MCP 服务器

open-sales-stack

Collection of B2B sales intelligence MCP servers. Includes website analysis, tech stack detection, hiring signals, review aggregation, ad tracking, social profiles, financial reporting and more for AI-powered prospecting

README

Open Sales Stack

Open source MCP servers for B2B sales research — built by Ekas

Give Claude the ability to research companies and prospects using public web data.

Platform: macOS only for now. Windows and Linux support coming soon.

What's in here

MCP Server	What you get	Status
website-intel	Product info, pricing, team pages, company details — extracted as structured data from any website	✅ Ready
techstack-intel	CRM, marketing automation, analytics, chat, support tools — detected from page source	✅ Ready
social-intel	LinkedIn company profiles, people profiles, company posts	✅ Ready
hiring-intel	Open roles across Indeed, LinkedIn, Glassdoor, Google Jobs, ZipRecruiter, and direct careers pages	✅ Ready
ad-intel	Active campaigns, ad creatives, targeting signals — from LinkedIn Ad Library and Meta Ad Library	✅ Ready
review-intel	Star ratings, review counts, pros/cons themes — from G2, Capterra, and Glassdoor	🔄 In Progress
funding-intel	Funding rounds, investors, total raised, valuations — from Crunchbase and public filings	🔄 In Progress
news-intel	Recent press coverage, product launches, leadership changes, M&A activity	🔄 In Progress
financial-reporting-intel	10-K/10-Q filings, revenue, growth rate, operating margins, guidance — for public companies	🔄 In Progress
firmographic-intel	Employee count, headcount growth, HQ location, founding year, industry, SIC/NAICS codes, legal entity name — all from public sources	🔄 In Progress
github-intel	Public repos, stars, contributors, commit activity, open issues, tech stack signals — from GitHub public API	🔄 In Progress

An API key from OpenAI, Anthropic, or Google Gemini is required for LLM-based extraction. Beyond that, no additional API keys are needed. Each MCP runs locally on your machine. Your IP, your requests — no proxy infrastructure, no rate limiting concerns.

Setup

You'll need two things installed before starting:

Python 3.10+ — download from python.org or install via brew install python@3.12
An LLM API key — from OpenAI, Anthropic, or Google AI Studio

Then run these commands in your terminal:

# 1. Clone the repo
git clone https://github.com/ekas-io/open-sales-stack.git
cd open-sales-stack

# 2. Run setup (installs everything and prompts you to choose your LLM provider)
bash scripts/setup.sh

# 3. Verify your setup
bash scripts/verify.sh

# 4. Add all MCPs to Claude
bash scripts/add-to-claude.sh --all

By default, the script adds MCPs to Claude Code if the claude CLI is available, otherwise to Claude Desktop. You can override this:

bash scripts/add-to-claude.sh --all --desktop   # force Claude Desktop
bash scripts/add-to-claude.sh --all --code      # force Claude Code

The setup script will ask you to choose between OpenAI, Anthropic, or Gemini and prompt for your API key. It configures everything in .env automatically.

If you want to change the default model later, edit the LLM_PROVIDER value in your .env file. See .env.example for supported format.

During setup, you'll also be asked how you'd like to authenticate with LinkedIn (for social-intel):

Skip (default) — configure later; company scraping works without login
Browser login — a browser window opens, you log in manually
Credentials — provide your email + password, saved locally for headless login

See the social-intel README for more details.

If you only want specific MCPs:

bash scripts/add-to-claude.sh --website-intel --social-intel --hiring-intel

Verify in Claude

Once added, ask Claude:

"What MCP tools do you have access to?"

You should see your installed tools listed.

How the MCPs work together

Each MCP is independent — use one or use all. But they're designed to chain naturally in Claude. Here's what a typical company research flow looks like:

You: "Research Acme Corp for me"

Claude calls: website-intel    → scrapes acmecorp.com, extracts product info, pricing, team
Claude calls: techstack-intel  → detects they use HubSpot, Drift, Segment
Claude calls: hiring-intel     → finds 3 open SDR roles on their Greenhouse page
Claude calls: social-intel     → finds their VP Sales on LinkedIn, pulls bio and recent posts
Claude calls: review-intel     → pulls G2 rating (4.2/5, 47 reviews), Glassdoor sentiment
Claude calls: ad-intel         → 12 active LinkedIn ad campaigns, 5 on Meta
Claude calls: funding-intel    → Series B, $24M raised, led by Accel
Claude calls: firmographic-intel → 320 employees, 40% headcount growth YoY
Claude calls: news-intel       → 3 recent press mentions, product launch last month

Claude: "Here's what I found about Acme Corp..."

You don't need to orchestrate this. Claude reads the tool descriptions and decides which to call based on your request.

Skills

Skills are instruction files that teach Claude how to use research data for sales workflows. Drop them into your Claude project knowledge or reference them in prompts.

Skill	What it teaches Claude
Lead Qualification	Evaluate whether a company matches your ICP based on research signals
Prospect Research	Full account + contact level research methodology
LinkedIn Recon	Read a prospect's LinkedIn profile and posts for outreach signals
Cold Email Personalization	Turn research into personalized outreach copy

MCPs get the data. Skills tell Claude what to do with it.

Each MCP in detail

Every package has its own README with tool descriptions, input/output schemas, and usage examples. Browse the packages/ directory, or see detailed use cases on our website: ekas.io/open-sales-stack

Contributing

Found a bug? Want to add a new research MCP? PRs welcome. See the packages/ directory for the existing pattern.

Custom sales automation

These tools cover common research workflows. If you need AI automation built for your team's specific sales stack — CRM integration, lead routing, qualification scoring, automated outreach — we build that.

ekas.io — AI engineering for B2B sales teams.