Technology Convergence Disruption MCP Server

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Quick Start

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

{
  "mcpServers": {
    "technology-convergence-disruption-mcp": {
      "url": "https://ryanclinton--technology-convergence-disruption-mcp.apify.actor/mcp"
    }
  }
}

---

Technology convergence disruption intelligence — quantified across 14 live data sources and eight statistical models — now available as a Model Context Protocol server your AI agent calls directly. Connect Claude Desktop, Cursor, or any MCP-compatible client to detect cross-domain patent convergence 3-5 years before it reaches mainstream awareness, trace academic-to-commercial knowledge cascades, and score industries against the Christensen disruption framework from a single endpoint.

This server orchestrates parallel calls to USPTO and EPO patent databases, OpenAlex, Semantic Scholar, arXiv, Crossref, GitHub, Stack Overflow, Hacker News, NIH grants, job market data, EUIPO trademarks, ORCID researcher profiles, and company deep research. Each tool call gathers the appropriate data and feeds results through purpose-built statistical algorithms: bipartite projection cosine similarity for patent convergence, branching process models for knowledge cascades, log-logistic diffusion curve fitting for adoption velocity, Fiedler spectral clustering for skill transitions, and ARDL time-series regression for funding leading indicators.

What data can you access?

| Data Point | Source | Example |
|---|---|---|
| 📄 Patent filings, IPC/CPC codes, applicants, citations | USPTO Patents | US11,234,567 — G06N 20/00 (AI/ML), filed 2023 |
| 🌍 European patent publications and classifications | EPO Patents | EP3891234 — H04L 9/32 (Cryptography), 8 IPC codes |
| 📚 Academic papers, citation counts, topics, concepts | OpenAlex | "Attention Is All You Need" — 86,000 citations |
| 🔬 Research papers with semantic field classification | Semantic Scholar | "AlphaFold2" — paperId, 12,800 citations, 42 topics |
| 📐 Preprints in physics, CS, math, and biology | arXiv | arXiv:2303.08774 — cs.CL, 11,000 daily readers |
| 🔗 DOI metadata, journal citations, funding links | Crossref | 10.1038/s41586-021-03819-2, cited-by: 4,200 |
| 💻 Open-source repositories, stars, creation dates | GitHub | pytorch/pytorch — 82,000 stars, created 2016-08 |
| ❓ Developer Q&A volume and question trends | Stack Overflow | "kubernetes" — 94,000 questions since 2014 |
| 💬 Tech community discussion signals, hiring trends | Hacker News | "Ask HN: Who is Hiring? — LLM infra engineers" |
| 💼 Job postings, required skills, posting dates | Job Market Intel | "ML Platform Engineer — PyTorch, Ray, Kubernetes" |
| 🏢 Company profiles, revenue estimates, funding rounds | Company Deep Research | Databricks — $1.6B funding, $43B valuation |
| 🧬 NIH research grants, fiscal years, award amounts | NIH Research Grants | R01 GM123456 — $450K, FY2024, genomics |
| ™ EU trademark registrations and brand activity | EUIPO Trademarks | "NeuralCore" — Class 42 (Software services) |
| 🧑‍🔬 Researcher profiles, affiliations, publications | ORCID | 0000-0002-1234-5678 — Stanford AI Lab, 87 papers |

Why use the Technology Convergence Disruption MCP Server?

Strategy teams, venture capital analysts, and R&D directors typically spend weeks manually triangulating patent filings, academic citation trends, developer ecosystem signals, and labor market shifts to identify where disruption is forming. The data lives in a dozen separate databases — each requiring its own API account, rate-limit handling, and custom parsing logic. Cross-domain pattern recognition, the part that actually signals disruption 3-5 years early, demands statistical methods most teams lack time to implement.

This server automates the full pipeline. A single tool call gathers data from up to 14 sources in parallel, runs the appropriate statistical model, and returns structured output your AI agent can reason over immediately.

- Scheduling — run weekly patent convergence scans or monthly landscape reports to track technology trajectories over time
- API access — trigger any of the eight tools from Python, JavaScript, or any MCP-compatible HTTP client
- Parallel data collection — up to 14 actor calls run concurrently per request using Promise.all, cutting latency vs. sequential queries
- Monitoring — configure Apify alerts when runs fail or return unexpected output for production pipeline reliability
- Integrations — connect to Claude Desktop, Cursor, LangChain, LlamaIndex, Zapier, or any webhook-capable system

Features

- Bipartite patent convergence analysis — builds a bipartite graph B(patents, IPC codes), projects onto IPC subclass space, and computes temporal cosine similarity between adjacent 3-year windows. Returns up to 50 ranked convergence pairs with delta similarity, convergence acceleration, and estimated lead-time years.
- 8 IPC domain sections mapped — filings normalized across sections A through H (Human Necessities, Chemistry, Physics/Computing, Electricity/Electronics, etc.) for human-readable domain labels in all output.
- Branching process cascade model — computes branching ratio r = average patent citations per academic paper per topic. Supercritical (r > 1) signals explosive commercial adoption; subcritical (r < 1) indicates dying research lines. Cascade depth computed via log2 citation chain depth.
- Log-logistic diffusion curve fitting — fits F(t) = 1 / (1 + (t/alpha)^(-beta)) to GitHub star and Stack Overflow question time series using linearized OLS regression. Returns alpha (median adoption time), beta (steepness), velocity dF/dt, and acceleration d²F/dt².
- Fiedler spectral skill clustering — constructs a skill co-occurrence adjacency matrix from job postings (capped at 100 skills for tractable computation), computes graph Laplacian L = D - A, and extracts the Fiedler vector via deflated power iteration. Cluster sign partition identifies emerging vs. declining skill groups. Predicts convergence timing from cluster merger rate.
- Christensen disruption scoring — operationalizes the disruption framework as: score = convergence_velocity market_size / (incumbent_response + patent_moat + talent_pool). Five Christensen factors decomposed per technology: new market creation, low-end entry, sustaining innovation gap, talent migration, and technology overshoot.
- ARDL funding leading indicator — fits an Autoregressive Distributed Lag model Y_t = alpha + sum(beta_i Y_{t-i}) + sum(gamma_j X_{t-j}) to NIH grant and patent time series. Computes long-run multiplier = sum(gamma) / (1 - sum(beta)) and error correction speed phi. Requires minimum 5 yearly observations.
- Technology landscape profiler — aggregates 7 sources in one parallel call (patents, OpenAlex, arXiv, GitHub, jobs, NIH grants, Hacker News) and classifies each technology by maturity stage: emerging, growing, mature, or declining based on patent count thresholds and year-over-year trend.
- Full disruption brief synthesis — runs all six analytical models, synthesizes results into sections with per-section confidence scores (0-1), data point counts, an executive summary, a time horizon estimate, and ranked strategic recommendations. Standard depth: 50 results per source; deep: 100+ per source.
- Spend limit enforcement — every tool call checks the per-event charge limit before execution. Runs stop cleanly when the budget ceiling is reached, never silently over-spending.
- Parallel actor orchestration — runActorsParallel dispatches all data source calls via Promise.all and returns results in index-aligned arrays for deterministic downstream assembly.

1. Get your Apify API token — go to Apify Console > Settings > Integrations and copy your token.
2. Add the server to your MCP client — paste the configuration below for Claude Desktop, Cursor, or your preferred client.
3. Run your first tool call — ask your AI assistant: "Detect technology convergence in quantum computing with 3-year temporal windows."* Results return within seconds.
4. Explore the full suite — progress from convergence detection to knowledge cascade to disruption brief for increasing analytical depth.

MCP tools

| Tool | Input | Event price |
|------|-------|-------------|
| detect_technology_convergence | technology, windowYears, maxPatents | $0.08 |
| trace_knowledge_cascade | topic, maxPapers, maxPatents | $0.07 |
| measure_adoption_velocity | technology, relatedTerms | $0.06 |
| map_skill_transitions | industry, location, maxPostings | $0.07 |
| score_disruption_risk | technologies[], targetIndustry | $0.09 |
| predict_from_research_funding | researchAreas[], maxLag | $0.08 |
| profile_technology_landscape | domain, maxPerSource | $0.10 |
| generate_disruption_brief | technology, industry, depth | $0.12 |

Tool reference

detect_technology_convergence — Queries USPTO and EPO in parallel (up to 200 patents each), normalizes IPC codes to subclass level (4-character), builds per-window co-occurrence matrices via bipartite projection, and computes temporal cosine similarity between adjacent windows. Returns up to 50 convergence pairs ranked by delta similarity, top-10 converging domains, and average convergence rate.

trace_knowledge_cascade — Queries OpenAlex, Semantic Scholar, arXiv, Crossref, and USPTO in parallel. Matches paper titles against patent citation reference strings to compute per-topic branching ratios. Returns up to 30 topics ranked by inflection proximity score (cascade depth × log2(breadth + 1)), with regime classification: supercritical (r > 1), critical (0.8 ≤ r ≤ 1), or subcritical (r < 0.8).

measure_adoption_velocity — Queries GitHub (sorted by stars) and Stack Overflow for each search term. Aggregates Stack Overflow questions by calendar month. Fits a log-logistic diffusion curve via linearized OLS on log-transformed time series. Returns per-technology alpha, beta, current penetration [0,1], velocity dF/dt, acceleration d²F/dt², and phase classification.

map_skill_transitions — Queries job market data and supplements with Hacker News hiring discussions. Extracts technology terms from HN text via pattern matching. Caps the adjacency matrix at 100 skills for tractable Fiedler vector computation via deflated power iteration. Returns two spectral clusters with emerging and declining skill sub-lists, Fiedler gap eigenvalue, and convergence timing estimate in years.

score_disruption_risk — Queries USPTO, job market intel, and company deep research for each input technology. Derives convergence velocity from IPC section diversity (count of unique sections A-H, normalized to [0,1] over 8 sections). Computes HHI citation concentration from patent applicant distribution. Applies Christensen disruption formula and returns per-technology factor decomposition with composite score 0-100 and risk level.

predict_from_research_funding — Queries NIH grants, USPTO patents, and OpenAlex papers per research area. Aligns annual grant amounts and patent counts into time series from 2000 onward. Solves ARDL OLS coefficients via Gauss elimination with up to maxLag years. Requires at least 5 annual observations for model estimation. Returns long-run multiplier, error correction speed phi, and leading indicator ranking.

profile_technology_landscape — Runs 7 actor calls in a single parallel batch (patents, OpenAlex, arXiv, GitHub, job market, NIH grants, Hacker News) and classifies each sub-technology by maturity stage. Maturity thresholds: emerging < 10 patents; growing 10-50; mature 50-200; declining = year-over-year decline in filings. Includes cross-source composite scores.

generate_disruption_brief — Runs all 6 analytical models plus the landscape profiler in sequence. Gathers data from up to 14 sources concurrently. Synthesizes results into an executive brief with sections covering convergence, cascade, adoption, skills, disruption risk, and funding indicators — each with a confidence score (0-1) and data point count. Standard depth: 75 results per source; deep: 150 per source.

Input parameters

| Parameter | Type | Used by | Default | Description |
|-----------|------|---------|---------|-------------|
| technology | string | tools 1, 3, 8 | required | Technology domain or keyword (e.g., "quantum computing", "CRISPR") |
| topic | string | tool 2 | required | Research topic to trace from academia to patents |
| domain | string | tool 7 | required | Technology domain for landscape profiling |
| technologies | string[] | tool 5 | required | List of technologies to score for disruption risk |
| researchAreas | string[] | tool 6 | required | Research areas for ARDL funding analysis |
| industry | string | tools 4, 5, 8 | "" | Target industry for skill/disruption context |
| windowYears | number | tool 1 | 3 | Temporal analysis window size in years |
| maxPatents | number | tools 1, 2 | 200 | Maximum patents per source |
| maxPapers | number | tool 2 | 100 | Maximum academic papers to analyze |
| relatedTerms | string[] | tool 3 | [] | Additional search terms for adoption velocity |
| location | string | tool 4 | "" | Geographic focus for skill analysis (empty = global) |
| maxPostings | number | tool 4 | 200 | Maximum job postings to analyze |
| targetIndustry | string | tool 5 | "" | Industry being potentially disrupted |
| maxLag | number | tool 6 | 3 | Maximum lag years for ARDL model |
| maxPerSource | number | tool 7 | 50 | Maximum results per data source for landscape |
| depth | enum | tool 8 | "standard" | Analysis depth: "standard" or "deep" |

Input tips

- Start with detect_technology_convergence — it is the fastest tool and gives immediate signal on whether a technology is converging with adjacent domains before running the more compute-intensive models.
- Use windowYears: 5 for slow-moving fields — biotech and materials science patent cycles are longer than software. A 3-year window may miss meaningful signals; 5 years captures more co-occurrence history per window.
- Batch multiple technologies in score_disruption_risk — passing 5 technologies in one call is more efficient than 5 separate calls, since the disruption score normalization compares across the full technology set.
- Set maxLag: 2 for short ARDL datasets — if a research area has fewer than 10 years of NIH grant data, reduce maxLag to 2 to avoid rank deficiency in the OLS design matrix.
- Use depth: "deep" for generate_disruption_brief when completeness matters — standard depth completes in roughly 3-4 minutes; deep uses 150+ results per source and takes 6-8 minutes but produces higher-confidence cascade and convergence estimates.

Output example

detect_technology_convergence — quantum computing:

{

  "technology": "quantum computing",

  "pairs": [

    {

      "domainA": "G06N (Physics/Computing)",

      "domainB": "H01L (Electricity/Electronics)",

      "cosineSimilarity": 0.847,

      "deltaSimilarity": 0.0312,

      "convergenceAcceleration": 0.0089,

      "sharedPatentCount": 142,

      "leadTimeYears": 4

    },

    {

      "domainA": "G06N (Physics/Computing)",

      "domainB": "B82Y (Operations/Transport)",

      "cosineSimilarity": 0.721,

      "deltaSimilarity": 0.0241,

      "convergenceAcceleration": 0.0061,

      "sharedPatentCount": 87,

      "leadTimeYears": 6

    }

  ],

  "topConvergingDomains": [

    "G06N (Physics/Computing)",

    "H01L (Electricity/Electronics)",

    "B82Y (Operations/Transport)",

    "G06F (Physics/Computing)",

    "H04L (Electricity/Electronics)"

  ],

  "avgConvergenceRate": 0.0187,

  "bipartiteProjectionSize": 34,

  "temporalWindows": 7,

  "sourceCounts": { "uspto": 198, "epo": 176 }

}

generate_disruption_brief — large language models (excerpt):

{

  "technology": "large language models",

  "industry": "enterprise software",

  "executiveSummary": "LLMs show critical disruption risk (score 78/100) with supercritical academic cascade (avg branching ratio 1.34). Patent convergence accelerating between G06N and G06F. Skill cluster merger estimated 2.1 years. NIH/NSF funding long-run multiplier 3.2 — every $1M research grant historically yields $3.2M in downstream patent activity.",

  "timeHorizon": "18-30 months",

  "sections": [

    {

      "name": "Patent Convergence",

      "confidence": 0.82,

      "dataPoints": 312,

      "summary": "G06N/G06F cosine similarity delta +0.0312 per window, accelerating",

      "findings": ["IPC diversity: 7/8 sections active", "Lead time: 4 years on top pair"]

    },

    {

      "name": "Knowledge Cascade",

      "confidence": 0.74,

      "dataPoints": 840,

      "summary": "Supercritical branching ratio 1.34 across 22 topics",

      "findings": ["22 of 31 topics supercritical", "Max inflection proximity: 18.4"]

    }

  ],

  "recommendations": [

    "Accelerate R&D partnerships in G06N/G06F convergence zones",

    "Prioritize skill acquisition in emerging cluster: MLOps, RLHF, inference optimization",

    "Monitor ARDL long-run multiplier quarterly — currently 3.2x (high commercial translation)"

  ]

}

Output fields

detect_technology_convergence

| Field | Type | Description |
|-------|------|-------------|
| pairs[].domainA | string | First IPC domain in converging pair, with section label |
| pairs[].domainB | string | Second IPC domain in converging pair |
| pairs[].cosineSimilarity | number | Latest window cosine similarity between IPC co-occurrence vectors |
| pairs[].deltaSimilarity | number | Average change in similarity per window (positive = converging) |
| pairs[].convergenceAcceleration | number | Rate of change of delta similarity (is convergence itself speeding up?) |
| pairs[].sharedPatentCount | number | Total patents spanning both IPC domains |
| pairs[].leadTimeYears | number | Estimated years before convergence becomes mainstream |
| topConvergingDomains | string[] | Top 10 IPC domains by total convergence score |
| avgConvergenceRate | number | Mean delta similarity across all ranked pairs |
| bipartiteProjectionSize | number | Number of unique IPC subclasses in bipartite projection |
| temporalWindows | number | Number of time windows analyzed |
| sourceCounts.uspto | number | Patents retrieved from USPTO |
| sourceCounts.epo | number | Patents retrieved from EPO |

trace_knowledge_cascade

| Field | Type | Description |
|-------|------|-------------|
| chains[].topic | string | Research topic name |
| chains[].paperCount | number | Number of papers in this topic |
| chains[].patentCitationCount | number | Total patent citations to papers in this topic |
| chains[].branchingRatio | number | Average patent citations per paper (r > 1 = supercritical) |
| chains[].regime | string | "supercritical", "critical", or "subcritical" |
| chains[].cascadeDepth | number | log2(avg citations + 1) — depth of citation chain |
| chains[].cascadeBreadth | number | Number of distinct papers cited by patents |
| chains[].inflectionProximity | number | Composite: depth × log2(breadth + 1) |
| supercriticalCount | number | Topics with branching ratio > 1.0 |
| avgBranchingRatio | number | Mean branching ratio across all topics |
| maxInflectionProximity | number | Highest inflection proximity score |
| totalPapersAnalyzed | number | Total academic papers processed |
| totalPatentsCited | number | Total patent-to-paper citation links found |

measure_adoption_velocity

| Field | Type | Description |
|-------|------|-------------|
| curves[].technology | string | Technology name |
| curves[].alpha | number | Median adoption time parameter (log-logistic alpha) |
| curves[].beta | number | Steepness parameter (higher = sharper S-curve inflection) |
| curves[].currentPenetration | number | Estimated current position on adoption curve [0, 1] |
| curves[].currentVelocity | number | First derivative dF/dt at current time |
| curves[].acceleration | number | Second derivative d²F/dt² (positive = still in acceleration phase) |
| curves[].phase | string | "early" (<10%), "growth" (10-50%), "maturity" (50-90%), or "saturation" (>90%) |
| curves[].githubStars | number | Latest GitHub star count across matched repos |
| curves[].stackOverflowQuestions | number | Latest Stack Overflow question volume |
| fastestGrowing | string | Technology with highest current velocity |
| avgVelocity | number | Mean adoption velocity across all technologies |
| acceleratingCount | number | Technologies with positive acceleration |

map_skill_transitions

| Field | Type | Description |
|-------|------|-------------|
| clusters[].clusterId | number | Cluster index (0 or 1 for Fiedler bipartition) |
| clusters[].skills | string[] | Skills in this spectral cluster |
| clusters[].label | string | "emerging" or "declining" based on temporal growth analysis |
| clusters[].growthRate | number | Ratio of late-period to early-period posting frequency |
| transitions[].from | string | Skill being replaced or de-emphasized |
| transitions[].to | string | Skill replacing or growing relative to source |
| transitions[].edgeWeight | number | Co-occurrence strength in adjacency matrix |
| fiedlerGap | number | Second eigenvalue of graph Laplacian (larger = cleaner cluster separation) |
| clusterMergerRate | number | Rate at which cluster boundary is narrowing |
| convergenceTimingYears | number | Estimated years until skill clusters fully merge |
| totalSkillsAnalyzed | number | Unique skills found across all job postings |

score_disruption_risk

| Field | Type | Description |
|-------|------|-------------|
| assessments[].technology | string | Technology being assessed |
| assessments[].convergenceVelocity | number | Normalized IPC diversity score [0, 1] |
| assessments[].marketSize | number | Normalized market size proxy from company data |
| assessments[].incumbentResponse | number | Normalized incumbent patent filing rate |
| assessments[].patentMoat | number | Normalized HHI citation concentration |
| assessments[].talentPool | number | Normalized job posting volume |
| assessments[].disruptionScore | number | Composite score 0-100 |
| assessments[].riskLevel | string | "low", "moderate", "high", or "critical" (≥75) |
| assessments[].christensenFactors | object | Scores for: new_market_creation, low_end_entry, sustaining_innovation_gap, talent_migration, technology_overshoot |
| highestRisk | string | Technology with highest disruption score |
| avgDisruptionScore | number | Mean disruption score across all technologies |
| criticalCount | number | Technologies rated "critical" (score ≥ 75) |

predict_from_research_funding

| Field | Type | Description |
|-------|------|-------------|
| indicators[].researchArea | string | Research area name |
| indicators[].longRunMultiplier | number | sum(gamma) / (1 - sum(beta)) — commercial translation per grant dollar |
| indicators[].errorCorrectionSpeed | number | phi = -(1 - sum(beta)) — speed of mean reversion |
| indicators[].lagYears | number | Peak lag years between grant funding and commercial output |
| indicators[].isLeadingIndicator | boolean | True if commercial activity Granger-caused by grants |
| leadingIndicators | string[] | Areas where grants are confirmed leading indicators |
| avgLongRunMultiplier | number | Mean multiplier across all areas |

generate_disruption_brief

| Field | Type | Description |
|-------|------|-------------|
| executiveSummary | string | 2-3 sentence synthesis of highest-signal findings |
| timeHorizon | string | Estimated disruption window (e.g., "18-30 months") |
| sections[].name | string | Section name (convergence, cascade, adoption, skills, risk, funding) |
| sections[].confidence | number | Model confidence score [0, 1] based on data volume |
| sections[].dataPoints | number | Total data points used for this section |
| sections[].summary | string | One-line finding for this analytical dimension |
| sections[].findings | string[] | Bulleted supporting findings |
| recommendations | string[] | Ranked strategic recommendations |
| sourceCounts | object | Per-source data point counts used across all models |

How much does it cost to run technology convergence analysis?

Each tool call charges a flat per-event fee. Platform compute costs are included. There are no subscription fees — you pay only for the analysis you run.

| Scenario | Tool | Cost |
|----------|------|------|
| Single convergence scan | detect_technology_convergence | $0.08 |
| Knowledge cascade trace | trace_knowledge_cascade | $0.07 |
| Adoption velocity measurement | measure_adoption_velocity | $0.06 |
| Skill transition mapping | map_skill_transitions | $0.07 |
| Disruption risk score (per call) | score_disruption_risk | $0.09 |
| Funding leading indicator | predict_from_research_funding | $0.08 |
| Full landscape profile | profile_technology_landscape | $0.10 |
| Complete disruption brief | generate_disruption_brief | $0.12 |

A complete analysis workflow — convergence scan + cascade trace + disruption brief — costs $0.27. Running a disruption brief daily on 5 technologies costs roughly $18/month. Compare this to analyst research tools at $500-2,000/month or bespoke consulting engagements at $10,000+.

You can set a maximum spending limit per run in the Apify console to control costs. The server stops cleanly when your budget ceiling is reached.

Connect via the API

Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")