_name": "Sam Altman", "domain": "openai.com", "found_email": "sam@openai.com", "verification_method": "mx+smtp", "confidence": "high" } ``` ### Tools | Tool | What it does | |---|---| | `find_work_email` | Resolve + verify a work email from name + company domain | | `extract_contacts` | Every email, phone, and social profile from a website URL | | `detect_shopify_stack` | Shopify + 25+ app fingerprints (Klaviyo, Recharge, Yotpo) | | `detect_wordpress_stack` | WordPress + 30+ plugins (Elementor, Yoast, Rank Math) | | `dns_email_footprint` | MX, SPF, DMARC, CDN, hosting + 25 SaaS signals from DNS | | `find_jobs` | Live jobs from Greenhouse, Lever, Ashby, Workable | | `search_huggingface_models` | HF models by downloads, likes, trending, author | | `search_yc_companies` | Y Combinator portfolio by batch, industry, region | Each tool runs a maintained fetchcraft Apify Actor under the hood and returns clean structured JSON. No LLM key, no scraping infrastructure to manage. ### Example agent prompts - "Find the work email for Sam Altman at openai.com." -> `find_work_email` - "What apps does gymshark.com run?" -> `detect_shopify_stack` - "Pull live engineering jobs from Airbnb and Netflix." -> `find_jobs` ### Why pay-per-call beats a subscription - **No subscription, no seats, no minimums.** You pay $0.10 per call, only for what you actually use. Comparable tools in this space usually lock you into a monthly plan whether you run it or not. - **No LLM key and no scraping infrastructure to manage.** Connect your Apify token and the maintained Actors do the work, returning clean structured JSON. - **Built for AI agents.** Drop-in MCP server over Streamable HTTP for Claude Desktop, Cursor, Cline, Windsurf, and VS Code. - **$5/month in free Apify platform credits** on every account, that is 50 free calls every month, indefinitely. ### Pricing - **Free right now.** Every tool call is free during the 14-day price-notice window. - After that: **$0.10 per tool call**, flat. No subscription, no seat licenses, no minimum. - Every Apify account includes **$5/month in free platform credits** (= 50 free tool calls every month). ### FAQ **Which MCP clients work?** Any client supporting Streamable HTTP: Claude Desktop, Cursor, Cline, Windsurf, VS Code, and others. **Do I need an LLM key?** No. Connect your Apify token and go. **Is the first call slow?** Only the very first one, while the standby server wakes (about 5 seconds). Subsequent calls are instant. **Can I get the full fetchcraft suite?** Yes, 28 scrapers plus 6 MCP servers under the `fetchcraft` publisher. Storefront: https://ai-sales-personalizer.vercel.app/scrapers ### Support Found a bug or want a new tool? Open an issue on the Actor's Issues tab. ### Last Updated 2026-06-18 # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("fetchcraft/fetchcraft-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("fetchcraft/fetchcraft-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 fetchcraft/fetchcraft-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fetchcraft/fetchcraft-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "B2B Research MCP Server - Email Finder, Tech Stack, Jobs", "description": "MCP server giving AI agents 8 B2B research tools: find verified work emails, extract contacts, detect Shopify/WordPress stacks, DNS+SaaS footprint, live ATS jobs, Hugging Face models, YC companies. Streamable HTTP, works with Claude/Cursor/Cline. $0.10 per tool call.", "version": "1.0", "x-build-id": "pgdxiMawLf17O1tNx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fetchcraft~fetchcraft-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fetchcraft-fetchcraft-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/fetchcraft~fetchcraft-mcp/runs": { "post": { "operationId": "runs-sync-fetchcraft-fetchcraft-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/fetchcraft~fetchcraft-mcp/run-sync": { "post": { "operationId": "run-sync-fetchcraft-fetchcraft-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 } } } } } } } } } } ```