M&A Target Intelligence MCP Server

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---

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "m-and-a-target-intelligence-mcp": {
      "url": "https://ryanclinton--m-and-a-target-intelligence-mcp.apify.actor/mcp"
    }
  }
}

---

M&A target intelligence at your AI agent's fingertips — this MCP server runs pre-acquisition due diligence screening by orchestrating 16 Apify actors in parallel across financial filings, IP databases, workforce signals, technology assessment, and reputation data. It produces an Acquisition Readiness Score (0-100) with dimensional scoring across five factors and automatic deal breaker detection. Built for corporate development teams, PE firms, venture investors, and financial analysts who want fast, programmatic target screening without expensive data subscriptions.

Connect once via the Model Context Protocol and your AI assistant — Claude, Cursor, Windsurf, or any MCP client — can assess acquisition targets, compare candidates, and surface red flags across the entire due diligence spectrum in a single tool call. The server runs in Apify Standby mode, meaning it stays warm and responds to queries immediately with no cold-start delay.

What data can you extract?

| Data Point | Source | Example Value |
|------------|--------|---------------|
| 📄 SEC filings (10-K, 10-Q, 8-K) | SEC EDGAR | 3 annual reports filed 2021-2024 |
| 💹 Insider buy/sell sentiment | SEC Form 4 | 12 buys, 2 sells — net positive |
| ⚠️ Consumer complaint count | CFPB database | 7 complaints — LOW severity |
| 🔬 US patent portfolio | USPTO | 34 patents across 6 technology classes |
| 🌍 European patents | EPO | 18 EPO patents — US+EU coverage |
| ™️ EU trademark registrations | EUIPO | 11 trademarks — strong brand portfolio |
| 👥 Active job postings | Job Market Intelligence | 47 roles — AGGRESSIVE hiring velocity |
| 🖥️ Technologies detected | Tech Stack Detector | React, TypeScript, Kubernetes, AWS |
| 💻 Public GitHub repositories | GitHub | 23 repos — strong open source presence |
| ⭐ Customer review rating | Trustpilot + Multi-Review | 4.3/5 across 312 reviews — STRONG |
| 📊 Competitive intelligence | SaaS Competitive Intel | 6 direct competitors identified |
| 🔎 SERP top-10 rankings | SERP Rank Tracker | 4 top-10 positions for brand terms |
| 🛒 E-commerce presence | Shopify Intelligence | Shopify store detected |
| 📑 Company research profile | Company Deep Research | Funding history, leadership, market |

Why use M&A Target Intelligence MCP Server?

Manual pre-acquisition screening is slow and expensive. A typical analyst spends 2-3 days gathering SEC filings, checking patent databases, reading reviews, and mapping the tech stack — for a single target. At $300+/hour for financial advisors, even a preliminary screen costs thousands. Enterprise data platforms like Capital IQ or PitchBook charge $20,000-40,000 per year for similar coverage.

This MCP server automates the entire initial screening process. Your AI agent calls one tool and receives structured, scored intelligence across 16 data sources in under three minutes, at a fraction of the cost.

- Scheduling — monitor deal pipeline targets weekly to catch changes in insider sentiment, hiring velocity, or complaint volumes
- API access — integrate target screening directly into Python, JavaScript, or any HTTP workflow
- Proxy rotation — all underlying actors use Apify's built-in proxy infrastructure for reliable data collection
- Monitoring — receive Slack or email alerts when a target's acquisition readiness score shifts materially
- Integrations — pipe scored targets into Airtable, HubSpot, Notion, Zapier, or any webhook-compatible deal tracking system

Features

- 8 specialized MCP tools covering every phase of pre-acquisition screening from financial health to market position
- 16 parallel data sources executed simultaneously — full acquisition readiness assessment completes in under 3 minutes
- Acquisition Readiness Score (0-100) computed across 5 weighted dimensions: Financial Health (25 pts), IP Portfolio (20 pts), Workforce Stability (20 pts), Technology Maturity (20 pts), Market Position (15 pts)
- 5-tier grade system — PRIME TARGET, STRONG TARGET, MODERATE TARGET, WEAK TARGET, NOT RECOMMENDED — with a plain-language recommendation
- Automatic deal breaker detection — flags massive insider selling with zero buys, CFPB complaint counts above 200, and zero IP protection on technology-heavy targets
- Insider trading sentiment analysis — compares Form 4 buy/sell ratios and applies a negative flag when sells exceed buys by 2x with more than 5 total transactions
- IP geographic coverage mapping — tracks US (USPTO), European (EPO), and EU trademark (EUIPO) coverage separately and awards bonus points for multi-jurisdiction protection
- Tech debt scoring — detects legacy frameworks (jQuery, AngularJS, Backbone, CoffeeScript, Flash, Silverlight) and subtracts value relative to modern stack presence
- Modern stack bonus — awards additional points when 3+ modern technologies (React, Vue, Next.js, TypeScript, Kubernetes, Docker, GraphQL, Terraform) are detected
- R&D intensity scoring — calculates the percentage of engineering/research job postings as a proxy for innovation investment
- Hiring velocity classification — AGGRESSIVE (30+ postings), HEALTHY (10-30), MODERATE (1-9), STAGNANT (0)
- Department diversity scoring — maps postings across engineering, sales, marketing, product, and operations to flag single-function organizations
- Standby mode deployment — server stays warm with no cold-start latency; connects instantly from any MCP client
- Pay-per-tool pricing — each tool call is charged individually at $0.045, so focused assessments cost less than full scans

Use cases for M&A target intelligence

Pre-LOI acquisition screening

Corporate development teams screening 10-20 targets before issuing a letter of intent. Run acquisition_readiness_score for each candidate and rank by composite score, grade, and deal breaker count. A two-hour screening process replaces two weeks of manual research and surfaces the highest-potential targets for deeper financial due diligence.

Private equity deal sourcing

PE analysts building proprietary deal flow pipelines who need rapid, repeatable screening across sectors. The scoring model works for both public companies (full SEC data available) and private targets (job postings, tech stack, reviews, and competitive intelligence still produce meaningful scores). Integrate with your deal CRM via webhooks for automated pipeline scoring.

Intellectual property valuation

IP and licensing teams assessing patent portfolio depth before an acquisition. The target_ip_portfolio tool queries USPTO, EPO, and EUIPO simultaneously and maps geographic coverage. Patent counts across US and European jurisdictions serve as a proxy for IP moat strength and potential licensing revenue.

Technology due diligence

Engineering leaders and CTOs assessing a target's technology maturity before a tech acquisition. The target_technology_assessment tool fingerprints the target's website stack, scans GitHub for open source activity, and flags tech debt signals in legacy frameworks — all before the first technical interview.

Competitor acquisition monitoring

Strategy teams tracking whether a competitor is becoming an acquisition target. Schedule weekly target_regulatory_exposure runs to detect sudden changes in insider trading patterns, spikes in CFPB complaints, or reductions in hiring velocity that signal distress.

Reputation risk assessment

Brand and communications teams checking whether a target carries hidden reputation liabilities. The target_reputation_scan tool aggregates Trustpilot and multi-platform reviews, computes a weighted average rating, and classifies sentiment. Average ratings below 3.0 trigger a POOR classification that feeds into the composite score.

How to use M&A target intelligence tools

1. Connect the MCP server — Add the server URL https://m-and-a-target-intelligence-mcp.apify.actor/mcp to your MCP client configuration (Claude Desktop, Cursor, Windsurf, or any compatible client). Provide your Apify API token as the bearer token.
2. Configure your query — Tell your AI assistant the company name to assess. Optionally provide the domain (e.g., acmecorp.com) for tech stack detection and the GitHub organization handle for repository scanning. The server infers reasonable defaults when these are omitted.
3. Choose a tool or run a full scan — Use individual tools like target_ip_portfolio for focused queries, or call acquisition_readiness_score to run all 16 data sources at once. The full scan takes 2-3 minutes.
4. Review scored results — The response includes dimension scores, deal breaker flags, a grade (PRIME TARGET through NOT RECOMMENDED), and a plain-language recommendation you can paste directly into a deal memo.

MCP tools

| Tool | Price | Description |
|------|-------|-------------|
| target_financial_health | $0.045 | Assess financial health via SEC filings (10-K, 10-Q, 8-K), insider trading buy/sell sentiment, and CFPB consumer complaint patterns. Scores Financial Health dimension (0-25). |
| target_ip_portfolio | $0.045 | Evaluate IP portfolio via USPTO patents, EPO patents, and EUIPO trademarks. Maps geographic coverage across US and EU. Scores IP Portfolio dimension (0-20). |
| target_regulatory_exposure | $0.045 | Scan for regulatory and compliance exposure: CFPB complaint severity classification (LOW/MODERATE/HIGH/CRITICAL), insider selling flags, and deal breaker identification. |
| target_workforce_analysis | $0.045 | Analyze workforce signals from live job postings: hiring velocity (AGGRESSIVE/HEALTHY/MODERATE/STAGNANT), departmental coverage, and R&D intensity percentage. Scores Workforce dimension (0-20). |
| target_technology_assessment | $0.045 | Assess technology maturity via website fingerprinting, GitHub repository scanning, modern framework detection, and legacy tech debt identification. Scores Technology dimension (0-20). |
| target_competitive_position | $0.045 | Evaluate market position via SaaS competitive intelligence, company research profiles, Shopify e-commerce presence, and top-10 SERP ranking counts. Scores Market Position dimension (0-15). |
| target_reputation_scan | $0.045 | Multi-source reputation assessment combining Trustpilot and multi-platform reviews into a composite average rating with STRONG/ACCEPTABLE/POOR/NO_DATA sentiment classification. |
| acquisition_readiness_score | $0.045 | Full acquisition assessment: all 16 data sources in parallel, 5-dimension scoring, automatic deal breaker detection, Acquisition Readiness Score (0-100), letter grade, and recommendation. |

Tool parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| companyName | string | Yes (all tools) | Legal or common company name. Used to query SEC, patents, job boards, reviews, and competitive data. Example: "Pinnacle Analytics Inc" |
| domain | string | No | Company website domain without protocol. Used for tech stack detection and Trustpilot lookup. Example: "pinnacleanalytics.com". Inferred from company name if omitted. |
| githubOrg | string | No | GitHub organization handle. Used for repository scanning. Defaults to company name if omitted. Example: "pinnacle-analytics" |

Input tips

- Provide the domain explicitly when the company name does not match the domain — auto-inference works for simple names but fails for branded domains like "stripe.com" vs "Stripe Inc".
- Use the full legal name for SEC and patent lookups — abbreviated or brand names may miss filings. "Meta Platforms Inc" returns more filings than "Meta".
- Run individual tools for cost control — if you only need IP data, call target_ip_portfolio at $0.045 rather than the full acquisition_readiness_score.
- Batch multiple targets sequentially — run acquisition_readiness_score for each candidate and compare the composite scores side-by-side in your AI chat session.
- Set a spending limit in Apify to cap costs per run automatically. The server checks the limit before each tool call and returns a clean error message if reached.

Output example

{
  "company": "Pinnacle Analytics Inc",
  "acquisitionReadinessScore": 74,
  "grade": "STRONG TARGET",
  "dimensions": {
    "financialHealth": {
      "score": 19,
      "max": 25,
      "findings": [
        "18 SEC filing(s) — financial transparency available",
        "4 annual reports (10-K) — multi-year financial history",
        "Insider sentiment POSITIVE: 8 buys vs 3 sells — management believes in growth",
        "No CFPB complaints — clean consumer track record"
      ]
    },
    "ipPortfolio": {
      "score": 16,
      "max": 20,
      "findings": [
        "34 USPTO patents — strong US IP portfolio",
        "11 EPO patents — international IP coverage",
        "7 EUIPO trademark(s)",
        "IP coverage spans US and EU — geographic diversity"
      ]
    },
    "workforceStability": {
      "score": 17,
      "max": 20,
      "findings": [
        "47 active job postings — aggressive growth hiring",
        "Hiring across 5 departments — well-rounded organization",
        "68% R&D roles — innovation-focused"
      ]
    },
    "technologyMaturity": {
      "score": 15,
      "max": 20,
      "findings": [
        "23 technologies detected — mature tech stack",
        "14 public GitHub repos",
        "No major tech debt signals in detected stack",
        "Modern tech stack: React, TypeScript, Next.js, Docker, Kubernetes"
      ]
    },
    "marketPosition": {
      "score": 7,
      "max": 15,
      "findings": [
        "Average review rating 4.3/5 across 312 reviews — strong reputation",
        "Competitive intelligence data available for market positioning analysis",
        "Company research data available",
        "4 top-10 SERP ranking(s) — strong search visibility"
      ]
    }
  },
  "dealBreakers": [],
  "recommendation": "Strong acquisition target. Proceed to detailed due diligence and valuation.",
  "dataSources": {
    "secFilings": 18,
    "insiderTrades": 11,
    "cfpbComplaints": 0,
    "usptoPatents": 34,
    "epoPatents": 11,
    "trademarks": 7,
    "jobPostings": 47,
    "techStackItems": 23,
    "githubRepos": 14,
    "reviews": 312,
    "competitiveIntel": 6,
    "serpRankings": 20
  }
}

Output fields

| Field | Type | Description |
|-------|------|-------------|
| company | string | Company name as passed to the tool |
| acquisitionReadinessScore | number | Composite score 0-100. Higher = more acquisition-ready |
| grade | string | PRIME TARGET / STRONG TARGET / MODERATE TARGET / WEAK TARGET / NOT RECOMMENDED |
| recommendation | string | Plain-language deal recommendation derived from score and deal breakers |
| dealBreakers[] | array | List of critical red flags. Empty array = no deal breakers detected |
| dimensions.financialHealth.score | number | Financial Health sub-score (max 25) |
| dimensions.financialHealth.findings[] | array | Human-readable findings for this dimension |
| dimensions.ipPortfolio.score | number | IP Portfolio sub-score (max 20) |
| dimensions.ipPortfolio.findings[] | array | Patent and trademark findings |
| dimensions.workforceStability.score | number | Workforce Stability sub-score (max 20) |
| dimensions.workforceStability.findings[] | array | Hiring velocity and department coverage findings |
| dimensions.technologyMaturity.score | number | Technology Maturity sub-score (max 20) |
| dimensions.technologyMaturity.findings[] | array | Tech stack, GitHub, and tech debt findings |
| dimensions.marketPosition.score | number | Market Position sub-score (max 15) |
| dimensions.marketPosition.findings[] | array | Review ratings, SERP, and competitive findings |
| dataSources.* | number | Count of records returned from each data source |
| insiderSentiment.buys | number | Number of insider purchase transactions (financial health tool) |
| insiderSentiment.sells | number | Number of insider sale transactions (financial health tool) |
| regulatoryFlags.cfpbSeverity | string | LOW / MODERATE / HIGH / CRITICAL (regulatory exposure tool) |
| hiringVelocity | string | AGGRESSIVE / HEALTHY / MODERATE / STAGNANT (workforce tool) |
| reputation.averageRating | number | Weighted average review rating across all platforms (reputation tool) |
| reputation.sentiment | string | STRONG / ACCEPTABLE / POOR / NO_DATA (reputation tool) |

How much does it cost to screen acquisition targets?

Each MCP tool call costs $0.045. That is the full price — no platform fees, no subscription. The acquisition_readiness_score tool, which queries all 16 data sources, still costs $0.045 per company assessed.

| Scenario | Companies | Tool calls | Total cost |
|----------|-----------|------------|------------|
| Quick test — one full assessment | 1 | 1 | $0.045 |
| Initial deal list screen | 10 | 10 | $0.45 |
| Mid-stage screening round | 25 | 25 | $1.13 |
| Full sector sweep | 100 | 100 | $4.50 |
| Quarterly pipeline refresh | 500 | 500 | $22.50 |

You can set a maximum spending limit per run in Apify to control costs. The server checks the limit before each tool call and returns a graceful error if the budget is reached rather than continuing to charge.

Compare this to Capital IQ or PitchBook at $20,000-40,000 per year, or to manual analyst time at $300+/hour per target. Most corporate development teams screen 20-50 targets per quarter and spend under $3 total with this server.

How to connect and use the API

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "m-and-a-target-intelligence": {
      "url": "https://m-and-a-target-intelligence-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Python (with MCP client)

import httpx
import json
APIFY_TOKEN = "YOUR_APIFY_TOKEN"

MCP_URL = "https://m-and-a-target-intelligence-mcp.apify.actor/mcp"
payload = {

    "jsonrpc": "2.0",

    "method": "tools/call",

    "params": {

        "name": "acquisition_readiness_score",

        "arguments": {

            "companyName": "Pinnacle Analytics Inc",

            "domain": "pinnacleanalytics.com",

            "githubOrg": "pinnacle-analytics"

        }

    },

    "id": 1

}
response = httpx.post(

    MCP_URL,

    json=payload,

    headers={"Authorization": f"Bearer {APIFY_TOKEN}"}

)
result = response.json()

data = json.loads(result["result"]["content"][0]["text"])

print(f"Company: {data['company']}")

print(f"Score: {data['acquisitionReadinessScore']}/100 — {data['grade']}")

print(f"Deal breakers: {data['dealBreakers'] or 'None'}")

print(f"Recommendation: {data['recommendation']}")

JavaScript

const APIFY_TOKEN = "YOUR_APIFY_TOKEN";
const MCP_URL = "https://m-and-a-target-intelligence-mcp.apify.actor/mcp";
const response = await fetch(MCP_URL, {

  method: "POST",

  headers: {

    "Content-Type": "application/json",

    "Authorization": Bearer ${APIFY_TOKEN},

  },

  body: JSON.stringify({

    jsonrpc: "2.0",

    method: "tools/call",

    params: {

      name: "acquisition_readiness_score",

      arguments: {

        companyName: "Pinnacle Analytics Inc",

        domain: "pinnacleanalytics.com",

      },

    },

    id: 1,

  }),

});
const result = await response.json();

const data = JSON.parse(result.result.content[0].text);

console.log(${data.company}: ${data.acquisitionReadinessScore}/100 — ${data.grade});

for (const [dim, detail] of Object.entries(data.dimensions)) {

  console.log(  ${dim}: ${detail.score}/${detail.max});

}

if (data.dealBreakers.length > 0) {

  console.warn("DEAL BREAKERS:", data.dealBreakers);

}

cURL

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