# Stratalize Intelligence (remote · www.stratalize.com)

Vendor benchmarks, H-1B wages, federal contracts, USPTO patent filings, and public financials.

- Trust score: 70/100 (medium)
- Change this week: +9
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `www.stratalize.com`: 70/100 (this document), [markdown](https://verifymcp.io/servers/com-stratalize-intelligence/api-mcp-public.md), [page](https://verifymcp.io/servers/com-stratalize-intelligence/api-mcp-public)

## Channel facts

- Endpoint: `https://www.stratalize.com/api/mcp-public?vertical=intelligence`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.1.1`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 54/100
  - AI-judged instruction clarity (fair).
  - Context-footprint check failed: tool/resource definitions use about 5364 tokens (~119/item across 45 items; 45 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 77/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 31% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http com-stratalize-intelligence https://www.stratalize.com/api/mcp-public?vertical=intelligence
```

### Codex

```toml
[mcp_servers.com-stratalize-intelligence]
url = "https://www.stratalize.com/api/mcp-public?vertical=intelligence"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-stratalize-intelligence": {
      "type": "remote",
      "url": "https://www.stratalize.com/api/mcp-public?vertical=intelligence",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-stratalize-intelligence --url https://www.stratalize.com/api/mcp-public?vertical=intelligence --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-stratalize-intelligence:
    url: "https://www.stratalize.com/api/mcp-public?vertical=intelligence"
```

### Other

```json
{
  "mcpServers": {
    "com-stratalize-intelligence": {
      "type": "http",
      "url": "https://www.stratalize.com/api/mcp-public?vertical=intelligence"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 70, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-01 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 68, +5)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 63, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 62, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 61, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 60)

First indexed and scored.

## MCP tools (45)

### `get_web_research_synthesis` (~62 tokens)

Governed web research synthesis with cryptographic receipt. Agent submits a research question; returns an AI synthesis across live web sources with every source domain bound into an ML-DSA-65 signed receipt anchored on Base.

Input parameters:

- `question` (string, required)

### `get_regulatory_news_synthesis` (~69 tokens)

Real-time regulatory news synthesis for a topic or agency (SEC, FTC, OCC, CFPB, FDA). Returns current enforcement and rule-change developments synthesized from live sources with a signed receipt proving which sources were consulted.

Input parameters:

- `agency` (string)
- `topic` (string, required)

### `get_company_intelligence_synthesis` (~59 tokens)

Live company intelligence synthesis: recent litigation, regulatory exposure, financial and news developments for a named company with ML-DSA-65 signed receipt for due diligence workflows.

Input parameters:

- `company` (string, required)
- `focus` (string)

### `get_competitive_landscape_synthesis` (~47 tokens)

Competitive landscape synthesis for a market category: current leaders, positioning, and dynamics synthesized from live web sources with a signed provenance receipt.

Input parameters:

- `market` (string, required)

### `get_legislative_intelligence_synthesis` (~57 tokens)

Legislative intelligence synthesis: current bill status, recent passages, and amendments for a topic and jurisdiction, synthesized from live sources with a cryptographic receipt.

Input parameters:

- `jurisdiction` (string)
- `topic` (string, required)

### `get_vendor_market_rate` (~154 tokens)

Use when a CFO or procurement team needs to know if they are overpaying for any software vendor. Returns monthly_median, monthly_low, monthly_high, annual_median, pricing_model, source, and data_as_of from healthcare vendor benchmark lookups with Stratalize composite medians as fallback when no vendor-specific row exists. Example: Salesforce CRM median ~$8,400/mo — organizations above the monthly_high range are overpaying by a recoverable margin. Source: healthcare_vendor_benchmarks with Stratalize composite medians.

Input parameters:

- `company_size` (string): Optional company size segment
- `industry` (string): Optional industry filter
- `vendor_name` (string, required): Vendor name to look up

### `get_category_spend_benchmark` (~117 tokens)

Use when benchmarking total spend in a software category against same-size peers. Returns median monthly spend, p25/p75 band, and sample size for any software category by company size. Example: Mid-market CRM spend median ~$3,500/mo, p75 of $4,900 — organizations above p75 have a negotiation mandate supported by market data. Source: Stratalize enterprise spend composite.

Input parameters:

- `category` (string, required): Software or service category
- `company_size` (string)
- `industry` (string)

### `get_vendor_negotiation_intelligence` (~107 tokens)

Use when preparing to renew or renegotiate a SaaS contract. Returns typical discount percentage, best negotiation windows, leverage points, auto-renewal risk flags, and a negotiation script for any vendor. Example: Salesforce renewals average 12-18% discount when initiated 90 days before renewal with multi-year commit — Q4 close urgency adds 5-8% additional leverage. Source: Stratalize procurement intelligence composite.

Input parameters:

- `vendor_name` (string, required)

### `get_top_vendors_by_category` (~107 tokens)

Use when building a vendor shortlist for a new software category purchase. Returns vendors ranked by mention count and median spend from enterprise spend data. Example: HR tech category — Workday median $42K/mo, BambooHR $3,200/mo, Rippling $8,400/mo — 13x spend spread between enterprise and SMB confirms size-appropriate shortlisting. Source: Stratalize market composite.

Input parameters:

- `category` (string, required)
- `limit` (number)

### `get_vendor_alternatives` (~115 tokens)

Use when evaluating a vendor switch or building a competitive RFP against an incumbent. Returns alternative vendors with migration complexity scores, estimated savings, and switching narrative. Example: Salesforce alternatives — HubSpot at 22% lower median spend with comparable CRM coverage, Pipedrive at 41% lower for sales-only — migration complexity rated MEDIUM for both. Source: Stratalize competitive displacement composite.

Input parameters:

- `reason` (string, required): Primary driver for evaluating alternatives
- `vendor_name` (string, required): Incumbent vendor name

### `get_saas_negotiation_playbook` (~145 tokens)

Use when a major SaaS contract is approaching renewal or auto-renewal risk. Returns timing strategy, leverage points, walk-away alternatives, and a complete negotiation script for any vendor. Example: Datadog renewal — initiate 90 days before, cite Grafana Cloud at 40% lower cost as walk-away, target 15-20% discount — Q4 close adds urgency leverage. Source: Stratalize procurement intelligence.

Input parameters:

- `contract_value_annual` (number): Current ACV in USD
- `renewal_days_out` (number): Days until renewal
- `vendor_name` (string, required): e.g. Salesforce, HubSpot, Slack

### `get_industry_spend_profile` (~129 tokens)

Use when sizing a technology budget for a specific industry and headcount, or identifying category spend outliers. Returns spend bands, category ranges, and outlier flags scaled to employee count. Example: 500-person healthcare org — total SaaS stack median $1.2M/yr, EHR 34% of spend, clinical productivity tools 18% — organizations above $1.8M are consolidation candidates. Source: Stratalize workforce-scaled composite.

Input parameters:

- `employee_count` (number, required): Employee headcount for banding
- `industry` (string, required): Industry vertical

### `get_vendor_contract_intelligence` (~107 tokens)

Use when reviewing a new vendor agreement or benchmarking contract terms before a negotiation. Returns typical contract length, auto-renewal notice window, price escalation percentage, and key risk clauses for any major vendor. Example: Salesforce standard — 36-month term, 60-day auto-renewal notice, 7% annual escalation — missing the 60-day window costs 12 months of negotiation leverage. Source: Stratalize contract intelligence composite.

Input parameters:

- `vendor_name` (string, required)

### `get_spend_by_company_size` (~104 tokens)

Use when benchmarking software category spend against same-size organizations before a purchase or renewal. Returns SMB, mid-market, and enterprise median monthly spend for any software category. Example: CRM median spend — SMB $1,200/mo, mid-market $8,400/mo, enterprise $42,000/mo — 35x spread confirms size-appropriate benchmarking before any negotiation. Source: Stratalize size-segmented composite.

Input parameters:

- `vendor_name` (string, required)

### `get_software_pricing_intelligence` (~119 tokens)

Use when evaluating a new software purchase or reviewing a vendor quote for hidden costs. Returns common pricing models, hidden cost patterns, implementation cost ranges, and budget guidance by category. Example: CRM hidden costs — API overage $0.02/call adds $8,400/yr at 420K monthly calls, sandbox $1,200/mo additional, SSO integration $15K one-time — total cost 40% above list price. Source: Stratalize category pricing composite.

Input parameters:

- `category` (string, required)

### `get_category_ai_leaders` (~96 tokens)

Use when assessing brand visibility in AI-generated recommendations or researching which vendors dominate AI platform responses in a software category. Returns vendors ranked by unprompted AI mention frequency. Example: CRM category — Salesforce 42 mentions across 100 queries, HubSpot 28, Microsoft Dynamics 14 — Salesforce dominates AI recommendations by 50% over nearest competitor. Source: Stratalize AI citation index.

Input parameters:

- `category` (string, required)

### `get_platform_divergence` (~102 tokens)

Use when identifying gaps between AI platform recommendations and actual market position for a vendor or topic. Returns platform agreement score showing consistency across AI platforms. Example: Salesforce scores 0.91 agreement across ChatGPT, Claude, Gemini, Perplexity — near-universal consensus. Niche vendors often score below 0.50 — high divergence signals a content gap opportunity. Source: Stratalize multi-platform citation composite.

Input parameters:

- `brand_name` (string, required)

### `get_brand_momentum` (~101 tokens)

Use when monitoring a vendor brand trajectory in AI recommendations or tracking week-over-week competitor momentum for a CMO brief. Returns 4-week momentum score, trend direction, and weekly movement series. Example: HubSpot 4-week momentum +1.8, GROWING trend — 3 consecutive weeks of citation increase following major product launch — competitive signal requiring CMO attention. Source: Stratalize brand index.

Input parameters:

- `brand_name` (string, required)

### `get_ai_consensus_on_topic` (~109 tokens)

Use when researching how AI systems characterize a vendor, category, trend, or business topic across multiple platforms simultaneously. Returns consensus score, sentiment mix, key themes, and platform-by-platform breakdown. Example: AI in healthcare scores 0.78 consensus — key themes: clinical decision support, administrative automation, prior auth reduction — high consensus signals established narrative safe for board communications. Source: Stratalize AI citation composite.

Input parameters:

- `category` (string)
- `topic` (string, required)

### `get_market_intelligence_brief` (~108 tokens)

Use when producing a quick industry intelligence brief or validating market narrative for a strategy deck. Returns AI-generated market summary, up to six key themes, and sentiment skew for any industry. Example: Healthcare IT market — POSITIVE sentiment 68%, key themes: EHR consolidation, AI-assisted coding, value-based care expansion — consolidation narrative dominant across 847 analyzed queries. Source: Stratalize AI citation composite.

Input parameters:

- `industry` (string, required)
- `topic` (string)

### `get_industry_spend_benchmark` (~124 tokens)

Use when validating total IT spend against industry peers or building a software budget baseline for a CFO board presentation. Returns median monthly total software stack spend, category breakdown, and productivity tool medians by industry. Example: Mid-market healthcare org — median total SaaS spend $18,500/mo, EHR and clinical tools 41% of stack, productivity suite $2,800/mo — organizations above $26,000/mo are consolidation candidates. Source: Stratalize industry composite.

Input parameters:

- `company_size` (string)
- `industry` (string, required)

### `get_category_disruption_signal` (~108 tokens)

Use when assessing whether a software category faces near-term displacement risk, or timing a market entry or exit decision. Returns disruption risk score from 0 to 1 with evidence strings from citation volume patterns. Example: ERP category — disruption risk score 0.71, evidence: 34 citations referencing AI-native alternatives, 12 referencing no-code replacements — HIGH disruption risk for legacy on-premise vendors. Source: Stratalize citation volume heuristics.

Input parameters:

- `category` (string, required)

### `get_competitive_displacement_signal` (~99 tokens)

Use when tracking competitive threats to an incumbent vendor or identifying switching trends in a software category. Returns vendors mentioned as replacements for a target vendor with switch narrative and mention counts. Example: Salesforce displacement — HubSpot replacing in SMB at 28 mentions, Dynamics replacing in enterprise at 19 — highest displacement pressure in mid-market 100-500 employees. Source: Stratalize citation displacement composite.

Input parameters:

- `vendor_name` (string, required)

### `get_vendor_risk_signal` (~98 tokens)

Use when screening a vendor for financial instability or procurement risk before a long-term contract commitment. Returns a risk score from 0 to 1 with risk indicators and negative mention evidence. Example: Vendor X scores 0.72 risk — indicators: customer churn citations, pricing disputes, product roadmap uncertainty — HIGH risk classification, recommend short-term contract only. Source: Stratalize citation risk composite.

Input parameters:

- `vendor_name` (string, required)

### `get_market_structure_signal` (~98 tokens)

Use when assessing software category maturity, timing a market entry, or evaluating consolidation risk in a category. Returns market structure signal (consolidating or fragmenting) with citation evidence. Example: HR tech category — CONSOLIDATING signal, top 3 vendors hold 71% of AI citation share — late-stage consolidation signals pricing power shift to incumbents. Source: Stratalize market structure composite.

Input parameters:

- `category` (string, required)

### `get_macro_market_signal` (~119 tokens)

Use when a current macro environment snapshot is needed for a trading brief, CFO board presentation, or investment committee context. Returns Fed funds rate, Treasury yields, CPI, PCE, and employment data when FRED API is configured. Example: Fed funds 5.33%, 10Y 4.42%, CPI 3.1% — rates and inflation above long-run targets, labor market tight — LATE-CYCLE positioning signal. Source: FRED St. Louis Fed, daily update.

Input parameters:

- `signal_type` (string)

### `get_sector_ai_intelligence` (~106 tokens)

Use when producing equity research, tracking brand share in AI sector coverage, or benchmarking a company AI visibility against sector peers. Returns top brands by AI mention share, sector trend narrative, and themed bullets for any equity sector. Example: Financials sector — JPMorgan leads at 34% citation share, Goldman 22%, BlackRock 18% — narrative focused on digital transformation and cost efficiency. Source: Stratalize AI citation composite.

Input parameters:

- `sector` (string, required)

### `get_pe_portfolio_benchmark` (~114 tokens)

Use when benchmarking portco technology spend for PE operating partners or building a software cost reduction case across a portfolio. Returns median software spend per company, category breakdown, and savings opportunity percentage. Example: Mid-market portco median $480K/yr — 18% savings opportunity through vendor consolidation — $86K/portco recovery across 10-company portfolio = $860K EBITDA improvement. Source: Stratalize PE Intelligence composite.

Input parameters:

- `company_count` (number)
- `sector` (string)

### `get_saas_market_intelligence` (~114 tokens)

Use when assessing a SaaS category investment thesis, competitive dynamics, or market momentum before a strategic decision. Returns growth signal, AI citation leaders, and disruption risk for any software category. Example: CRM category — GROWING signal, Salesforce leads at 42% citation share, HubSpot gaining 8% share year-over-year, disruption risk MODERATE from AI-native CRMs — signals consolidation pressure on mid-tier vendors. Source: Stratalize market intelligence composite.

Input parameters:

- `category` (string, required)

### `get_investment_category_signal` (~99 tokens)

Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

Input parameters:

- `category` (string, required)

### `get_portfolio_vendor_intelligence` (~103 tokens)

Use when conducting vendor diligence for a PE or VC portfolio company before a value creation initiative. Returns market rate data, brand index snapshot, and competitive displacement signals for any vendor. Example: Portco using Salesforce at $12,400/mo — market median $8,400/mo, 48% above market — immediate renegotiation opportunity with $48K annual EBITDA recovery. Source: Stratalize composite diligence.

Input parameters:

- `vendor_name` (string, required)

### `get_vendor_benchmark` (~145 tokens)

Use when a CFO or procurement lead needs org-specific vendor pricing vs market before renewal or negotiation. Returns market_low, market_median, market_high, position_label, negotiation tactics, estimated_savings_monthly from benchmark_cache when fresh, or guidance to load intelligence in Stratalize. Example: Salesforce median ~$8,400/mo — recoverable gap when spend exceeds monthly_high. Source: benchmark_cache + Stratalize composites.

Input parameters:

- `mcp_client_source` (string): Optional client identifier for analytics (e.g. host app or integration name)
- `vendor_name` (string, required): Name of the vendor to benchmark (e.g. HubSpot, QuickBooks, Salesforce)

### `get_company_salary_disclosure` (~143 tokens)

Use when benchmarking compensation against disclosed employer wages or assessing H-1B wage practices before a talent acquisition or competitive hire. Returns DOL LCA and H-1B wage aggregates by employer, job title, state, and fiscal year. Example: Microsoft H-1B software engineer — prevailing wage Level III $178K in Seattle, Level IV $215K — 847 certified positions in 2023, concentrated in Washington and California. Source: DOL Office of Foreign Labor Certification public filings.

Input parameters:

- `company_name` (string, required)
- `fiscal_year` (number)
- `job_title` (string)
- `state` (string)

### `get_employer_h1b_wages` (~126 tokens)

Use when analyzing an employer H-1B compensation strategy or benchmarking tech sector wages against DOL prevailing wage data. Returns prevailing wage statistics, certified job titles, wage levels, and state distribution from DOL LCA filings. Example: Google H-1B — software engineer Level IV prevailing wage $195K, 1,243 certified positions in 2023 — concentrated in Mountain View and New York City offices. Source: DOL Labor Condition Application public data.

Input parameters:

- `employer_name` (string, required): e.g. Google, Deloitte, Cognizant

### `get_federal_contract_intelligence` (~164 tokens)

Use when researching a company federal revenue concentration, identifying government contract competitors, or assessing vendor dependency on federal business. Returns contract obligation data by vendor, agency, NAICS code, and fiscal year from USASpending. Example: Acme IT Services — $847M federal obligations FY2023, 67% from DoD, 3 agencies representing 89% of revenue — high concentration risk for supply chain or M&A due diligence. Source: USASpending.gov synced data.

Input parameters:

- `agency_name` (string)
- `fiscal_year` (number)
- `naics_code` (string)
- `state` (string): Two-letter state code for place of performance (e.g. PA, IL).
- `vendor_name` (string, required)

### `get_workplace_safety_benchmark` (~102 tokens)

OSHA injury and illness rate benchmarks by company, industry, NAICS code, and state. Industry composite benchmarks available immediately with no sync required — establishment-specific data enabled when OSHA sync is connected. Covers injury rates, top-quartile performance, and EMR context for insurance, bonding, and public contract prequalification.

Input parameters:

- `company_or_industry` (string, required)
- `naics_code` (string)
- `state` (string)

### `get_public_company_financials` (~128 tokens)

Use when pulling public company financials for a comparable company analysis, M&A due diligence, or investor brief. Returns SEC EDGAR financial statement data — income statement, balance sheet, and key ratios from filed reports. Note: cache may reflect prior quarter — verify against latest SEC filing for time-sensitive analysis. Example: Salesforce FY2024 — $34.9B revenue, 29% operating margin on services, $4.1B operating cash flow — fundamental anchor for CRM sector comparable analysis. Source: SEC EDGAR synced filings.

Input parameters:

- `company_name` (string, required)

### `get_uspto_patent_intelligence` (~135 tokens)

Use when assessing a company IP portfolio strength, tracking competitor patent activity, or preparing M&A patent due diligence. Returns USPTO filing rollups by assignee — patent counts, filing years, and CPC classification. Example: Qualcomm — 47,000+ active patents, 3,200 filed in 2023, concentrated in 5G and AI/ML — top 3 CPC codes represent 61% of portfolio — IP moat assessment critical for semiconductor M&A. Source: USPTO PatentsView synced data.

Input parameters:

- `assignee_name` (string, required)
- `patent_year` (number)

### `get_github_ecosystem_intelligence` (~117 tokens)

Use when assessing a technology vendor open-source presence, evaluating developer community strength, or researching a company GitHub footprint before a technical due diligence. Returns organization profile and top repository stats — stars, forks, contributors, and language breakdown. Example: HashiCorp GitHub — 18 public repos, Terraform at 38,000 stars, 147,000 forks, 2,800 contributors — strong community signal supporting enterprise adoption thesis. Source: GitHub public API.

Input parameters:

- `org_or_company` (string, required)

### `get_stratalize_overview` (~112 tokens)

START HERE — Returns the complete Stratalize tool catalog: governed MCP tools across finance, healthcare, governance, real estate, crypto, and intelligence. Available via public MCP (no auth) or x402 micropayments on Base ($0.02 atomic · $0.10 benchmark · $0.50 synthesis · $1.00 premium · $3.00 outcome pack). Org intelligence, agent governance, and role briefs require OAuth. Call this first to discover tools by role or vertical.

### `get_salary_benchmark` (~164 tokens)

Use when setting compensation ranges, evaluating a job offer, or preparing a comp committee presentation for any role. Returns p25, p50, p75 wage estimates with state and industry adjustments across 50+ role families. Example: Software engineer in Illinois — p25 $98K, median $127K, p75 $158K — organizations benchmarking above p75 retain 34% fewer departures in competitive talent markets. Source: BLS Occupational Employment Statistics, latest release.

Input parameters:

- `industry` (string): e.g. saas, healthcare, legal, financial_services, manufacturing, retail
- `job_title` (string, required): e.g. Software Engineer, CFO, Account Executive, Data Scientist, HR Manager
- `state` (string): Two-letter US state code

### `get_saas_metrics_benchmark` (~167 tokens)

Use when assessing SaaS company financial health, preparing investor reporting, or benchmarking KPIs before a fundraise or board presentation. Returns Rule of 40, burn multiple, CAC payback, NRR, gross margin, and ARR growth targets by ARR band. Example: $10-50M ARR benchmark — Rule of 40 median 28, NRR median 108%, CAC payback 18 months — companies below median Rule of 40 face 2-3x valuation compression in current market. Source: Stratalize SaaS benchmark tables.

Input parameters:

- `arr_usd` (number, required): Annual Recurring Revenue in USD
- `burn_multiple` (number): Net burn divided by net new ARR
- `growth_rate_pct` (number): YoY ARR growth %

### `get_cac_benchmark` (~146 tokens)

Use when evaluating sales and marketing efficiency, setting CAC targets, or benchmarking GTM performance before a board review. Returns CAC payback ranges, LTV/CAC guardrails, and channel efficiency benchmarks by industry and GTM motion. Example: Mid-market SaaS with field sales — median CAC payback 22 months, LTV/CAC 3.8x — organizations above 30-month payback face capital efficiency pressure from investors. Source: Stratalize go-to-market composite.

Input parameters:

- `avg_contract_value_usd` (number): ACV for LTV:CAC calculation
- `gtm_motion` (string)
- `industry` (string, required): Industry vertical

### `get_osha_enforcement` (~85 tokens)

OSHA inspection and violation history for a named employer — total inspections, violations, cumulative penalties, and violation type breakdown. Source: DOL Enforcement Data. Use in vendor diligence, M&A, or ESG risk. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.

Input parameters:

- `employer_name` (string, required)

### `get_irs_industry_tax_statistics` (~190 tokens)

Use when benchmarking financial performance against industry-level tax return data, establishing valuation comparables for M&A, assessing typical effective tax rates by sector, or producing financial due diligence context from the most authoritative source of actual US business financial performance. Returns IRS SOI aggregate statistics from actual filed corporation income tax returns — gross receipts, net income margins, and effective tax rates by industry. Data reflects actual filed returns, not survey estimates. Example: Healthcare and Social Assistance — 284,000 returns, 8.3% net income margin, 19.1% effective tax rate, $4.2M average gross receipts per return — baseline for healthcare PE valuation and acquisition multiples analysis. Source: IRS Statistics of Income Division.

Input parameters:

- `entity_type` (string)
- `industry` (string, required): Industry name or NAICS sector (e.g. healthcare, construction, professional services, manufacturing)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-stratalize-intelligence/api-mcp-public#diagnostics

## Score history

- 2026-08-03: 70
- 2026-08-02: 69
- 2026-08-01: 69
- 2026-07-31: 68
- 2026-07-30: 63
- 2026-07-29: 62
- 2026-07-28: 61
- 2026-07-27: 61
- 2026-07-26: 60

## Links

- Remote endpoint: https://www.stratalize.com/api/mcp-public?vertical=intelligence
- Repository: https://github.com/Stratalize/Stratalize
- Website: https://www.stratalize.com/
- Changelog RSS feed: https://verifymcp.io/servers/com-stratalize-intelligence/api-mcp-public/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-stratalize-intelligence/api-mcp-public/changelog.json
- HTML version of this page: https://verifymcp.io/servers/com-stratalize-intelligence/api-mcp-public